From 8f2aff6ecf243c03526e746b6a7b4c23719b956d Mon Sep 17 00:00:00 2001
From: JustHeroo <JustHeroo@users.noreply.github.com>
Date: Tue, 10 Dec 2024 21:01:53 +0000
Subject: [PATCH] deploy: 91d43c7d8b84850b120aa84346a7d78993d4ea07

---
 docs/en/benchmark.html |   28 +-
 feed.xml               | 2689 +++++++++++++++++++---------------------
 sitemap.xml            | 2332 +++++++++++++++++-----------------
 3 files changed, 2453 insertions(+), 2596 deletions(-)

diff --git a/docs/en/benchmark.html b/docs/en/benchmark.html
index f9da6693e9..872a1c2cbb 100644
--- a/docs/en/benchmark.html
+++ b/docs/en/benchmark.html
@@ -398,7 +398,7 @@
 <path d="M20.2658 26.412H8.38867V26.5781H20.2658V26.412Z" fill="#9EDEFA"/>
 </svg>
 -->
-</a><div class="sidebar-toc"><ul class="toc toc--navigator"><li class="toc-h1">All Enterprise libraries</li><li class="toc-h2"><a href="/docs/en/license_getting_started">Getting Started</a></li><li class="toc-h1">Healthcare NLP</li><li class="toc-h2"><a href="/docs/en/licensed_install">Installation</a></li><li class="toc-h2"><a href="/docs/en/licensed_annotators">Annotators</a></li><li class="toc-h2"><a href="/docs/en/licensed_training">Training</a></li><li class="toc-h2"><a href="/docs/en/evaluation">Evaluation</a></li><li class="toc-h2"><a href="/docs/en/healthcare_risk_adjustments_score_calculation">Risk Adjustments Score Calculation</a></li><li class="toc-h2"><a href="/docs/en/utility_helper_modules">Utility & Helper Modules</a></li><li class="toc-h2"><a href="/docs/en/licensed_serving_spark_nlp_via_api_synapseml">Serving Spark NLP&#58 SynapseML</a></li><li class="toc-h2"><a href="/docs/en/licensed_serving_spark_nlp_via_api_fastapi">Serving Spark NLP&#58 FastAPI</a></li><li class="toc-h2"><a href="/licensed/api/">Scala API (Scaladoc)</a></li><li class="toc-h2"><a href="/licensed/api/python">Python API (Sphinx)</a></li><li class="toc-h2"><a href="/docs/en/wiki">Wiki</a></li><li class="toc-h2 active"><a href="/docs/en/benchmark">Benchmarks</a></li><li class="toc-h2"><a href="/docs/en/best_practices_pretrained_models">Best Practices Using Pretrained Models Together</a></li><li class="toc-h2"><a href="/docs/en/spark_nlp_healthcare_versions/licensed_release_notes">Release Notes</a></li><li class="toc-h2"><a href="/models?edition=Spark+NLP+for+Healthcare">Healthcare Models</a></li><li class="toc-h2"><a href="/docs/en/licensed_version_compatibility">Version Compatibility</a></li><li class="toc-h1">Visual NLP</li><li class="toc-h2"><a href="/docs/en/ocr">Getting Started</a></li><li class="toc-h2"><a href="/docs/en/ocr_install">Installation</a></li><li class="toc-h2"><a href="/docs/en/version_compatibility">Version Compatibility</a></li><li class="toc-h2"><a href="/docs/en/ocr_pipeline_components">Pipeline components</a></li><li class="toc-h2"><a href="/docs/en/ocr_table_recognition">Table recognition</a></li><li class="toc-h2"><a href="/docs/en/ocr_visual_document_understanding">Visual document understanding</a></li><li class="toc-h2"><a href="/docs/en/ocr_object_detection">Object detection</a></li><li class="toc-h2"><a href="/docs/en/ocr_structures">Structures and helpers</a></li><li class="toc-h2"><a href="/docs/en/ocr_benchmark">Speed benchmarks</a></li><li class="toc-h2"><a href="/docs/en/spark_ocr_versions/ocr_release_notes">Release notes</a></li><li class="toc-h1">Finance NLP</li><li class="toc-h2"><a href="/docs/en/financial_release_notes">Release Notes</a></li><li class="toc-h2"><a href="/models?edition=Spark+NLP+for+Finance">Financial Models</a></li><li class="toc-h2"><a href="/docs/en/financial_version_compatibility">Version Compatibility</a></li><li class="toc-h1">Legal NLP</li><li class="toc-h2"><a href="/docs/en/legal_release_notes">Release Notes</a></li><li class="toc-h2"><a href="/models?edition=Spark+NLP+for+Legal">Legal Models</a></li><li class="toc-h2"><a href="/docs/en/legal_version_compatibility">Version Compatibility</a></li></ul></div></div><div class="page__main js-page-main has-aside cell cell--auto">
+</a><div class="sidebar-toc"><ul class="toc toc--navigator"><li class="toc-h1">All Enterprise libraries</li><li class="toc-h2"><a href="/docs/en/license_getting_started">Getting Started</a></li><li class="toc-h1">Healthcare NLP</li><li class="toc-h2"><a href="/docs/en/licensed_install">Installation</a></li><li class="toc-h2"><a href="/docs/en/licensed_annotators">Annotators</a></li><li class="toc-h2"><a href="/docs/en/licensed_training">Training</a></li><li class="toc-h2"><a href="/docs/en/evaluation">Evaluation</a></li><li class="toc-h2"><a href="/docs/en/healthcare_risk_adjustments_score_calculation">Risk Adjustments Score Calculation</a></li><li class="toc-h2"><a href="/docs/en/utility_helper_modules">Utility & Helper Modules</a></li><li class="toc-h2"><a href="/docs/en/licensed_serving_spark_nlp_via_api_synapseml">Serving Spark NLP&#58 SynapseML</a></li><li class="toc-h2"><a href="/docs/en/licensed_serving_spark_nlp_via_api_fastapi">Serving Spark NLP&#58 FastAPI</a></li><li class="toc-h2"><a href="/licensed/api/">Scala API (Scaladoc)</a></li><li class="toc-h2"><a href="/licensed/api/python">Python API (Sphinx)</a></li><li class="toc-h2"><a href="/docs/en/wiki">Wiki</a></li><li class="toc-h2 active"><a href="/docs/en/benchmark">Benchmarks</a></li><li class="toc-h2"><a href="/docs/en/benchmark-llm">Benchmarks LLM</a></li><li class="toc-h2"><a href="/docs/en/best_practices_pretrained_models">Best Practices Using Pretrained Models Together</a></li><li class="toc-h2"><a href="/docs/en/spark_nlp_healthcare_versions/licensed_release_notes">Release Notes</a></li><li class="toc-h2"><a href="/models?edition=Spark+NLP+for+Healthcare">Healthcare Models</a></li><li class="toc-h2"><a href="/docs/en/licensed_version_compatibility">Version Compatibility</a></li><li class="toc-h1">Visual NLP</li><li class="toc-h2"><a href="/docs/en/ocr">Getting Started</a></li><li class="toc-h2"><a href="/docs/en/ocr_install">Installation</a></li><li class="toc-h2"><a href="/docs/en/version_compatibility">Version Compatibility</a></li><li class="toc-h2"><a href="/docs/en/ocr_pipeline_components">Pipeline components</a></li><li class="toc-h2"><a href="/docs/en/ocr_table_recognition">Table recognition</a></li><li class="toc-h2"><a href="/docs/en/ocr_visual_document_understanding">Visual document understanding</a></li><li class="toc-h2"><a href="/docs/en/ocr_object_detection">Object detection</a></li><li class="toc-h2"><a href="/docs/en/ocr_structures">Structures and helpers</a></li><li class="toc-h2"><a href="/docs/en/ocr_benchmark">Speed benchmarks</a></li><li class="toc-h2"><a href="/docs/en/spark_ocr_versions/ocr_release_notes">Release notes</a></li><li class="toc-h1">Finance NLP</li><li class="toc-h2"><a href="/docs/en/financial_release_notes">Release Notes</a></li><li class="toc-h2"><a href="/models?edition=Spark+NLP+for+Finance">Financial Models</a></li><li class="toc-h2"><a href="/docs/en/financial_version_compatibility">Version Compatibility</a></li><li class="toc-h1">Legal NLP</li><li class="toc-h2"><a href="/docs/en/legal_release_notes">Release Notes</a></li><li class="toc-h2"><a href="/models?edition=Spark+NLP+for+Legal">Legal Models</a></li><li class="toc-h2"><a href="/docs/en/legal_version_compatibility">Version Compatibility</a></li></ul></div></div><div class="page__main js-page-main has-aside cell cell--auto">
 
       <div class="page__main-inner"><div class="page__header d-print-none"><!-- Google Tag Manager (noscript) -->
 <noscript><iframe src="https://www.googletagmanager.com/ns.html?id=GTM-59JLR64"
@@ -2271,10 +2271,26 @@ <h3 id="deidentification-pipelines-speed-comparison">Deidentification Pipelines
               </table>
             </li>
             <li>AWS EC2 instance Config:
-      - 32 CPU cores, 64GiB RAM (c6a.8xlarge $1.224/h)
-              <p>|data_count |partition |   AWS   |
-  |———-:|———:|——–:|
-  |    100000 |      512 |1h 9m 56s|</p>
+              <ul>
+                <li>32 CPU cores, 64GiB RAM (c6a.8xlarge $1.224/h)</li>
+              </ul>
+
+              <table class="table-model-big">
+                <thead>
+                  <tr>
+                    <th style="text-align: right">data_count</th>
+                    <th style="text-align: right">partition</th>
+                    <th style="text-align: right">AWS</th>
+                  </tr>
+                </thead>
+                <tbody>
+                  <tr>
+                    <td style="text-align: right">100000</td>
+                    <td style="text-align: right">512</td>
+                    <td style="text-align: right">1h 9m 56s</td>
+                  </tr>
+                </tbody>
+              </table>
             </li>
           </ul>
         </li>
@@ -3537,7 +3553,7 @@ <h3 id="additional-comments">Additional Comments</h3>
 
 }
 
-</style><div class="article__section-navigator clearfix"><div class="previous nav_link"><span>PREVIOUS</span><a href="/docs/en/wiki">Wiki</a></div><div class="next nav_link"><span>NEXT</span><a href="/docs/en/best_practices_pretrained_models">Best Practices Using Pretrained Models Together</a></div></div></div>
+</style><div class="article__section-navigator clearfix"><div class="previous nav_link"><span>PREVIOUS</span><a href="/docs/en/wiki">Wiki</a></div><div class="next nav_link"><span>NEXT</span><a href="/docs/en/benchmark-llm">Benchmarks LLM</a></div></div></div>
 
 </div>
 </div>
diff --git a/feed.xml b/feed.xml
index 504397d0c4..09820d6f8c 100644
--- a/feed.xml
+++ b/feed.xml
@@ -1,159 +1,167 @@
-<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="3.9.2">Jekyll</generator><link href="/feed.xml" rel="self" type="application/atom+xml" /><link href="/" rel="alternate" type="text/html" /><updated>2024-12-10T16:46:17+00:00</updated><id>/feed.xml</id><title type="html">Spark NLP</title><subtitle>High Performance NLP with Apache Spark
-</subtitle><author><name>{&quot;type&quot;=&gt;nil, &quot;name&quot;=&gt;nil, &quot;url&quot;=&gt;nil, &quot;avatar&quot;=&gt;nil, &quot;bio&quot;=&gt;nil, &quot;email&quot;=&gt;nil, &quot;facebook&quot;=&gt;nil, &quot;twitter&quot;=&gt;nil, &quot;weibo&quot;=&gt;nil, &quot;googleplus&quot;=&gt;nil, &quot;telegram&quot;=&gt;nil, &quot;medium&quot;=&gt;nil, &quot;zhihu&quot;=&gt;nil, &quot;douban&quot;=&gt;nil, &quot;linkedin&quot;=&gt;nil, &quot;github&quot;=&gt;nil, &quot;npm&quot;=&gt;nil}</name></author><entry><title type="html">Pretrained Zero-Shot Named Entity Recognition (zeroshot_ner_vop_large)</title><link href="/2024/11/30/zeroshot_ner_vop_large_en.html" rel="alternate" type="text/html" title="Pretrained Zero-Shot Named Entity Recognition (zeroshot_ner_vop_large)" /><published>2024-11-30T00:00:00+00:00</published><updated>2024-11-30T00:00:00+00:00</updated><id>/2024/11/30/zeroshot_ner_vop_large_en</id><content type="html" xml:base="/2024/11/30/zeroshot_ner_vop_large_en.html">&lt;h2 id=&quot;description&quot;&gt;Description&lt;/h2&gt;
-
-&lt;p&gt;Zero-shot Named Entity Recognition (NER) enables the identification of entities in text with minimal effort. By leveraging pre-trained language models and contextual understanding, zero-shot NER extends entity recognition capabilities to new domains and languages.
-While the model card includes default labels as examples, it is important to highlight that users are not limited to these labels. The model is designed to support any set of entity labels, allowing users to adapt it to their specific use cases. For best results, it is recommended to use labels that are conceptually similar to the provided defaults.&lt;/p&gt;
-
-&lt;h2 id=&quot;predicted-entities&quot;&gt;Predicted Entities&lt;/h2&gt;
-
-&lt;p&gt;&lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;AdmissionDischarge&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;Age&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;Allergen&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;BodyPart&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ClinicalDept&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DateTime&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;Disease&lt;/code&gt;,&lt;br /&gt;
-&lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;Dosage&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;Drug&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;Duration&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;Employment&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;Form&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;Frequency&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;Gender&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;Laterality&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;MedicalDevice&lt;/code&gt;,&lt;br /&gt;
-&lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;Modifier&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;Procedure&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PsychologicalCondition&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;RaceEthnicity&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;Substance&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;Symptom&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;Test&lt;/code&gt;,&lt;br /&gt;
-&lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;Treatment&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;Vaccine&lt;/code&gt;&lt;/p&gt;
-
-&lt;p class=&quot;btn-box&quot;&gt;&lt;button class=&quot;button button-orange&quot; disabled=&quot;&quot;&gt;Live Demo&lt;/button&gt;
-&lt;button class=&quot;button button-orange&quot; disabled=&quot;&quot;&gt;Open in Colab&lt;/button&gt;
-&lt;a href=&quot;https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/zeroshot_ner_vop_large_en_5.5.1_3.0_1732980381062.zip&quot; class=&quot;button button-orange button-orange-trans arr button-icon hidden&quot;&gt;Download&lt;/a&gt;
-&lt;a href=&quot;s3://auxdata.johnsnowlabs.com/clinical/models/zeroshot_ner_vop_large_en_5.5.1_3.0_1732980381062.zip&quot; class=&quot;button button-orange button-orange-trans button-icon button-copy-s3&quot;&gt;Copy S3 URI&lt;/a&gt;&lt;/p&gt;
-
-&lt;h2 id=&quot;how-to-use&quot;&gt;How to use&lt;/h2&gt;
-
-&lt;div class=&quot;tabs-box&quot;&gt;
-  &lt;div class=&quot;tabs-model-aproach-head&quot;&gt;&lt;button class=&quot;tab-li-model-aproach tabheader_active&quot;&gt;Python&lt;strong&gt;Healthcare NLP&lt;/strong&gt;&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach tab-li-jsl&quot;&gt;Python&lt;strong&gt;JohnSnowLabs&lt;/strong&gt;&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach&quot;&gt;Scala&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach tab-li-nlu&quot;&gt;NLU&lt;/button&gt;&lt;/div&gt;
-
-  &lt;div class=&quot;language-python highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-&lt;span class=&quot;n&quot;&gt;document_assembler&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;DocumentAssembler&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;()&lt;/span&gt;\
-    &lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;setInputCol&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;text&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;\
-    &lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;setOutputCol&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;document&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;sentence_detector&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;SentenceDetector&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;()&lt;/span&gt;\
-    &lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;setInputCols&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;document&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;])&lt;/span&gt;\
-    &lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;setOutputCol&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;sentence&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;tokenizer&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;Tokenizer&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;()&lt;/span&gt;\
-    &lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;setInputCols&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;sentence&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;])&lt;/span&gt;\
-    &lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;setOutputCol&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;token&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;labels&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;
-    &lt;span class=&quot;s&quot;&gt;'AdmissionDischarge'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;'Age'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;'Allergen'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'BodyPart'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'ClinicalDept'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'DateTime'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'Disease'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;
-    &lt;span class=&quot;s&quot;&gt;'Dosage'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'Drug'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'Duration'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'Employment'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'Form'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'Frequency'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'Gender'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'Laterality'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'MedicalDevice'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;
-    &lt;span class=&quot;s&quot;&gt;'Modifier'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'Procedure'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'PsychologicalCondition'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'RaceEthnicity'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'Substance'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'Symptom'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'Test'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;
-    &lt;span class=&quot;s&quot;&gt;'Treatment'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'Vaccine'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;pretrained_zero_shot_ner&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;PretrainedZeroShotNER&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;().&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;pretrained&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;zeroshot_ner_vop_large&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;en&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;clinical/models&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;\
-    &lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;setInputCols&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;sentence&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;token&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;\
-    &lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;setOutputCol&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;ner&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;\
-    &lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;setPredictionThreshold&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;mf&quot;&gt;0.5&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;\
-    &lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;setLabels&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;labels&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;ner_converter&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;NerConverterInternal&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;()&lt;/span&gt;\
-    &lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;setInputCols&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;sentence&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;token&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;ner&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;\
-    &lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;setOutputCol&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;ner_chunk&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;pipeline&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;Pipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;().&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;setStages&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;
-    &lt;span class=&quot;n&quot;&gt;document_assembler&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;
-    &lt;span class=&quot;n&quot;&gt;sentence_detector&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;
-    &lt;span class=&quot;n&quot;&gt;tokenizer&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;
-    &lt;span class=&quot;n&quot;&gt;pretrained_zero_shot_ner&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;
-    &lt;span class=&quot;n&quot;&gt;ner_converter&lt;/span&gt;
-&lt;span class=&quot;p&quot;&gt;])&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;data&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;spark&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;createDataFrame&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([[&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;&quot;&quot;Hello,I'm 20 year old girl. I'm diagnosed with hyperthyroid 1 month ago. I was feeling weak, light headed,poor digestion, panic attacks, depression, left chest pain, increased heart rate, rapidly weight loss,  from 4 months. Because of this, I stayed in the hospital and just discharged from hospital. I had many other blood tests, brain mri, ultrasound scan, endoscopy because of some dumb doctors bcs they were not able to diagnose actual problem. Finally I got an appointment with a homeopathy doctor finally he find that i was suffering from hyperthyroid and my TSH was 0.15 T3 and T4 is normal . Also i have b12 deficiency and vitamin D deficiency so I'm taking weekly supplement of vitamin D and 1000 mcg b12 daily. I'm taking homeopathy medicine for 40 days and took 2nd test after 30 days. My TSH is 0.5 now. I feel a little bit relief from weakness and depression but I'm facing with 2 new problem from last week that is breathtaking problem and very rapid heartrate. I just want to know if i should start allopathy medicine or homeopathy is okay? Bcs i heard that thyroid take time to start recover. So please let me know if both of medicines take same time. Because some of my friends advising me to start allopathy and never take a chance as i can develop some serious problems.Sorry for my poor english😐Thank you.&quot;&quot;&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]).&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;toDF&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;text&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;pipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;fit&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;data&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;transform&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;data&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
-
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;  &lt;/div&gt;
-
-  &lt;div class=&quot;language-python jsl-block highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-&lt;span class=&quot;n&quot;&gt;document_assembler&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;nlp&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;DocumentAssembler&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;()&lt;/span&gt;\
-    &lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;setInputCol&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;text&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;\
-    &lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;setOutputCol&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;document&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;sentence_detector&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;nlp&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;SentenceDetector&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;()&lt;/span&gt;\
-    &lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;setInputCols&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;document&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;])&lt;/span&gt;\
-    &lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;setOutputCol&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;sentence&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;tokenizer&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;nlp&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;Tokenizer&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;()&lt;/span&gt;\
-    &lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;setInputCols&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;sentence&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;])&lt;/span&gt;\
-    &lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;setOutputCol&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;token&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;labels&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;
-    &lt;span class=&quot;s&quot;&gt;'AdmissionDischarge'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;'Age'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;'Allergen'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'BodyPart'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'ClinicalDept'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'DateTime'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'Disease'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;
-    &lt;span class=&quot;s&quot;&gt;'Dosage'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'Drug'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'Duration'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'Employment'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'Form'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'Frequency'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'Gender'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'Laterality'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'MedicalDevice'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;
-    &lt;span class=&quot;s&quot;&gt;'Modifier'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'Procedure'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'PsychologicalCondition'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'RaceEthnicity'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'Substance'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'Symptom'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'Test'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;
-    &lt;span class=&quot;s&quot;&gt;'Treatment'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'Vaccine'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;pretrained_zero_shot_ner&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;medical&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;PretrainedZeroShotNER&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;().&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;pretrained&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;zeroshot_ner_vop_large&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;en&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;clinical/models&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;\
-    &lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;setInputCols&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;sentence&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;token&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;\
-    &lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;setOutputCol&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;ner&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;\
-    &lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;setPredictionThreshold&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;mf&quot;&gt;0.5&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;\
-    &lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;setLabels&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;labels&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;ner_converter&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;medical&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;NerConverterInternal&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;()&lt;/span&gt;\
-    &lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;setInputCols&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;sentence&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;token&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;ner&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;\
-    &lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;setOutputCol&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;ner_chunk&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;pipeline&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;nlp&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;Pipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;().&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;setStages&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;
-    &lt;span class=&quot;n&quot;&gt;document_assembler&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;
-    &lt;span class=&quot;n&quot;&gt;sentence_detector&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;
-    &lt;span class=&quot;n&quot;&gt;tokenizer&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;
-    &lt;span class=&quot;n&quot;&gt;pretrained_zero_shot_ner&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt;
-    &lt;span class=&quot;n&quot;&gt;ner_converter&lt;/span&gt;
-&lt;span class=&quot;p&quot;&gt;])&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;data&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;spark&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;createDataFrame&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([[&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;&quot;&quot;Hello,I'm 20 year old girl. I'm diagnosed with hyperthyroid 1 month ago. I was feeling weak, light headed,poor digestion, panic attacks, depression, left chest pain, increased heart rate, rapidly weight loss,  from 4 months. Because of this, I stayed in the hospital and just discharged from hospital. I had many other blood tests, brain mri, ultrasound scan, endoscopy because of some dumb doctors bcs they were not able to diagnose actual problem. Finally I got an appointment with a homeopathy doctor finally he find that i was suffering from hyperthyroid and my TSH was 0.15 T3 and T4 is normal . Also i have b12 deficiency and vitamin D deficiency so I'm taking weekly supplement of vitamin D and 1000 mcg b12 daily. I'm taking homeopathy medicine for 40 days and took 2nd test after 30 days. My TSH is 0.5 now. I feel a little bit relief from weakness and depression but I'm facing with 2 new problem from last week that is breathtaking problem and very rapid heartrate. I just want to know if i should start allopathy medicine or homeopathy is okay? Bcs i heard that thyroid take time to start recover. So please let me know if both of medicines take same time. Because some of my friends advising me to start allopathy and never take a chance as i can develop some serious problems.Sorry for my poor english😐Thank you.&quot;&quot;&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]).&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;toDF&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;text&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;pipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;fit&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;data&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;transform&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;data&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
-
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;  &lt;/div&gt;
-  &lt;div class=&quot;language-scala highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;document_assembler&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;new&lt;/span&gt; &lt;span class=&quot;nc&quot;&gt;DocumentAssembler&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;()&lt;/span&gt;
-    &lt;span class=&quot;o&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;setInputCol&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;text&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
-    &lt;span class=&quot;o&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;setOutputCol&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;document&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;sentence_detector&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;new&lt;/span&gt; &lt;span class=&quot;nc&quot;&gt;SentenceDetector&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;()&lt;/span&gt;
-    &lt;span class=&quot;o&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;setInputCols&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;document&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
-    &lt;span class=&quot;o&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;setOutputCol&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;sentence&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;tokenizer&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;new&lt;/span&gt; &lt;span class=&quot;nc&quot;&gt;Tokenizer&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;()&lt;/span&gt;
-    &lt;span class=&quot;o&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;setInputCols&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;sentence&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
-    &lt;span class=&quot;o&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;setOutputCol&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;token&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;labels&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;nc&quot;&gt;Array&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;
-    &lt;span class=&quot;s&quot;&gt;&quot;AdmissionDischarge&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;Age&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;Allergen&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;BodyPart&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;ClinicalDept&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;DateTime&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;Disease&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt;
-    &lt;span class=&quot;s&quot;&gt;&quot;Dosage&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;Drug&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;Duration&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;Employment&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;Form&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;Frequency&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;Gender&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;Laterality&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;MedicalDevice&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt;
-    &lt;span class=&quot;s&quot;&gt;&quot;Modifier&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;Procedure&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;PsychologicalCondition&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;RaceEthnicity&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;Substance&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;Symptom&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;Test&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt;
-    &lt;span class=&quot;s&quot;&gt;&quot;Treatment&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;Vaccine&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;pretrained_zero_shot_ner&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;nc&quot;&gt;PretrainedZeroShotNER&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;().&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;pretrained&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;zeroshot_ner_vop_large&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;en&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;clinical/models&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
-    &lt;span class=&quot;o&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;setInputCols&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nc&quot;&gt;Array&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;sentence&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;token&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;))&lt;/span&gt;
-    &lt;span class=&quot;o&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;setOutputCol&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;ner&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
-    &lt;span class=&quot;o&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;setPredictionThreshold&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;mf&quot;&gt;0.5&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
-    &lt;span class=&quot;o&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;setLabels&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;labels&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;ner_converter&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;new&lt;/span&gt; &lt;span class=&quot;nc&quot;&gt;NerConverterInternal&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;()&lt;/span&gt;
-    &lt;span class=&quot;o&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;setInputCols&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nc&quot;&gt;Array&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;sentence&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;token&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;ner&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;))&lt;/span&gt;
-    &lt;span class=&quot;o&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;setOutputCol&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;ner_chunk&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
-
-
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;pipeline&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;new&lt;/span&gt; &lt;span class=&quot;nc&quot;&gt;Pipeline&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;().&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;setStages&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nc&quot;&gt;Array&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;
-    &lt;span class=&quot;n&quot;&gt;document_assembler&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt;
-    &lt;span class=&quot;n&quot;&gt;sentence_detector&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt;
-    &lt;span class=&quot;n&quot;&gt;tokenizer&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt;
-    &lt;span class=&quot;n&quot;&gt;pretrained_zero_shot_ner&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt;
-    &lt;span class=&quot;n&quot;&gt;ner_converter&lt;/span&gt;
-&lt;span class=&quot;o&quot;&gt;))&lt;/span&gt;
-
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;data&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;nc&quot;&gt;Seq&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;([[&lt;/span&gt;&lt;span class=&quot;err&quot;&gt;&quot;&quot;&quot;&lt;/span&gt;&lt;span class=&quot;kt&quot;&gt;Hello&lt;/span&gt;,&lt;span class=&quot;kt&quot;&gt;I'm&lt;/span&gt; &lt;span class=&quot;err&quot;&gt;20&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;year&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;old&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;girl.&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;I'm&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;diagnosed&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;with&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;hyperthyroid&lt;/span&gt; &lt;span class=&quot;err&quot;&gt;1&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;month&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;ago.&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;I&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;was&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;feeling&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;weak&lt;/span&gt;, &lt;span class=&quot;kt&quot;&gt;light&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;headed&lt;/span&gt;,&lt;span class=&quot;kt&quot;&gt;poor&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;digestion&lt;/span&gt;, &lt;span class=&quot;kt&quot;&gt;panic&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;attacks&lt;/span&gt;, &lt;span class=&quot;kt&quot;&gt;depression&lt;/span&gt;, &lt;span class=&quot;kt&quot;&gt;left&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;chest&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;pain&lt;/span&gt;, &lt;span class=&quot;kt&quot;&gt;increased&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;heart&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;rate&lt;/span&gt;, &lt;span class=&quot;kt&quot;&gt;rapidly&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;weight&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;loss&lt;/span&gt;,  &lt;span class=&quot;kt&quot;&gt;from&lt;/span&gt; &lt;span class=&quot;err&quot;&gt;4&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;months.&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;Because&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;of&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;this&lt;/span&gt;, &lt;span class=&quot;kt&quot;&gt;I&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;stayed&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;the&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;hospital&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;and&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;just&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;discharged&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;from&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;hospital.&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;I&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;had&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;many&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;other&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;blood&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;tests&lt;/span&gt;, &lt;span class=&quot;kt&quot;&gt;brain&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;mri&lt;/span&gt;, &lt;span class=&quot;kt&quot;&gt;ultrasound&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;scan&lt;/span&gt;, &lt;span class=&quot;kt&quot;&gt;endoscopy&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;because&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;of&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;some&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;dumb&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;doctors&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;bcs&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;they&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;were&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;not&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;able&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;to&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;diagnose&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;actual&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;problem.&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;Finally&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;I&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;got&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;an&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;appointment&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;with&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;a&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;homeopathy&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;doctor&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;finally&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;he&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;find&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;that&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;was&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;suffering&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;from&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;hyperthyroid&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;and&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;my&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;TSH&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;was&lt;/span&gt; &lt;span class=&quot;err&quot;&gt;0&lt;/span&gt;&lt;span class=&quot;kt&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;err&quot;&gt;15&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;T3&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;and&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;T4&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;is&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;normal&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;.&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;Also&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;have&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;b12&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;deficiency&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;and&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;vitamin&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;D&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;deficiency&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;so&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;I'm&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;taking&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;weekly&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;supplement&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;of&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;vitamin&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;D&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;and&lt;/span&gt; &lt;span class=&quot;err&quot;&gt;1000&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;mcg&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;b12&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;daily.&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;I'm&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;taking&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;homeopathy&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;medicine&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;err&quot;&gt;40&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;days&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;and&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;took&lt;/span&gt; &lt;span class=&quot;err&quot;&gt;2&lt;/span&gt;&lt;span class=&quot;kt&quot;&gt;nd&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;test&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;after&lt;/span&gt; &lt;span class=&quot;err&quot;&gt;30&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;days.&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;My&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;TSH&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;is&lt;/span&gt; &lt;span class=&quot;err&quot;&gt;0&lt;/span&gt;&lt;span class=&quot;kt&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;err&quot;&gt;5&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;now.&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;I&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;feel&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;a&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;little&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;bit&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;relief&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;from&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;weakness&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;and&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;depression&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;but&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;I'm&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;facing&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;with&lt;/span&gt; &lt;span class=&quot;err&quot;&gt;2&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;new&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;problem&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;from&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;last&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;week&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;that&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;is&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;breathtaking&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;problem&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;and&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;very&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;rapid&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;heartrate.&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;I&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;just&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;want&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;to&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;know&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;if&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;should&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;start&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;allopathy&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;medicine&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;or&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;homeopathy&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;is&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;okay?&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;Bcs&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;heard&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;that&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;thyroid&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;take&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;time&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;to&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;start&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;recover.&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;So&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;please&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;let&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;me&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;know&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;if&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;both&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;of&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;medicines&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;take&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;same&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;time.&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;Because&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;some&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;of&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;my&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;friends&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;advising&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;me&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;to&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;start&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;allopathy&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;and&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;never&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;take&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;a&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;chance&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;as&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;can&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;develop&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;some&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;serious&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;problems.Sorry&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;my&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;poor&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;english😐Thank&lt;/span&gt; &lt;span class=&quot;kt&quot;&gt;you.&lt;/span&gt;&lt;span class=&quot;err&quot;&gt;&quot;&quot;&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;]]).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;toDF&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;text&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;pipeline&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;fit&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;data&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;transform&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;data&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
-
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;  &lt;/div&gt;
+<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="3.9.2">Jekyll</generator><link href="/feed.xml" rel="self" type="application/atom+xml" /><link href="/" rel="alternate" type="text/html" /><updated>2024-12-10T21:00:47+00:00</updated><id>/feed.xml</id><title type="html">Spark NLP</title><subtitle>High Performance NLP with Apache Spark
+</subtitle><author><name>{&quot;type&quot;=&gt;nil, &quot;name&quot;=&gt;nil, &quot;url&quot;=&gt;nil, &quot;avatar&quot;=&gt;nil, &quot;bio&quot;=&gt;nil, &quot;email&quot;=&gt;nil, &quot;facebook&quot;=&gt;nil, &quot;twitter&quot;=&gt;nil, &quot;weibo&quot;=&gt;nil, &quot;googleplus&quot;=&gt;nil, &quot;telegram&quot;=&gt;nil, &quot;medium&quot;=&gt;nil, &quot;zhihu&quot;=&gt;nil, &quot;douban&quot;=&gt;nil, &quot;linkedin&quot;=&gt;nil, &quot;github&quot;=&gt;nil, &quot;npm&quot;=&gt;nil}</name></author><entry><title type="html">Pretrained Zero-Shot Named Entity Recognition (zeroshot_ner_vop_large)</title><link href="/2024/11/30/zeroshot_ner_vop_large_en.html" rel="alternate" type="text/html" title="Pretrained Zero-Shot Named Entity Recognition (zeroshot_ner_vop_large)" /><published>2024-11-30T00:00:00+00:00</published><updated>2024-11-30T00:00:00+00:00</updated><id>/2024/11/30/zeroshot_ner_vop_large_en</id><content type="html" xml:base="/2024/11/30/zeroshot_ner_vop_large_en.html">## Description
+
+Zero-shot Named Entity Recognition (NER) enables the identification of entities in text with minimal effort. By leveraging pre-trained language models and contextual understanding, zero-shot NER extends entity recognition capabilities to new domains and languages.
+While the model card includes default labels as examples, it is important to highlight that users are not limited to these labels. The model is designed to support any set of entity labels, allowing users to adapt it to their specific use cases. For best results, it is recommended to use labels that are conceptually similar to the provided defaults.
+
+## Predicted Entities
+
+`AdmissionDischarge`, `Age`, `Allergen`, `BodyPart`, `ClinicalDept`, `DateTime`, `Disease`,  
+`Dosage`, `Drug`, `Duration`, `Employment`, `Form`, `Frequency`, `Gender`, `Laterality`, `MedicalDevice`,  
+`Modifier`, `Procedure`, `PsychologicalCondition`, `RaceEthnicity`, `Substance`, `Symptom`, `Test`,  
+`Treatment`, `Vaccine`  
+
+{:.btn-box}
+&lt;button class=&quot;button button-orange&quot; disabled&gt;Live Demo&lt;/button&gt;
+&lt;button class=&quot;button button-orange&quot; disabled&gt;Open in Colab&lt;/button&gt;
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/zeroshot_ner_vop_large_en_5.5.1_3.0_1732980381062.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/zeroshot_ner_vop_large_en_5.5.1_3.0_1732980381062.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+&lt;div class=&quot;tabs-box&quot; markdown=&quot;1&quot;&gt;
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+  
+```python
+
+document_assembler = DocumentAssembler()\
+    .setInputCol(&quot;text&quot;)\
+    .setOutputCol(&quot;document&quot;)
+
+sentence_detector = SentenceDetector()\
+    .setInputCols([&quot;document&quot;])\
+    .setOutputCol(&quot;sentence&quot;)
+
+tokenizer = Tokenizer()\
+    .setInputCols([&quot;sentence&quot;])\
+    .setOutputCol(&quot;token&quot;)
+
+labels = [
+    'AdmissionDischarge', 'Age', 'Allergen','BodyPart','ClinicalDept','DateTime','Disease',
+    'Dosage','Drug','Duration','Employment','Form','Frequency','Gender','Laterality','MedicalDevice',
+    'Modifier','Procedure','PsychologicalCondition','RaceEthnicity','Substance','Symptom','Test',
+    'Treatment','Vaccine']
+
+pretrained_zero_shot_ner = PretrainedZeroShotNER().pretrained(&quot;zeroshot_ner_vop_large&quot;, &quot;en&quot;, &quot;clinical/models&quot;)\
+    .setInputCols(&quot;sentence&quot;, &quot;token&quot;)\
+    .setOutputCol(&quot;ner&quot;)\
+    .setPredictionThreshold(0.5)\
+    .setLabels(labels)
+
+ner_converter = NerConverterInternal()\
+    .setInputCols(&quot;sentence&quot;, &quot;token&quot;, &quot;ner&quot;)\
+    .setOutputCol(&quot;ner_chunk&quot;)
+
+pipeline = Pipeline().setStages([
+    document_assembler,
+    sentence_detector,
+    tokenizer,
+    pretrained_zero_shot_ner,
+    ner_converter
+])
+
+data = spark.createDataFrame([[&quot;&quot;&quot;Hello,I'm 20 year old girl. I'm diagnosed with hyperthyroid 1 month ago. I was feeling weak, light headed,poor digestion, panic attacks, depression, left chest pain, increased heart rate, rapidly weight loss,  from 4 months. Because of this, I stayed in the hospital and just discharged from hospital. I had many other blood tests, brain mri, ultrasound scan, endoscopy because of some dumb doctors bcs they were not able to diagnose actual problem. Finally I got an appointment with a homeopathy doctor finally he find that i was suffering from hyperthyroid and my TSH was 0.15 T3 and T4 is normal . Also i have b12 deficiency and vitamin D deficiency so I'm taking weekly supplement of vitamin D and 1000 mcg b12 daily. I'm taking homeopathy medicine for 40 days and took 2nd test after 30 days. My TSH is 0.5 now. I feel a little bit relief from weakness and depression but I'm facing with 2 new problem from last week that is breathtaking problem and very rapid heartrate. I just want to know if i should start allopathy medicine or homeopathy is okay? Bcs i heard that thyroid take time to start recover. So please let me know if both of medicines take same time. Because some of my friends advising me to start allopathy and never take a chance as i can develop some serious problems.Sorry for my poor english😐Thank you.&quot;&quot;&quot;]]).toDF(&quot;text&quot;)
+
+result = pipeline.fit(data).transform(data)
+
+```
+
+{:.jsl-block}
+```python
+
+document_assembler = nlp.DocumentAssembler()\
+    .setInputCol(&quot;text&quot;)\
+    .setOutputCol(&quot;document&quot;)
+
+sentence_detector = nlp.SentenceDetector()\
+    .setInputCols([&quot;document&quot;])\
+    .setOutputCol(&quot;sentence&quot;)
+
+tokenizer = nlp.Tokenizer()\
+    .setInputCols([&quot;sentence&quot;])\
+    .setOutputCol(&quot;token&quot;)
+
+labels = [
+    'AdmissionDischarge', 'Age', 'Allergen','BodyPart','ClinicalDept','DateTime','Disease',
+    'Dosage','Drug','Duration','Employment','Form','Frequency','Gender','Laterality','MedicalDevice',
+    'Modifier','Procedure','PsychologicalCondition','RaceEthnicity','Substance','Symptom','Test',
+    'Treatment','Vaccine']
+
+pretrained_zero_shot_ner = medical.PretrainedZeroShotNER().pretrained(&quot;zeroshot_ner_vop_large&quot;, &quot;en&quot;, &quot;clinical/models&quot;)\
+    .setInputCols(&quot;sentence&quot;, &quot;token&quot;)\
+    .setOutputCol(&quot;ner&quot;)\
+    .setPredictionThreshold(0.5)\
+    .setLabels(labels)
+
+ner_converter = medical.NerConverterInternal()\
+    .setInputCols(&quot;sentence&quot;, &quot;token&quot;, &quot;ner&quot;)\
+    .setOutputCol(&quot;ner_chunk&quot;)
+
+pipeline = nlp.Pipeline().setStages([
+    document_assembler,
+    sentence_detector,
+    tokenizer,
+    pretrained_zero_shot_ner,
+    ner_converter
+])
+
+data = spark.createDataFrame([[&quot;&quot;&quot;Hello,I'm 20 year old girl. I'm diagnosed with hyperthyroid 1 month ago. I was feeling weak, light headed,poor digestion, panic attacks, depression, left chest pain, increased heart rate, rapidly weight loss,  from 4 months. Because of this, I stayed in the hospital and just discharged from hospital. I had many other blood tests, brain mri, ultrasound scan, endoscopy because of some dumb doctors bcs they were not able to diagnose actual problem. Finally I got an appointment with a homeopathy doctor finally he find that i was suffering from hyperthyroid and my TSH was 0.15 T3 and T4 is normal . Also i have b12 deficiency and vitamin D deficiency so I'm taking weekly supplement of vitamin D and 1000 mcg b12 daily. I'm taking homeopathy medicine for 40 days and took 2nd test after 30 days. My TSH is 0.5 now. I feel a little bit relief from weakness and depression but I'm facing with 2 new problem from last week that is breathtaking problem and very rapid heartrate. I just want to know if i should start allopathy medicine or homeopathy is okay? Bcs i heard that thyroid take time to start recover. So please let me know if both of medicines take same time. Because some of my friends advising me to start allopathy and never take a chance as i can develop some serious problems.Sorry for my poor english😐Thank you.&quot;&quot;&quot;]]).toDF(&quot;text&quot;)
+
+result = pipeline.fit(data).transform(data)
+
+```
+```scala
+
+val document_assembler = new DocumentAssembler()
+    .setInputCol(&quot;text&quot;)
+    .setOutputCol(&quot;document&quot;)
+
+val sentence_detector = new SentenceDetector()
+    .setInputCols(&quot;document&quot;)
+    .setOutputCol(&quot;sentence&quot;)
+
+val tokenizer = new Tokenizer()
+    .setInputCols(&quot;sentence&quot;)
+    .setOutputCol(&quot;token&quot;)
+
+labels = Array(
+    &quot;AdmissionDischarge&quot;, &quot;Age&quot;, &quot;Allergen&quot;, &quot;BodyPart&quot;, &quot;ClinicalDept&quot;, &quot;DateTime&quot;, &quot;Disease&quot;,
+    &quot;Dosage&quot;, &quot;Drug&quot;, &quot;Duration&quot;, &quot;Employment&quot;, &quot;Form&quot;, &quot;Frequency&quot;, &quot;Gender&quot;, &quot;Laterality&quot;, &quot;MedicalDevice&quot;,
+    &quot;Modifier&quot;, &quot;Procedure&quot;, &quot;PsychologicalCondition&quot;, &quot;RaceEthnicity&quot;, &quot;Substance&quot;, &quot;Symptom&quot;, &quot;Test&quot;,
+    &quot;Treatment&quot;, &quot;Vaccine&quot;)
+
+val pretrained_zero_shot_ner = PretrainedZeroShotNER().pretrained(&quot;zeroshot_ner_vop_large&quot;, &quot;en&quot;, &quot;clinical/models&quot;)
+    .setInputCols(Array(&quot;sentence&quot;, &quot;token&quot;))
+    .setOutputCol(&quot;ner&quot;)
+    .setPredictionThreshold(0.5)
+    .setLabels(labels)
+
+val ner_converter = new NerConverterInternal()
+    .setInputCols(Array(&quot;sentence&quot;, &quot;token&quot;, &quot;ner&quot;))
+    .setOutputCol(&quot;ner_chunk&quot;)
+
+
+val pipeline = new Pipeline().setStages(Array(
+    document_assembler,
+    sentence_detector,
+    tokenizer,
+    pretrained_zero_shot_ner,
+    ner_converter
+))
+
+val data = Seq([[&quot;&quot;&quot;Hello,I'm 20 year old girl. I'm diagnosed with hyperthyroid 1 month ago. I was feeling weak, light headed,poor digestion, panic attacks, depression, left chest pain, increased heart rate, rapidly weight loss,  from 4 months. Because of this, I stayed in the hospital and just discharged from hospital. I had many other blood tests, brain mri, ultrasound scan, endoscopy because of some dumb doctors bcs they were not able to diagnose actual problem. Finally I got an appointment with a homeopathy doctor finally he find that i was suffering from hyperthyroid and my TSH was 0.15 T3 and T4 is normal . Also i have b12 deficiency and vitamin D deficiency so I'm taking weekly supplement of vitamin D and 1000 mcg b12 daily. I'm taking homeopathy medicine for 40 days and took 2nd test after 30 days. My TSH is 0.5 now. I feel a little bit relief from weakness and depression but I'm facing with 2 new problem from last week that is breathtaking problem and very rapid heartrate. I just want to know if i should start allopathy medicine or homeopathy is okay? Bcs i heard that thyroid take time to start recover. So please let me know if both of medicines take same time. Because some of my friends advising me to start allopathy and never take a chance as i can develop some serious problems.Sorry for my poor english😐Thank you.&quot;&quot;&quot;]]).toDF(&quot;text&quot;)
+
+val result = pipeline.fit(data).transform(data)
+
+```
 &lt;/div&gt;
 
-&lt;h2 id=&quot;results&quot;&gt;Results&lt;/h2&gt;
+## Results
+
+```bash
 
-&lt;div class=&quot;language-bash highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
 +--------------------+-----+----+----------------------+----------+
 |chunk               |begin|end |ner_label             |confidence|
 +--------------------+-----+----+----------------------+----------+
@@ -206,42 +214,24 @@ While the model card includes default labels as examples, it is important to hig
 |thyroid             |1075 |1081|BodyPart              |0.8369291 |
 +--------------------+-----+----+----------------------+----------+
 
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;/div&gt;
-
-&lt;h2 class=&quot;model-param&quot; id=&quot;model-information&quot;&gt;Model Information&lt;/h2&gt;
-
-&lt;table class=&quot;table-model&quot;&gt;
-  &lt;tbody&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Model Name:&lt;/td&gt;
-      &lt;td&gt;zeroshot_ner_vop_large&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Compatibility:&lt;/td&gt;
-      &lt;td&gt;Healthcare NLP 5.5.1+&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;License:&lt;/td&gt;
-      &lt;td&gt;Licensed&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Edition:&lt;/td&gt;
-      &lt;td&gt;Official&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Language:&lt;/td&gt;
-      &lt;td&gt;en&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Size:&lt;/td&gt;
-      &lt;td&gt;1.6 GB&lt;/td&gt;
-    &lt;/tr&gt;
-  &lt;/tbody&gt;
-&lt;/table&gt;
-
-&lt;h2 id=&quot;benchmarking&quot;&gt;Benchmarking&lt;/h2&gt;
-
-&lt;div class=&quot;language-bash highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;                 label  precision    recall  f1-score   support
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|zeroshot_ner_vop_large|
+|Compatibility:|Healthcare NLP 5.5.1+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+|Size:|1.6 GB|
+
+## Benchmarking
+
+```bash
+                 label  precision    recall  f1-score   support
     AdmissionDischarge     0.4507    0.9697    0.6154        33
                    Age     0.8991    0.9635    0.9302       657
               Allergen     0.3717    0.9130    0.5283        46
@@ -271,1397 +261,1248 @@ PsychologicalCondition     0.8029    0.9236    0.8590       419
               accuracy          -         -    0.9352    142994
              macro avg     0.7403    0.8890    0.7950    142994
           weighted avg     0.9471    0.9352    0.9393    142994
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;/div&gt;</content><author><name>John Snow Labs</name></author><category term="licensed" /><category term="en" /><category term="ner" /><category term="vop" /><category term="zeroshot" /><category term="clinical" /><summary type="html">Description Zero-shot Named Entity Recognition (NER) enables the identification of entities in text with minimal effort. By leveraging pre-trained language models and contextual understanding, zero-shot NER extends entity recognition capabilities to new domains and languages. While the model card includes default labels as examples, it is important to highlight that users are not limited to these labels. The model is designed to support any set of entity labels, allowing users to adapt it to their specific use cases. For best results, it is recommended to use labels that are conceptually similar to the provided defaults. Predicted Entities AdmissionDischarge, Age, Allergen, BodyPart, ClinicalDept, DateTime, Disease, Dosage, Drug, Duration, Employment, Form, Frequency, Gender, Laterality, MedicalDevice, Modifier, Procedure, PsychologicalCondition, RaceEthnicity, Substance, Symptom, Test, Treatment, Vaccine Live Demo Open in Colab Download Copy S3 URI How to use PythonHealthcare NLPPythonJohnSnowLabsScalaNLU document_assembler = DocumentAssembler()\ .setInputCol(&quot;text&quot;)\ .setOutputCol(&quot;document&quot;) sentence_detector = SentenceDetector()\ .setInputCols([&quot;document&quot;])\ .setOutputCol(&quot;sentence&quot;) tokenizer = Tokenizer()\ .setInputCols([&quot;sentence&quot;])\ .setOutputCol(&quot;token&quot;) labels = [ 'AdmissionDischarge', 'Age', 'Allergen','BodyPart','ClinicalDept','DateTime','Disease', 'Dosage','Drug','Duration','Employment','Form','Frequency','Gender','Laterality','MedicalDevice', 'Modifier','Procedure','PsychologicalCondition','RaceEthnicity','Substance','Symptom','Test', 'Treatment','Vaccine'] pretrained_zero_shot_ner = PretrainedZeroShotNER().pretrained(&quot;zeroshot_ner_vop_large&quot;, &quot;en&quot;, &quot;clinical/models&quot;)\ .setInputCols(&quot;sentence&quot;, &quot;token&quot;)\ .setOutputCol(&quot;ner&quot;)\ .setPredictionThreshold(0.5)\ .setLabels(labels) ner_converter = NerConverterInternal()\ .setInputCols(&quot;sentence&quot;, &quot;token&quot;, &quot;ner&quot;)\ .setOutputCol(&quot;ner_chunk&quot;) pipeline = Pipeline().setStages([ document_assembler, sentence_detector, tokenizer, pretrained_zero_shot_ner, ner_converter ]) data = spark.createDataFrame([[&quot;&quot;&quot;Hello,I'm 20 year old girl. I'm diagnosed with hyperthyroid 1 month ago. I was feeling weak, light headed,poor digestion, panic attacks, depression, left chest pain, increased heart rate, rapidly weight loss, from 4 months. Because of this, I stayed in the hospital and just discharged from hospital. I had many other blood tests, brain mri, ultrasound scan, endoscopy because of some dumb doctors bcs they were not able to diagnose actual problem. Finally I got an appointment with a homeopathy doctor finally he find that i was suffering from hyperthyroid and my TSH was 0.15 T3 and T4 is normal . Also i have b12 deficiency and vitamin D deficiency so I'm taking weekly supplement of vitamin D and 1000 mcg b12 daily. I'm taking homeopathy medicine for 40 days and took 2nd test after 30 days. My TSH is 0.5 now. I feel a little bit relief from weakness and depression but I'm facing with 2 new problem from last week that is breathtaking problem and very rapid heartrate. I just want to know if i should start allopathy medicine or homeopathy is okay? Bcs i heard that thyroid take time to start recover. So please let me know if both of medicines take same time. Because some of my friends advising me to start allopathy and never take a chance as i can develop some serious problems.Sorry for my poor english😐Thank you.&quot;&quot;&quot;]]).toDF(&quot;text&quot;) result = pipeline.fit(data).transform(data) document_assembler = nlp.DocumentAssembler()\ .setInputCol(&quot;text&quot;)\ .setOutputCol(&quot;document&quot;) sentence_detector = nlp.SentenceDetector()\ .setInputCols([&quot;document&quot;])\ .setOutputCol(&quot;sentence&quot;) tokenizer = nlp.Tokenizer()\ .setInputCols([&quot;sentence&quot;])\ .setOutputCol(&quot;token&quot;) labels = [ 'AdmissionDischarge', 'Age', 'Allergen','BodyPart','ClinicalDept','DateTime','Disease', 'Dosage','Drug','Duration','Employment','Form','Frequency','Gender','Laterality','MedicalDevice', 'Modifier','Procedure','PsychologicalCondition','RaceEthnicity','Substance','Symptom','Test', 'Treatment','Vaccine'] pretrained_zero_shot_ner = medical.PretrainedZeroShotNER().pretrained(&quot;zeroshot_ner_vop_large&quot;, &quot;en&quot;, &quot;clinical/models&quot;)\ .setInputCols(&quot;sentence&quot;, &quot;token&quot;)\ .setOutputCol(&quot;ner&quot;)\ .setPredictionThreshold(0.5)\ .setLabels(labels) ner_converter = medical.NerConverterInternal()\ .setInputCols(&quot;sentence&quot;, &quot;token&quot;, &quot;ner&quot;)\ .setOutputCol(&quot;ner_chunk&quot;) pipeline = nlp.Pipeline().setStages([ document_assembler, sentence_detector, tokenizer, pretrained_zero_shot_ner, ner_converter ]) data = spark.createDataFrame([[&quot;&quot;&quot;Hello,I'm 20 year old girl. I'm diagnosed with hyperthyroid 1 month ago. I was feeling weak, light headed,poor digestion, panic attacks, depression, left chest pain, increased heart rate, rapidly weight loss, from 4 months. Because of this, I stayed in the hospital and just discharged from hospital. I had many other blood tests, brain mri, ultrasound scan, endoscopy because of some dumb doctors bcs they were not able to diagnose actual problem. Finally I got an appointment with a homeopathy doctor finally he find that i was suffering from hyperthyroid and my TSH was 0.15 T3 and T4 is normal . Also i have b12 deficiency and vitamin D deficiency so I'm taking weekly supplement of vitamin D and 1000 mcg b12 daily. I'm taking homeopathy medicine for 40 days and took 2nd test after 30 days. My TSH is 0.5 now. I feel a little bit relief from weakness and depression but I'm facing with 2 new problem from last week that is breathtaking problem and very rapid heartrate. I just want to know if i should start allopathy medicine or homeopathy is okay? Bcs i heard that thyroid take time to start recover. So please let me know if both of medicines take same time. Because some of my friends advising me to start allopathy and never take a chance as i can develop some serious problems.Sorry for my poor english😐Thank you.&quot;&quot;&quot;]]).toDF(&quot;text&quot;) result = pipeline.fit(data).transform(data) val document_assembler = new DocumentAssembler() .setInputCol(&quot;text&quot;) .setOutputCol(&quot;document&quot;) val sentence_detector = new SentenceDetector() .setInputCols(&quot;document&quot;) .setOutputCol(&quot;sentence&quot;) val tokenizer = new Tokenizer() .setInputCols(&quot;sentence&quot;) .setOutputCol(&quot;token&quot;) labels = Array( &quot;AdmissionDischarge&quot;, &quot;Age&quot;, &quot;Allergen&quot;, &quot;BodyPart&quot;, &quot;ClinicalDept&quot;, &quot;DateTime&quot;, &quot;Disease&quot;, &quot;Dosage&quot;, &quot;Drug&quot;, &quot;Duration&quot;, &quot;Employment&quot;, &quot;Form&quot;, &quot;Frequency&quot;, &quot;Gender&quot;, &quot;Laterality&quot;, &quot;MedicalDevice&quot;, &quot;Modifier&quot;, &quot;Procedure&quot;, &quot;PsychologicalCondition&quot;, &quot;RaceEthnicity&quot;, &quot;Substance&quot;, &quot;Symptom&quot;, &quot;Test&quot;, &quot;Treatment&quot;, &quot;Vaccine&quot;) val pretrained_zero_shot_ner = PretrainedZeroShotNER().pretrained(&quot;zeroshot_ner_vop_large&quot;, &quot;en&quot;, &quot;clinical/models&quot;) .setInputCols(Array(&quot;sentence&quot;, &quot;token&quot;)) .setOutputCol(&quot;ner&quot;) .setPredictionThreshold(0.5) .setLabels(labels) val ner_converter = new NerConverterInternal() .setInputCols(Array(&quot;sentence&quot;, &quot;token&quot;, &quot;ner&quot;)) .setOutputCol(&quot;ner_chunk&quot;) val pipeline = new Pipeline().setStages(Array( document_assembler, sentence_detector, tokenizer, pretrained_zero_shot_ner, ner_converter )) val data = Seq([[&quot;&quot;&quot;Hello,I'm 20 year old girl. I'm diagnosed with hyperthyroid 1 month ago. I was feeling weak, light headed,poor digestion, panic attacks, depression, left chest pain, increased heart rate, rapidly weight loss, from 4 months. Because of this, I stayed in the hospital and just discharged from hospital. I had many other blood tests, brain mri, ultrasound scan, endoscopy because of some dumb doctors bcs they were not able to diagnose actual problem. Finally I got an appointment with a homeopathy doctor finally he find that i was suffering from hyperthyroid and my TSH was 0.15 T3 and T4 is normal . Also i have b12 deficiency and vitamin D deficiency so I'm taking weekly supplement of vitamin D and 1000 mcg b12 daily. I'm taking homeopathy medicine for 40 days and took 2nd test after 30 days. My TSH is 0.5 now. I feel a little bit relief from weakness and depression but I'm facing with 2 new problem from last week that is breathtaking problem and very rapid heartrate. I just want to know if i should start allopathy medicine or homeopathy is okay? Bcs i heard that thyroid take time to start recover. So please let me know if both of medicines take same time. Because some of my friends advising me to start allopathy and never take a chance as i can develop some serious problems.Sorry for my poor english😐Thank you.&quot;&quot;&quot;]]).toDF(&quot;text&quot;) val result = pipeline.fit(data).transform(data) Results +--------------------+-----+----+----------------------+----------+ |chunk |begin|end |ner_label |confidence| +--------------------+-----+----+----------------------+----------+ |20 year old |11 |21 |Age |0.99549705| |girl |23 |26 |Gender |0.99473166| |hyperthyroid |48 |59 |Disease |0.72869056| |1 month ago |61 |71 |DateTime |0.93013126| |weak |88 |91 |Symptom |0.98246646| |light |94 |98 |Symptom |0.914502 | |digestion |112 |120 |Symptom |0.7636672 | |panic attacks |123 |135 |PsychologicalCondition|0.7021233 | |depression |138 |147 |PsychologicalCondition|0.88090825| |left |150 |153 |Laterality |0.9534648 | |chest |155 |159 |BodyPart |0.90008795| |pain |161 |164 |Symptom |0.96790904| |heart rate |177 |186 |Test |0.86297375| |weight loss |197 |207 |Symptom |0.96102 | |4 months |216 |223 |Duration |0.9639058 | |hospital |259 |266 |ClinicalDept |0.93731934| |discharged |277 |286 |AdmissionDischarge |0.9054539 | |hospital |293 |300 |ClinicalDept |0.93180996| |blood tests |320 |330 |Test |0.94237006| |brain |333 |337 |BodyPart |0.7430142 | |mri |339 |341 |Test |0.93738127| |ultrasound scan |344 |358 |Test |0.94679016| |endoscopy |361 |369 |Test |0.5370005 | |doctors |392 |398 |Employment |0.7688288 | |homeopathy doctor |487 |503 |Employment |0.93872774| |he |513 |514 |Gender |0.974668 | |hyperthyroid |547 |558 |Disease |0.71458954| |TSH |567 |569 |Test |0.9895893 | |T3 |580 |581 |Test |0.9048731 | |T4 |587 |588 |Test |0.9588674 | |b12 deficiency |614 |627 |Disease |0.55843806| |vitamin D deficiency|633 |652 |Disease |0.5240673 | |weekly |668 |673 |Frequency |0.66599834| |supplement |675 |684 |Drug |0.5972447 | |vitamin D |689 |697 |Drug |0.86098486| |1000 mcg |703 |710 |Dosage |0.9711681 | |b12 |712 |714 |Drug |0.83438593| |daily |716 |720 |Frequency |0.93205667| |homeopathy medicine |734 |752 |Drug |0.5354072 | |40 days |758 |764 |Duration |0.9758632 | |after 30 days |784 |796 |DateTime |0.8755744 | |TSH |802 |804 |Test |0.98604935| |now |813 |815 |DateTime |0.8677737 | |weakness |850 |857 |Symptom |0.9838275 | |depression |863 |872 |PsychologicalCondition|0.89689744| |last week |913 |921 |DateTime |0.8067833 | |thyroid |1075 |1081|BodyPart |0.8369291 | +--------------------+-----+----+----------------------+----------+ Model Information Model Name: zeroshot_ner_vop_large Compatibility: Healthcare NLP 5.5.1+ License: Licensed Edition: Official Language: en Size: 1.6 GB Benchmarking label precision recall f1-score support AdmissionDischarge 0.4507 0.9697 0.6154 33 Age 0.8991 0.9635 0.9302 657 Allergen 0.3717 0.9130 0.5283 46 BodyPart 0.8916 0.9583 0.9238 2953 ClinicalDept 0.7534 0.9424 0.8373 295 DateTime 0.8348 0.8969 0.8648 4395 Disease 0.8917 0.7993 0.8430 2277 Dosage 0.6617 0.8898 0.7590 499 Drug 0.8271 0.9246 0.8732 1340 Duration 0.7676 0.8581 0.8103 2255 Employment 0.9338 0.9522 0.9429 1214 Form 0.6627 0.9621 0.7848 290 Frequency 0.6645 0.9149 0.7698 1093 Gender 0.9564 0.9675 0.9619 1293 Laterality 0.6961 0.9548 0.8052 619 MedicalDevice 0.5978 0.6781 0.6354 320 Modifier 0.3887 0.9112 0.5449 1092 O 0.9849 0.9468 0.9655 114576 Procedure 0.6593 0.7295 0.6926 573 PsychologicalCondition 0.8029 0.9236 0.8590 419 RaceEthnicity 0.8810 1.0000 0.9367 37 Substance 0.8292 0.9618 0.8906 419 Symptom 0.7636 0.8518 0.8053 4501 Test 0.8112 0.8481 0.8292 1560 Treatment 0.3214 0.6562 0.5315 192 Vaccine 0.9444 0.7391 0.8293 46 accuracy - - 0.9352 142994 macro avg 0.7403 0.8890 0.7950 142994 weighted avg 0.9471 0.9352 0.9393 142994</summary></entry><entry><title type="html">Clinical Deidentification Pipeline (German, Document Wise)</title><link href="/2024/11/29/clinical_deidentification_docwise_large_wip_de.html" rel="alternate" type="text/html" title="Clinical Deidentification Pipeline (German, Document Wise)" /><published>2024-11-29T00:00:00+00:00</published><updated>2024-11-29T00:00:00+00:00</updated><id>/2024/11/29/clinical_deidentification_docwise_large_wip_de</id><content type="html" xml:base="/2024/11/29/clinical_deidentification_docwise_large_wip_de.html">&lt;h2 id=&quot;description&quot;&gt;Description&lt;/h2&gt;
+```</content><author><name>John Snow Labs</name></author><category term="licensed" /><category term="en" /><category term="ner" /><category term="vop" /><category term="zeroshot" /><category term="clinical" /><summary type="html">Description Zero-shot Named Entity Recognition (NER) enables the identification of entities in text with minimal effort. By leveraging pre-trained language models and contextual understanding, zero-shot NER extends entity recognition capabilities to new domains and languages. While the model card includes default labels as examples, it is important to highlight that users are not limited to these labels. The model is designed to support any set of entity labels, allowing users to adapt it to their specific use cases. For best results, it is recommended to use labels that are conceptually similar to the provided defaults. Predicted Entities AdmissionDischarge, Age, Allergen, BodyPart, ClinicalDept, DateTime, Disease, Dosage, Drug, Duration, Employment, Form, Frequency, Gender, Laterality, MedicalDevice, Modifier, Procedure, PsychologicalCondition, RaceEthnicity, Substance, Symptom, Test, Treatment, Vaccine Live Demo Open in Colab Download Copy S3 URI How to use PythonHealthcare NLPPythonJohnSnowLabsScalaNLU document_assembler = DocumentAssembler()\ .setInputCol(&quot;text&quot;)\ .setOutputCol(&quot;document&quot;) sentence_detector = SentenceDetector()\ .setInputCols([&quot;document&quot;])\ .setOutputCol(&quot;sentence&quot;) tokenizer = Tokenizer()\ .setInputCols([&quot;sentence&quot;])\ .setOutputCol(&quot;token&quot;) labels = [ 'AdmissionDischarge', 'Age', 'Allergen','BodyPart','ClinicalDept','DateTime','Disease', 'Dosage','Drug','Duration','Employment','Form','Frequency','Gender','Laterality','MedicalDevice', 'Modifier','Procedure','PsychologicalCondition','RaceEthnicity','Substance','Symptom','Test', 'Treatment','Vaccine'] pretrained_zero_shot_ner = PretrainedZeroShotNER().pretrained(&quot;zeroshot_ner_vop_large&quot;, &quot;en&quot;, &quot;clinical/models&quot;)\ .setInputCols(&quot;sentence&quot;, &quot;token&quot;)\ .setOutputCol(&quot;ner&quot;)\ .setPredictionThreshold(0.5)\ .setLabels(labels) ner_converter = NerConverterInternal()\ .setInputCols(&quot;sentence&quot;, &quot;token&quot;, &quot;ner&quot;)\ .setOutputCol(&quot;ner_chunk&quot;) pipeline = Pipeline().setStages([ document_assembler, sentence_detector, tokenizer, pretrained_zero_shot_ner, ner_converter ]) data = spark.createDataFrame([[&quot;&quot;&quot;Hello,I'm 20 year old girl. I'm diagnosed with hyperthyroid 1 month ago. I was feeling weak, light headed,poor digestion, panic attacks, depression, left chest pain, increased heart rate, rapidly weight loss, from 4 months. Because of this, I stayed in the hospital and just discharged from hospital. I had many other blood tests, brain mri, ultrasound scan, endoscopy because of some dumb doctors bcs they were not able to diagnose actual problem. Finally I got an appointment with a homeopathy doctor finally he find that i was suffering from hyperthyroid and my TSH was 0.15 T3 and T4 is normal . Also i have b12 deficiency and vitamin D deficiency so I'm taking weekly supplement of vitamin D and 1000 mcg b12 daily. I'm taking homeopathy medicine for 40 days and took 2nd test after 30 days. My TSH is 0.5 now. I feel a little bit relief from weakness and depression but I'm facing with 2 new problem from last week that is breathtaking problem and very rapid heartrate. I just want to know if i should start allopathy medicine or homeopathy is okay? Bcs i heard that thyroid take time to start recover. So please let me know if both of medicines take same time. Because some of my friends advising me to start allopathy and never take a chance as i can develop some serious problems.Sorry for my poor english😐Thank you.&quot;&quot;&quot;]]).toDF(&quot;text&quot;) result = pipeline.fit(data).transform(data) document_assembler = nlp.DocumentAssembler()\ .setInputCol(&quot;text&quot;)\ .setOutputCol(&quot;document&quot;) sentence_detector = nlp.SentenceDetector()\ .setInputCols([&quot;document&quot;])\ .setOutputCol(&quot;sentence&quot;) tokenizer = nlp.Tokenizer()\ .setInputCols([&quot;sentence&quot;])\ .setOutputCol(&quot;token&quot;) labels = [ 'AdmissionDischarge', 'Age', 'Allergen','BodyPart','ClinicalDept','DateTime','Disease', 'Dosage','Drug','Duration','Employment','Form','Frequency','Gender','Laterality','MedicalDevice', 'Modifier','Procedure','PsychologicalCondition','RaceEthnicity','Substance','Symptom','Test', 'Treatment','Vaccine'] pretrained_zero_shot_ner = medical.PretrainedZeroShotNER().pretrained(&quot;zeroshot_ner_vop_large&quot;, &quot;en&quot;, &quot;clinical/models&quot;)\ .setInputCols(&quot;sentence&quot;, &quot;token&quot;)\ .setOutputCol(&quot;ner&quot;)\ .setPredictionThreshold(0.5)\ .setLabels(labels) ner_converter = medical.NerConverterInternal()\ .setInputCols(&quot;sentence&quot;, &quot;token&quot;, &quot;ner&quot;)\ .setOutputCol(&quot;ner_chunk&quot;) pipeline = nlp.Pipeline().setStages([ document_assembler, sentence_detector, tokenizer, pretrained_zero_shot_ner, ner_converter ]) data = spark.createDataFrame([[&quot;&quot;&quot;Hello,I'm 20 year old girl. I'm diagnosed with hyperthyroid 1 month ago. I was feeling weak, light headed,poor digestion, panic attacks, depression, left chest pain, increased heart rate, rapidly weight loss, from 4 months. Because of this, I stayed in the hospital and just discharged from hospital. I had many other blood tests, brain mri, ultrasound scan, endoscopy because of some dumb doctors bcs they were not able to diagnose actual problem. Finally I got an appointment with a homeopathy doctor finally he find that i was suffering from hyperthyroid and my TSH was 0.15 T3 and T4 is normal . Also i have b12 deficiency and vitamin D deficiency so I'm taking weekly supplement of vitamin D and 1000 mcg b12 daily. I'm taking homeopathy medicine for 40 days and took 2nd test after 30 days. My TSH is 0.5 now. I feel a little bit relief from weakness and depression but I'm facing with 2 new problem from last week that is breathtaking problem and very rapid heartrate. I just want to know if i should start allopathy medicine or homeopathy is okay? Bcs i heard that thyroid take time to start recover. So please let me know if both of medicines take same time. Because some of my friends advising me to start allopathy and never take a chance as i can develop some serious problems.Sorry for my poor english😐Thank you.&quot;&quot;&quot;]]).toDF(&quot;text&quot;) result = pipeline.fit(data).transform(data) val document_assembler = new DocumentAssembler() .setInputCol(&quot;text&quot;) .setOutputCol(&quot;document&quot;) val sentence_detector = new SentenceDetector() .setInputCols(&quot;document&quot;) .setOutputCol(&quot;sentence&quot;) val tokenizer = new Tokenizer() .setInputCols(&quot;sentence&quot;) .setOutputCol(&quot;token&quot;) labels = Array( &quot;AdmissionDischarge&quot;, &quot;Age&quot;, &quot;Allergen&quot;, &quot;BodyPart&quot;, &quot;ClinicalDept&quot;, &quot;DateTime&quot;, &quot;Disease&quot;, &quot;Dosage&quot;, &quot;Drug&quot;, &quot;Duration&quot;, &quot;Employment&quot;, &quot;Form&quot;, &quot;Frequency&quot;, &quot;Gender&quot;, &quot;Laterality&quot;, &quot;MedicalDevice&quot;, &quot;Modifier&quot;, &quot;Procedure&quot;, &quot;PsychologicalCondition&quot;, &quot;RaceEthnicity&quot;, &quot;Substance&quot;, &quot;Symptom&quot;, &quot;Test&quot;, &quot;Treatment&quot;, &quot;Vaccine&quot;) val pretrained_zero_shot_ner = PretrainedZeroShotNER().pretrained(&quot;zeroshot_ner_vop_large&quot;, &quot;en&quot;, &quot;clinical/models&quot;) .setInputCols(Array(&quot;sentence&quot;, &quot;token&quot;)) .setOutputCol(&quot;ner&quot;) .setPredictionThreshold(0.5) .setLabels(labels) val ner_converter = new NerConverterInternal() .setInputCols(Array(&quot;sentence&quot;, &quot;token&quot;, &quot;ner&quot;)) .setOutputCol(&quot;ner_chunk&quot;) val pipeline = new Pipeline().setStages(Array( document_assembler, sentence_detector, tokenizer, pretrained_zero_shot_ner, ner_converter )) val data = Seq([[&quot;&quot;&quot;Hello,I'm 20 year old girl. I'm diagnosed with hyperthyroid 1 month ago. I was feeling weak, light headed,poor digestion, panic attacks, depression, left chest pain, increased heart rate, rapidly weight loss, from 4 months. Because of this, I stayed in the hospital and just discharged from hospital. I had many other blood tests, brain mri, ultrasound scan, endoscopy because of some dumb doctors bcs they were not able to diagnose actual problem. Finally I got an appointment with a homeopathy doctor finally he find that i was suffering from hyperthyroid and my TSH was 0.15 T3 and T4 is normal . Also i have b12 deficiency and vitamin D deficiency so I'm taking weekly supplement of vitamin D and 1000 mcg b12 daily. I'm taking homeopathy medicine for 40 days and took 2nd test after 30 days. My TSH is 0.5 now. I feel a little bit relief from weakness and depression but I'm facing with 2 new problem from last week that is breathtaking problem and very rapid heartrate. I just want to know if i should start allopathy medicine or homeopathy is okay? Bcs i heard that thyroid take time to start recover. So please let me know if both of medicines take same time. Because some of my friends advising me to start allopathy and never take a chance as i can develop some serious problems.Sorry for my poor english😐Thank you.&quot;&quot;&quot;]]).toDF(&quot;text&quot;) val result = pipeline.fit(data).transform(data) Results +--------------------+-----+----+----------------------+----------+ |chunk |begin|end |ner_label |confidence| +--------------------+-----+----+----------------------+----------+ |20 year old |11 |21 |Age |0.99549705| |girl |23 |26 |Gender |0.99473166| |hyperthyroid |48 |59 |Disease |0.72869056| |1 month ago |61 |71 |DateTime |0.93013126| |weak |88 |91 |Symptom |0.98246646| |light |94 |98 |Symptom |0.914502 | |digestion |112 |120 |Symptom |0.7636672 | |panic attacks |123 |135 |PsychologicalCondition|0.7021233 | |depression |138 |147 |PsychologicalCondition|0.88090825| |left |150 |153 |Laterality |0.9534648 | |chest |155 |159 |BodyPart |0.90008795| |pain |161 |164 |Symptom |0.96790904| |heart rate |177 |186 |Test |0.86297375| |weight loss |197 |207 |Symptom |0.96102 | |4 months |216 |223 |Duration |0.9639058 | |hospital |259 |266 |ClinicalDept |0.93731934| |discharged |277 |286 |AdmissionDischarge |0.9054539 | |hospital |293 |300 |ClinicalDept |0.93180996| |blood tests |320 |330 |Test |0.94237006| |brain |333 |337 |BodyPart |0.7430142 | |mri |339 |341 |Test |0.93738127| |ultrasound scan |344 |358 |Test |0.94679016| |endoscopy |361 |369 |Test |0.5370005 | |doctors |392 |398 |Employment |0.7688288 | |homeopathy doctor |487 |503 |Employment |0.93872774| |he |513 |514 |Gender |0.974668 | |hyperthyroid |547 |558 |Disease |0.71458954| |TSH |567 |569 |Test |0.9895893 | |T3 |580 |581 |Test |0.9048731 | |T4 |587 |588 |Test |0.9588674 | |b12 deficiency |614 |627 |Disease |0.55843806| |vitamin D deficiency|633 |652 |Disease |0.5240673 | |weekly |668 |673 |Frequency |0.66599834| |supplement |675 |684 |Drug |0.5972447 | |vitamin D |689 |697 |Drug |0.86098486| |1000 mcg |703 |710 |Dosage |0.9711681 | |b12 |712 |714 |Drug |0.83438593| |daily |716 |720 |Frequency |0.93205667| |homeopathy medicine |734 |752 |Drug |0.5354072 | |40 days |758 |764 |Duration |0.9758632 | |after 30 days |784 |796 |DateTime |0.8755744 | |TSH |802 |804 |Test |0.98604935| |now |813 |815 |DateTime |0.8677737 | |weakness |850 |857 |Symptom |0.9838275 | |depression |863 |872 |PsychologicalCondition|0.89689744| |last week |913 |921 |DateTime |0.8067833 | |thyroid |1075 |1081|BodyPart |0.8369291 | +--------------------+-----+----+----------------------+----------+ Model Information Model Name: zeroshot_ner_vop_large Compatibility: Healthcare NLP 5.5.1+ License: Licensed Edition: Official Language: en Size: 1.6 GB Benchmarking label precision recall f1-score support AdmissionDischarge 0.4507 0.9697 0.6154 33 Age 0.8991 0.9635 0.9302 657 Allergen 0.3717 0.9130 0.5283 46 BodyPart 0.8916 0.9583 0.9238 2953 ClinicalDept 0.7534 0.9424 0.8373 295 DateTime 0.8348 0.8969 0.8648 4395 Disease 0.8917 0.7993 0.8430 2277 Dosage 0.6617 0.8898 0.7590 499 Drug 0.8271 0.9246 0.8732 1340 Duration 0.7676 0.8581 0.8103 2255 Employment 0.9338 0.9522 0.9429 1214 Form 0.6627 0.9621 0.7848 290 Frequency 0.6645 0.9149 0.7698 1093 Gender 0.9564 0.9675 0.9619 1293 Laterality 0.6961 0.9548 0.8052 619 MedicalDevice 0.5978 0.6781 0.6354 320 Modifier 0.3887 0.9112 0.5449 1092 O 0.9849 0.9468 0.9655 114576 Procedure 0.6593 0.7295 0.6926 573 PsychologicalCondition 0.8029 0.9236 0.8590 419 RaceEthnicity 0.8810 1.0000 0.9367 37 Substance 0.8292 0.9618 0.8906 419 Symptom 0.7636 0.8518 0.8053 4501 Test 0.8112 0.8481 0.8292 1560 Treatment 0.3214 0.6562 0.5315 192 Vaccine 0.9444 0.7391 0.8293 46 accuracy - - 0.9352 142994 macro avg 0.7403 0.8890 0.7950 142994 weighted avg 0.9471 0.9352 0.9393 142994</summary></entry><entry><title type="html">Clinical Deidentification Pipeline (German, Document Wise)</title><link href="/2024/11/29/clinical_deidentification_docwise_large_wip_de.html" rel="alternate" type="text/html" title="Clinical Deidentification Pipeline (German, Document Wise)" /><published>2024-11-29T00:00:00+00:00</published><updated>2024-11-29T00:00:00+00:00</updated><id>/2024/11/29/clinical_deidentification_docwise_large_wip_de</id><content type="html" xml:base="/2024/11/29/clinical_deidentification_docwise_large_wip_de.html">## Description
+
+This pipeline can be used to deidentify PHI information from medical texts in German language. The PHI information will be masked and obfuscated in the resulting text.
+The pipeline can mask and obfuscate: `LOCATION`, `DATE`, `NAME`, `ID`, `AGE`, `PROFESSION`, `CONTACT`, `ORGANIZATION`, `DOCTOR`, `CITY`, `COUNTRY`, `STREET`, `PATIENT`, `PHONE`, `HOSPITAL`, `STATE`, `DLN`, `SSN`, `ZIP`, `ACCOUNT`, `LICENSE`, `PLATE`, `VIN`, `MEDICALRECORD`, `EMAIL`, `URL` entities.
+
+{:.btn-box}
+&lt;button class=&quot;button button-orange&quot; disabled&gt;Live Demo&lt;/button&gt;
+&lt;button class=&quot;button button-orange&quot; disabled&gt;Open in Colab&lt;/button&gt;
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_large_wip_de_5.5.1_3.2_1732884589972.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_large_wip_de_5.5.1_3.2_1732884589972.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
 
-&lt;p&gt;This pipeline can be used to deidentify PHI information from medical texts in German language. The PHI information will be masked and obfuscated in the resulting text.
-The pipeline can mask and obfuscate: &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LOCATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;NAME&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ID&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;AGE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PROFESSION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CONTACT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ORGANIZATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DOCTOR&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CITY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;COUNTRY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STREET&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PATIENT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PHONE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;HOSPITAL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DLN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;SSN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ZIP&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ACCOUNT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LICENSE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PLATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;VIN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;MEDICALRECORD&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;EMAIL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;URL&lt;/code&gt; entities.&lt;/p&gt;
 
-&lt;p class=&quot;btn-box&quot;&gt;&lt;button class=&quot;button button-orange&quot; disabled=&quot;&quot;&gt;Live Demo&lt;/button&gt;
-&lt;button class=&quot;button button-orange&quot; disabled=&quot;&quot;&gt;Open in Colab&lt;/button&gt;
-&lt;a href=&quot;https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_large_wip_de_5.5.1_3.2_1732884589972.zip&quot; class=&quot;button button-orange button-orange-trans arr button-icon hidden&quot;&gt;Download&lt;/a&gt;
-&lt;a href=&quot;s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_large_wip_de_5.5.1_3.2_1732884589972.zip&quot; class=&quot;button button-orange button-orange-trans button-icon button-copy-s3&quot;&gt;Copy S3 URI&lt;/a&gt;&lt;/p&gt;
 
-&lt;h2 id=&quot;how-to-use&quot;&gt;How to use&lt;/h2&gt;
+&lt;div class=&quot;tabs-box&quot; markdown=&quot;1&quot;&gt;
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
 
-&lt;div class=&quot;tabs-box&quot;&gt;
-  &lt;div class=&quot;tabs-model-aproach-head&quot;&gt;&lt;button class=&quot;tab-li-model-aproach tabheader_active&quot;&gt;Python&lt;strong&gt;Healthcare NLP&lt;/strong&gt;&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach tab-li-jsl&quot;&gt;Python&lt;strong&gt;JohnSnowLabs&lt;/strong&gt;&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach&quot;&gt;Scala&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach tab-li-nlu&quot;&gt;NLU&lt;/button&gt;&lt;/div&gt;
-  &lt;div class=&quot;language-python highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
 
-&lt;span class=&quot;kn&quot;&gt;from&lt;/span&gt; &lt;span class=&quot;nn&quot;&gt;sparknlp.pretrained&lt;/span&gt; &lt;span class=&quot;kn&quot;&gt;import&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;
+from sparknlp.pretrained import PretrainedPipeline
 
-&lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;clinical_deidentification_docwise_large_wip&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;de&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;clinical/models&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
+deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_large_wip&quot;, &quot;de&quot;, &quot;clinical/models&quot;)
 
-&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot;&lt;/span&gt;
+text = &quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot;
 
-&lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;fullAnnotate&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
+deid_result = deid_pipeline.fullAnnotate(text)
 
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;mi&quot;&gt;0&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;][&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'document'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;mi&quot;&gt;0&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;][&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'masked'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;mi&quot;&gt;0&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;][&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'obfuscated'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
+print(''.join([i.result for i in deid_result[0]['document']]))
+print(''.join([i.result for i in deid_result[0]['masked']]))
+print(''.join([i.result for i in deid_result[0]['obfuscated']]))
 
 
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;  &lt;/div&gt;
-  &lt;div class=&quot;language-scala highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
+```
+```scala
 
-&lt;span class=&quot;k&quot;&gt;import&lt;/span&gt; &lt;span class=&quot;nn&quot;&gt;com.johnsnowlabs.nlp.pretrained.PretrainedPipeline&lt;/span&gt;
 
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_pipeline&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;nc&quot;&gt;PretrainedPipeline&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;clinical_deidentification_docwise_large_wip&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;de&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;clinical/models&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;text&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot;&lt;/span&gt;
+val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_large_wip&quot;, &quot;de&quot;, &quot;clinical/models&quot;)
 
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_pipeline&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;fullAnnotate&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
+val text = &quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot;
 
-&lt;span class=&quot;nf&quot;&gt;println&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;mi&quot;&gt;0&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;document&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;map&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;_&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;result&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;toString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;mkString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;))&lt;/span&gt;
-&lt;span class=&quot;nf&quot;&gt;println&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;mi&quot;&gt;0&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;masked&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;map&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;_&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;result&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;toString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;mkString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;))&lt;/span&gt;
-&lt;span class=&quot;nf&quot;&gt;println&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;mi&quot;&gt;0&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;obfuscated&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;map&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;_&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;result&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;toString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;mkString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;))&lt;/span&gt;
+val deid_result = deid_pipeline.fullAnnotate(text)
 
+println(deid_result(0)(&quot;document&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;))
+println(deid_result(0)(&quot;masked&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;))
+println(deid_result(0)(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;))
 
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;  &lt;/div&gt;
+
+```
 &lt;/div&gt;
 
-&lt;h2 id=&quot;results&quot;&gt;Results&lt;/h2&gt;
-
-&lt;div class=&quot;language-bash highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt &lt;span class=&quot;k&quot;&gt;in &lt;/span&gt;unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.
-&lt;span class=&quot;nt&quot;&gt;-------------MASKED------------&lt;/span&gt;
-Sehr geehrter Herr &amp;lt;NAME&amp;gt;, bezüglich Ihrer Anfrage vom &amp;lt;DATE&amp;gt; für Ihre Krankenakte &amp;lt;MEDICALRECORD&amp;gt; möchte ich bestätigen, dass Ihre Sozialversicherungsnummer &amp;lt;SSN&amp;gt; und Ihre Versichertennummer &amp;lt;ID&amp;gt; korrekt &lt;span class=&quot;k&quot;&gt;in &lt;/span&gt;unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen &amp;lt;PLATE&amp;gt; ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter &amp;lt;PHONE&amp;gt; oder schreiben Sie an &amp;lt;EMAIL&amp;gt;. Unsere Details finden Sie auch unter &amp;lt;URL&amp;gt;. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto &amp;lt;ACCOUNT&amp;gt;.
-&lt;span class=&quot;nt&quot;&gt;-------------OBFUSCATED-----------&lt;/span&gt;
-Sehr geehrter Herr Berta, bezüglich Ihrer Anfrage vom 22.03.2024 für Ihre Krankenakte 816698 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 91990385M363und Ihre Versichertennummer B6556697P9R korrekt &lt;span class=&quot;k&quot;&gt;in &lt;/span&gt;unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen N-RO 4769 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +70 10 25476981 oder schreiben Sie an Flaminia@google.com. Unsere Details finden Sie auch unter http://www.duran-brown.com/. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto XY21 5948 4488 4756 4354 44.
-
-
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;/div&gt;
-
-&lt;h2 class=&quot;model-param&quot; id=&quot;model-information&quot;&gt;Model Information&lt;/h2&gt;
-
-&lt;table class=&quot;table-model&quot;&gt;
-  &lt;tbody&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Model Name:&lt;/td&gt;
-      &lt;td&gt;clinical_deidentification_docwise_large_wip&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Type:&lt;/td&gt;
-      &lt;td&gt;pipeline&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Compatibility:&lt;/td&gt;
-      &lt;td&gt;Healthcare NLP 5.5.1+&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;License:&lt;/td&gt;
-      &lt;td&gt;Licensed&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Edition:&lt;/td&gt;
-      &lt;td&gt;Official&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Language:&lt;/td&gt;
-      &lt;td&gt;de&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Size:&lt;/td&gt;
-      &lt;td&gt;2.9 GB&lt;/td&gt;
-    &lt;/tr&gt;
-  &lt;/tbody&gt;
-&lt;/table&gt;
-
-&lt;h2 id=&quot;included-models&quot;&gt;Included Models&lt;/h2&gt;
-
-&lt;ul&gt;
-  &lt;li&gt;DocumentAssembler&lt;/li&gt;
-  &lt;li&gt;InternalDocumentSplitter&lt;/li&gt;
-  &lt;li&gt;TokenizerModel&lt;/li&gt;
-  &lt;li&gt;WordEmbeddingsModel&lt;/li&gt;
-  &lt;li&gt;PretrainedZeroShotNER&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ChunkFilterer&lt;/li&gt;
-  &lt;li&gt;LightDeIdentification&lt;/li&gt;
-  &lt;li&gt;LightDeIdentification&lt;/li&gt;
-&lt;/ul&gt;</content><author><name>John Snow Labs</name></author><category term="deidentification" /><category term="deid" /><category term="de" /><category term="licensed" /><category term="clinical" /><category term="pipeline" /><category term="docwise" /><category term="zero_shot" /><summary type="html">Description This pipeline can be used to deidentify PHI information from medical texts in German language. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate: LOCATION, DATE, NAME, ID, AGE, PROFESSION, CONTACT, ORGANIZATION, DOCTOR, CITY, COUNTRY, STREET, PATIENT, PHONE, HOSPITAL, STATE, DLN, SSN, ZIP, ACCOUNT, LICENSE, PLATE, VIN, MEDICALRECORD, EMAIL, URL entities. Live Demo Open in Colab Download Copy S3 URI How to use PythonHealthcare NLPPythonJohnSnowLabsScalaNLU from sparknlp.pretrained import PretrainedPipeline deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_large_wip&quot;, &quot;de&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.result for i in deid_result[0]['document']])) print(''.join([i.result for i in deid_result[0]['masked']])) print(''.join([i.result for i in deid_result[0]['obfuscated']])) import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_large_wip&quot;, &quot;de&quot;, &quot;clinical/models&quot;) val text = &quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot; val deid_result = deid_pipeline.fullAnnotate(text) println(deid_result(0)(&quot;document&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) println(deid_result(0)(&quot;masked&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) println(deid_result(0)(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) Results Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00. -------------MASKED------------ Sehr geehrter Herr &amp;lt;NAME&amp;gt;, bezüglich Ihrer Anfrage vom &amp;lt;DATE&amp;gt; für Ihre Krankenakte &amp;lt;MEDICALRECORD&amp;gt; möchte ich bestätigen, dass Ihre Sozialversicherungsnummer &amp;lt;SSN&amp;gt; und Ihre Versichertennummer &amp;lt;ID&amp;gt; korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen &amp;lt;PLATE&amp;gt; ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter &amp;lt;PHONE&amp;gt; oder schreiben Sie an &amp;lt;EMAIL&amp;gt;. Unsere Details finden Sie auch unter &amp;lt;URL&amp;gt;. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto &amp;lt;ACCOUNT&amp;gt;. -------------OBFUSCATED----------- Sehr geehrter Herr Berta, bezüglich Ihrer Anfrage vom 22.03.2024 für Ihre Krankenakte 816698 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 91990385M363und Ihre Versichertennummer B6556697P9R korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen N-RO 4769 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +70 10 25476981 oder schreiben Sie an Flaminia@google.com. Unsere Details finden Sie auch unter http://www.duran-brown.com/. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto XY21 5948 4488 4756 4354 44. Model Information Model Name: clinical_deidentification_docwise_large_wip Type: pipeline Compatibility: Healthcare NLP 5.5.1+ License: Licensed Edition: Official Language: de Size: 2.9 GB Included Models DocumentAssembler InternalDocumentSplitter TokenizerModel WordEmbeddingsModel PretrainedZeroShotNER NerConverterInternalModel MedicalNerModel NerConverterInternalModel MedicalNerModel NerConverterInternalModel RegexMatcherInternalModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel RegexMatcherInternalModel RegexMatcherInternalModel TextMatcherInternalModel TextMatcherInternalModel TextMatcherInternalModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel RegexMatcherModel RegexMatcherModel RegexMatcherModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ChunkMergeModel ChunkMergeModel ChunkMergeModel ChunkMergeModel ChunkMergeModel ChunkFilterer LightDeIdentification LightDeIdentification</summary></entry><entry><title type="html">Clinical Deidentification Pipeline (German, Document Wise)</title><link href="/2024/11/29/clinical_deidentification_docwise_wip_de.html" rel="alternate" type="text/html" title="Clinical Deidentification Pipeline (German, Document Wise)" /><published>2024-11-29T00:00:00+00:00</published><updated>2024-11-29T00:00:00+00:00</updated><id>/2024/11/29/clinical_deidentification_docwise_wip_de</id><content type="html" xml:base="/2024/11/29/clinical_deidentification_docwise_wip_de.html">&lt;h2 id=&quot;description&quot;&gt;Description&lt;/h2&gt;
-
-&lt;p&gt;This pipeline can be used to deidentify PHI information from medical texts in German language. The PHI information will be masked and obfuscated in the resulting text.
-The pipeline can mask and obfuscate: &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LOCATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;NAME&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ID&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;AGE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PROFESSION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CONTACT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ORGANIZATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DOCTOR&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CITY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;COUNTRY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STREET&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PATIENT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PHONE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;HOSPITAL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DLN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;SSN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ZIP&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ACCOUNT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LICENSE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PLATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;VIN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;MEDICALRECORD&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;EMAIL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;URL&lt;/code&gt; entities.&lt;/p&gt;
-
-&lt;p class=&quot;btn-box&quot;&gt;&lt;button class=&quot;button button-orange&quot; disabled=&quot;&quot;&gt;Live Demo&lt;/button&gt;
-&lt;button class=&quot;button button-orange&quot; disabled=&quot;&quot;&gt;Open in Colab&lt;/button&gt;
-&lt;a href=&quot;https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_wip_de_5.5.1_3.2_1732884330047.zip&quot; class=&quot;button button-orange button-orange-trans arr button-icon hidden&quot;&gt;Download&lt;/a&gt;
-&lt;a href=&quot;s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_wip_de_5.5.1_3.2_1732884330047.zip&quot; class=&quot;button button-orange button-orange-trans button-icon button-copy-s3&quot;&gt;Copy S3 URI&lt;/a&gt;&lt;/p&gt;
-
-&lt;h2 id=&quot;how-to-use&quot;&gt;How to use&lt;/h2&gt;
-
-&lt;div class=&quot;tabs-box&quot;&gt;
-  &lt;div class=&quot;tabs-model-aproach-head&quot;&gt;&lt;button class=&quot;tab-li-model-aproach tabheader_active&quot;&gt;Python&lt;strong&gt;Healthcare NLP&lt;/strong&gt;&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach tab-li-jsl&quot;&gt;Python&lt;strong&gt;JohnSnowLabs&lt;/strong&gt;&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach&quot;&gt;Scala&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach tab-li-nlu&quot;&gt;NLU&lt;/button&gt;&lt;/div&gt;
-  &lt;div class=&quot;language-python highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-
-&lt;span class=&quot;kn&quot;&gt;from&lt;/span&gt; &lt;span class=&quot;nn&quot;&gt;sparknlp.pretrained&lt;/span&gt; &lt;span class=&quot;kn&quot;&gt;import&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;clinical_deidentification_docwise_wip&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;de&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;clinical/models&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot;&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;fullAnnotate&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;mi&quot;&gt;0&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;][&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'document'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;mi&quot;&gt;0&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;][&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'masked'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;mi&quot;&gt;0&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;][&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'obfuscated'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
-
-
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;  &lt;/div&gt;
-  &lt;div class=&quot;language-scala highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-
-&lt;span class=&quot;k&quot;&gt;import&lt;/span&gt; &lt;span class=&quot;nn&quot;&gt;com.johnsnowlabs.nlp.pretrained.PretrainedPipeline&lt;/span&gt;
-
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_pipeline&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;nc&quot;&gt;PretrainedPipeline&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;clinical_deidentification_docwise_wip&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;de&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;clinical/models&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;text&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot;&lt;/span&gt;
-
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_pipeline&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;fullAnnotate&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;nf&quot;&gt;println&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;mi&quot;&gt;0&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;document&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;map&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;_&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;result&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;toString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;mkString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;))&lt;/span&gt;
-&lt;span class=&quot;nf&quot;&gt;println&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;mi&quot;&gt;0&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;masked&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;map&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;_&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;result&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;toString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;mkString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;))&lt;/span&gt;
-&lt;span class=&quot;nf&quot;&gt;println&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;mi&quot;&gt;0&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;obfuscated&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;map&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;_&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;result&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;toString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;mkString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;))&lt;/span&gt;
-
-
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;  &lt;/div&gt;
+## Results
+
+```bash
+
+Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.
+-------------MASKED------------
+Sehr geehrter Herr &lt;NAME&gt;, bezüglich Ihrer Anfrage vom &lt;DATE&gt; für Ihre Krankenakte &lt;MEDICALRECORD&gt; möchte ich bestätigen, dass Ihre Sozialversicherungsnummer &lt;SSN&gt; und Ihre Versichertennummer &lt;ID&gt; korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen &lt;PLATE&gt; ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter &lt;PHONE&gt; oder schreiben Sie an &lt;EMAIL&gt;. Unsere Details finden Sie auch unter &lt;URL&gt;. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto &lt;ACCOUNT&gt;.
+-------------OBFUSCATED-----------
+Sehr geehrter Herr Berta, bezüglich Ihrer Anfrage vom 22.03.2024 für Ihre Krankenakte 816698 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 91990385M363und Ihre Versichertennummer B6556697P9R korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen N-RO 4769 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +70 10 25476981 oder schreiben Sie an Flaminia@google.com. Unsere Details finden Sie auch unter http://www.duran-brown.com/. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto XY21 5948 4488 4756 4354 44.
+
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|clinical_deidentification_docwise_large_wip|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 5.5.1+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|de|
+|Size:|2.9 GB|
+
+## Included Models
+
+- DocumentAssembler
+- InternalDocumentSplitter
+- TokenizerModel
+- WordEmbeddingsModel
+- PretrainedZeroShotNER
+- NerConverterInternalModel
+- MedicalNerModel
+- NerConverterInternalModel
+- MedicalNerModel
+- NerConverterInternalModel
+- RegexMatcherInternalModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- RegexMatcherInternalModel
+- RegexMatcherInternalModel
+- TextMatcherInternalModel
+- TextMatcherInternalModel
+- TextMatcherInternalModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- RegexMatcherModel
+- RegexMatcherModel
+- RegexMatcherModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ChunkMergeModel
+- ChunkMergeModel
+- ChunkMergeModel
+- ChunkMergeModel
+- ChunkMergeModel
+- ChunkFilterer
+- LightDeIdentification
+- LightDeIdentification</content><author><name>John Snow Labs</name></author><category term="deidentification" /><category term="deid" /><category term="de" /><category term="licensed" /><category term="clinical" /><category term="pipeline" /><category term="docwise" /><category term="zero_shot" /><summary type="html">Description This pipeline can be used to deidentify PHI information from medical texts in German language. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate: LOCATION, DATE, NAME, ID, AGE, PROFESSION, CONTACT, ORGANIZATION, DOCTOR, CITY, COUNTRY, STREET, PATIENT, PHONE, HOSPITAL, STATE, DLN, SSN, ZIP, ACCOUNT, LICENSE, PLATE, VIN, MEDICALRECORD, EMAIL, URL entities. Live Demo Open in Colab Download Copy S3 URI How to use PythonHealthcare NLPPythonJohnSnowLabsScalaNLU from sparknlp.pretrained import PretrainedPipeline deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_large_wip&quot;, &quot;de&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.result for i in deid_result[0]['document']])) print(''.join([i.result for i in deid_result[0]['masked']])) print(''.join([i.result for i in deid_result[0]['obfuscated']])) import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_large_wip&quot;, &quot;de&quot;, &quot;clinical/models&quot;) val text = &quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot; val deid_result = deid_pipeline.fullAnnotate(text) println(deid_result(0)(&quot;document&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) println(deid_result(0)(&quot;masked&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) println(deid_result(0)(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) Results Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00. -------------MASKED------------ Sehr geehrter Herr &amp;lt;NAME&amp;gt;, bezüglich Ihrer Anfrage vom &amp;lt;DATE&amp;gt; für Ihre Krankenakte &amp;lt;MEDICALRECORD&amp;gt; möchte ich bestätigen, dass Ihre Sozialversicherungsnummer &amp;lt;SSN&amp;gt; und Ihre Versichertennummer &amp;lt;ID&amp;gt; korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen &amp;lt;PLATE&amp;gt; ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter &amp;lt;PHONE&amp;gt; oder schreiben Sie an &amp;lt;EMAIL&amp;gt;. Unsere Details finden Sie auch unter &amp;lt;URL&amp;gt;. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto &amp;lt;ACCOUNT&amp;gt;. -------------OBFUSCATED----------- Sehr geehrter Herr Berta, bezüglich Ihrer Anfrage vom 22.03.2024 für Ihre Krankenakte 816698 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 91990385M363und Ihre Versichertennummer B6556697P9R korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen N-RO 4769 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +70 10 25476981 oder schreiben Sie an Flaminia@google.com. Unsere Details finden Sie auch unter http://www.duran-brown.com/. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto XY21 5948 4488 4756 4354 44. Model Information Model Name: clinical_deidentification_docwise_large_wip Type: pipeline Compatibility: Healthcare NLP 5.5.1+ License: Licensed Edition: Official Language: de Size: 2.9 GB Included Models DocumentAssembler InternalDocumentSplitter TokenizerModel WordEmbeddingsModel PretrainedZeroShotNER NerConverterInternalModel MedicalNerModel NerConverterInternalModel MedicalNerModel NerConverterInternalModel RegexMatcherInternalModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel RegexMatcherInternalModel RegexMatcherInternalModel TextMatcherInternalModel TextMatcherInternalModel TextMatcherInternalModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel RegexMatcherModel RegexMatcherModel RegexMatcherModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ChunkMergeModel ChunkMergeModel ChunkMergeModel ChunkMergeModel ChunkMergeModel ChunkFilterer LightDeIdentification LightDeIdentification</summary></entry><entry><title type="html">Clinical Deidentification Pipeline (German, Document Wise)</title><link href="/2024/11/29/clinical_deidentification_docwise_wip_de.html" rel="alternate" type="text/html" title="Clinical Deidentification Pipeline (German, Document Wise)" /><published>2024-11-29T00:00:00+00:00</published><updated>2024-11-29T00:00:00+00:00</updated><id>/2024/11/29/clinical_deidentification_docwise_wip_de</id><content type="html" xml:base="/2024/11/29/clinical_deidentification_docwise_wip_de.html">## Description
+
+This pipeline can be used to deidentify PHI information from medical texts in German language. The PHI information will be masked and obfuscated in the resulting text.
+The pipeline can mask and obfuscate: `LOCATION`, `DATE`, `NAME`, `ID`, `AGE`, `PROFESSION`, `CONTACT`, `ORGANIZATION`, `DOCTOR`, `CITY`, `COUNTRY`, `STREET`, `PATIENT`, `PHONE`, `HOSPITAL`, `STATE`, `DLN`, `SSN`, `ZIP`, `ACCOUNT`, `LICENSE`, `PLATE`, `VIN`, `MEDICALRECORD`, `EMAIL`, `URL` entities.
+
+{:.btn-box}
+&lt;button class=&quot;button button-orange&quot; disabled&gt;Live Demo&lt;/button&gt;
+&lt;button class=&quot;button button-orange&quot; disabled&gt;Open in Colab&lt;/button&gt;
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_wip_de_5.5.1_3.2_1732884330047.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_wip_de_5.5.1_3.2_1732884330047.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+&lt;div class=&quot;tabs-box&quot; markdown=&quot;1&quot;&gt;
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+
+from sparknlp.pretrained import PretrainedPipeline
+
+deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;de&quot;, &quot;clinical/models&quot;)
+
+text = &quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot;
+
+deid_result = deid_pipeline.fullAnnotate(text)
+
+print(''.join([i.result for i in deid_result[0]['document']]))
+print(''.join([i.result for i in deid_result[0]['masked']]))
+print(''.join([i.result for i in deid_result[0]['obfuscated']]))
+
+
+```
+```scala
+
+
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;de&quot;, &quot;clinical/models&quot;)
+
+val text = &quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot;
+
+val deid_result = deid_pipeline.fullAnnotate(text)
+
+println(deid_result(0)(&quot;document&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;))
+println(deid_result(0)(&quot;masked&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;))
+println(deid_result(0)(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;))
+
+
+```
 &lt;/div&gt;
 
-&lt;h2 id=&quot;results&quot;&gt;Results&lt;/h2&gt;
-
-&lt;div class=&quot;language-bash highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt &lt;span class=&quot;k&quot;&gt;in &lt;/span&gt;unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.
-&lt;span class=&quot;nt&quot;&gt;-------------MASKED------------&lt;/span&gt;
-Sehr geehrter Herr &amp;lt;NAME&amp;gt;, bezüglich Ihrer Anfrage vom &amp;lt;DATE&amp;gt; für Ihre Krankenakte &amp;lt;MEDICALRECORD&amp;gt; möchte ich bestätigen, dass Ihre Sozialversicherungsnummer &amp;lt;SSN&amp;gt; und Ihre Versichertennummer &amp;lt;ID&amp;gt; korrekt &lt;span class=&quot;k&quot;&gt;in &lt;/span&gt;unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen &amp;lt;PLATE&amp;gt; ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter &amp;lt;PHONE&amp;gt; oder schreiben Sie an &amp;lt;EMAIL&amp;gt;. Unsere Details finden Sie auch unter &amp;lt;URL&amp;gt;. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto &amp;lt;ACCOUNT&amp;gt;.
-&lt;span class=&quot;nt&quot;&gt;-------------OBFUSCATED-----------&lt;/span&gt;
-Sehr geehrter Herr Johan, bezüglich Ihrer Anfrage vom 14.05.2024 für Ihre Krankenakte 149921 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 68667052J030und Ihre Versichertennummer C1001142Q4S korrekt &lt;span class=&quot;k&quot;&gt;in &lt;/span&gt;unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen L-PM 6981 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +81 21 36587092 oder schreiben Sie an Aeron@google.com. Unsere Details finden Sie auch unter PoliticalMakeover.com.ee. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto IJ32 6059 5599 5867 5465 55.
-
-
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;/div&gt;
-
-&lt;h2 class=&quot;model-param&quot; id=&quot;model-information&quot;&gt;Model Information&lt;/h2&gt;
-
-&lt;table class=&quot;table-model&quot;&gt;
-  &lt;tbody&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Model Name:&lt;/td&gt;
-      &lt;td&gt;clinical_deidentification_docwise_wip&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Type:&lt;/td&gt;
-      &lt;td&gt;pipeline&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Compatibility:&lt;/td&gt;
-      &lt;td&gt;Healthcare NLP 5.5.1+&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;License:&lt;/td&gt;
-      &lt;td&gt;Licensed&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Edition:&lt;/td&gt;
-      &lt;td&gt;Official&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Language:&lt;/td&gt;
-      &lt;td&gt;de&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Size:&lt;/td&gt;
-      &lt;td&gt;1.3 GB&lt;/td&gt;
-    &lt;/tr&gt;
-  &lt;/tbody&gt;
-&lt;/table&gt;
-
-&lt;h2 id=&quot;included-models&quot;&gt;Included Models&lt;/h2&gt;
-
-&lt;ul&gt;
-  &lt;li&gt;DocumentAssembler&lt;/li&gt;
-  &lt;li&gt;InternalDocumentSplitter&lt;/li&gt;
-  &lt;li&gt;TokenizerModel&lt;/li&gt;
-  &lt;li&gt;WordEmbeddingsModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ChunkFilterer&lt;/li&gt;
-  &lt;li&gt;LightDeIdentification&lt;/li&gt;
-  &lt;li&gt;LightDeIdentification&lt;/li&gt;
-&lt;/ul&gt;</content><author><name>John Snow Labs</name></author><category term="deidentification" /><category term="deid" /><category term="de" /><category term="licensed" /><category term="clinical" /><category term="pipeline" /><category term="docwise" /><summary type="html">Description This pipeline can be used to deidentify PHI information from medical texts in German language. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate: LOCATION, DATE, NAME, ID, AGE, PROFESSION, CONTACT, ORGANIZATION, DOCTOR, CITY, COUNTRY, STREET, PATIENT, PHONE, HOSPITAL, STATE, DLN, SSN, ZIP, ACCOUNT, LICENSE, PLATE, VIN, MEDICALRECORD, EMAIL, URL entities. Live Demo Open in Colab Download Copy S3 URI How to use PythonHealthcare NLPPythonJohnSnowLabsScalaNLU from sparknlp.pretrained import PretrainedPipeline deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;de&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.result for i in deid_result[0]['document']])) print(''.join([i.result for i in deid_result[0]['masked']])) print(''.join([i.result for i in deid_result[0]['obfuscated']])) import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;de&quot;, &quot;clinical/models&quot;) val text = &quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot; val deid_result = deid_pipeline.fullAnnotate(text) println(deid_result(0)(&quot;document&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) println(deid_result(0)(&quot;masked&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) println(deid_result(0)(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) Results Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00. -------------MASKED------------ Sehr geehrter Herr &amp;lt;NAME&amp;gt;, bezüglich Ihrer Anfrage vom &amp;lt;DATE&amp;gt; für Ihre Krankenakte &amp;lt;MEDICALRECORD&amp;gt; möchte ich bestätigen, dass Ihre Sozialversicherungsnummer &amp;lt;SSN&amp;gt; und Ihre Versichertennummer &amp;lt;ID&amp;gt; korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen &amp;lt;PLATE&amp;gt; ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter &amp;lt;PHONE&amp;gt; oder schreiben Sie an &amp;lt;EMAIL&amp;gt;. Unsere Details finden Sie auch unter &amp;lt;URL&amp;gt;. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto &amp;lt;ACCOUNT&amp;gt;. -------------OBFUSCATED----------- Sehr geehrter Herr Johan, bezüglich Ihrer Anfrage vom 14.05.2024 für Ihre Krankenakte 149921 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 68667052J030und Ihre Versichertennummer C1001142Q4S korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen L-PM 6981 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +81 21 36587092 oder schreiben Sie an Aeron@google.com. Unsere Details finden Sie auch unter PoliticalMakeover.com.ee. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto IJ32 6059 5599 5867 5465 55. Model Information Model Name: clinical_deidentification_docwise_wip Type: pipeline Compatibility: Healthcare NLP 5.5.1+ License: Licensed Edition: Official Language: de Size: 1.3 GB Included Models DocumentAssembler InternalDocumentSplitter TokenizerModel WordEmbeddingsModel MedicalNerModel NerConverterInternalModel MedicalNerModel NerConverterInternalModel MedicalNerModel NerConverterInternalModel RegexMatcherInternalModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel RegexMatcherInternalModel RegexMatcherInternalModel TextMatcherInternalModel TextMatcherInternalModel TextMatcherInternalModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel RegexMatcherModel RegexMatcherModel RegexMatcherModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ChunkMergeModel ChunkMergeModel ChunkMergeModel ChunkMergeModel ChunkMergeModel ChunkFilterer LightDeIdentification LightDeIdentification</summary></entry><entry><title type="html">Clinical Deidentification Pipeline (Document Wise)</title><link href="/2024/11/29/clinical_deidentification_docwise_wip_en.html" rel="alternate" type="text/html" title="Clinical Deidentification Pipeline (Document Wise)" /><published>2024-11-29T00:00:00+00:00</published><updated>2024-11-29T00:00:00+00:00</updated><id>/2024/11/29/clinical_deidentification_docwise_wip_en</id><content type="html" xml:base="/2024/11/29/clinical_deidentification_docwise_wip_en.html">&lt;h2 id=&quot;description&quot;&gt;Description&lt;/h2&gt;
-
-&lt;p&gt;This pipeline can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text.
-The pipeline can mask and obfuscate &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LOCATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CONTACT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PROFESSION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;NAME&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;AGE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;MEDICALRECORD&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ORGANIZATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;HEALTHPLAN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DOCTOR&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;USERNAME&lt;/code&gt;,
-&lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LOCATION-OTHER&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;URL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DEVICE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CITY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ZIP&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PATIENT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;COUNTRY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STREET&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PHONE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;HOSPITAL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;EMAIL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;IDNUM&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;BIOID&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;FAX&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LOCATION_OTHER&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DLN&lt;/code&gt;,
-&lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;SSN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ACCOUNT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PLATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;VIN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LICENSE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;IP&lt;/code&gt; entities.&lt;/p&gt;
-
-&lt;h2 id=&quot;predicted-entities&quot;&gt;Predicted Entities&lt;/h2&gt;
-
-&lt;p&gt;&lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ACCOUNT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;AGE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CITY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CONTACT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;COUNTRY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DLN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DOCTOR&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;EMAIL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;EXCLUDED&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;HOSPITAL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ID&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LICENSE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LOCATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;MEDICALRECORD&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;NAME&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ORGANIZATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PATIENT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PHONE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PLATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PROFESSION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;SSN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STREET&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;URL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;USERNAME&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;VIN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ZIP&lt;/code&gt;&lt;/p&gt;
-
-&lt;p class=&quot;btn-box&quot;&gt;&lt;button class=&quot;button button-orange&quot; disabled=&quot;&quot;&gt;Live Demo&lt;/button&gt;
-&lt;button class=&quot;button button-orange&quot; disabled=&quot;&quot;&gt;Open in Colab&lt;/button&gt;
-&lt;a href=&quot;https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_wip_en_5.5.0_3.2_1732869574315.zip&quot; class=&quot;button button-orange button-orange-trans arr button-icon hidden&quot;&gt;Download&lt;/a&gt;
-&lt;a href=&quot;s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_wip_en_5.5.0_3.2_1732869574315.zip&quot; class=&quot;button button-orange button-orange-trans button-icon button-copy-s3&quot;&gt;Copy S3 URI&lt;/a&gt;&lt;/p&gt;
-
-&lt;h2 id=&quot;how-to-use&quot;&gt;How to use&lt;/h2&gt;
-
-&lt;div class=&quot;tabs-box&quot;&gt;
-  &lt;div class=&quot;tabs-model-aproach-head&quot;&gt;&lt;button class=&quot;tab-li-model-aproach tabheader_active&quot;&gt;Python&lt;strong&gt;Healthcare NLP&lt;/strong&gt;&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach tab-li-jsl&quot;&gt;Python&lt;strong&gt;JohnSnowLabs&lt;/strong&gt;&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach&quot;&gt;Scala&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach tab-li-nlu&quot;&gt;NLU&lt;/button&gt;&lt;/div&gt;
-
-  &lt;div class=&quot;language-python highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-&lt;span class=&quot;kn&quot;&gt;from&lt;/span&gt; &lt;span class=&quot;nn&quot;&gt;sparknlp.pretrained&lt;/span&gt; &lt;span class=&quot;kn&quot;&gt;import&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;clinical_deidentification_docwise_wip&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;en&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;clinical/models&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
+## Results
+
+```bash
+
+Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.
+-------------MASKED------------
+Sehr geehrter Herr &lt;NAME&gt;, bezüglich Ihrer Anfrage vom &lt;DATE&gt; für Ihre Krankenakte &lt;MEDICALRECORD&gt; möchte ich bestätigen, dass Ihre Sozialversicherungsnummer &lt;SSN&gt; und Ihre Versichertennummer &lt;ID&gt; korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen &lt;PLATE&gt; ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter &lt;PHONE&gt; oder schreiben Sie an &lt;EMAIL&gt;. Unsere Details finden Sie auch unter &lt;URL&gt;. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto &lt;ACCOUNT&gt;.
+-------------OBFUSCATED-----------
+Sehr geehrter Herr Johan, bezüglich Ihrer Anfrage vom 14.05.2024 für Ihre Krankenakte 149921 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 68667052J030und Ihre Versichertennummer C1001142Q4S korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen L-PM 6981 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +81 21 36587092 oder schreiben Sie an Aeron@google.com. Unsere Details finden Sie auch unter PoliticalMakeover.com.ee. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto IJ32 6059 5599 5867 5465 55.
+
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|clinical_deidentification_docwise_wip|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 5.5.1+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|de|
+|Size:|1.3 GB|
+
+## Included Models
+
+- DocumentAssembler
+- InternalDocumentSplitter
+- TokenizerModel
+- WordEmbeddingsModel
+- MedicalNerModel
+- NerConverterInternalModel
+- MedicalNerModel
+- NerConverterInternalModel
+- MedicalNerModel
+- NerConverterInternalModel
+- RegexMatcherInternalModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- RegexMatcherInternalModel
+- RegexMatcherInternalModel
+- TextMatcherInternalModel
+- TextMatcherInternalModel
+- TextMatcherInternalModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- RegexMatcherModel
+- RegexMatcherModel
+- RegexMatcherModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ChunkMergeModel
+- ChunkMergeModel
+- ChunkMergeModel
+- ChunkMergeModel
+- ChunkMergeModel
+- ChunkFilterer
+- LightDeIdentification
+- LightDeIdentification</content><author><name>John Snow Labs</name></author><category term="deidentification" /><category term="deid" /><category term="de" /><category term="licensed" /><category term="clinical" /><category term="pipeline" /><category term="docwise" /><summary type="html">Description This pipeline can be used to deidentify PHI information from medical texts in German language. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate: LOCATION, DATE, NAME, ID, AGE, PROFESSION, CONTACT, ORGANIZATION, DOCTOR, CITY, COUNTRY, STREET, PATIENT, PHONE, HOSPITAL, STATE, DLN, SSN, ZIP, ACCOUNT, LICENSE, PLATE, VIN, MEDICALRECORD, EMAIL, URL entities. Live Demo Open in Colab Download Copy S3 URI How to use PythonHealthcare NLPPythonJohnSnowLabsScalaNLU from sparknlp.pretrained import PretrainedPipeline deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;de&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.result for i in deid_result[0]['document']])) print(''.join([i.result for i in deid_result[0]['masked']])) print(''.join([i.result for i in deid_result[0]['obfuscated']])) import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;de&quot;, &quot;clinical/models&quot;) val text = &quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot; val deid_result = deid_pipeline.fullAnnotate(text) println(deid_result(0)(&quot;document&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) println(deid_result(0)(&quot;masked&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) println(deid_result(0)(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) Results Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00. -------------MASKED------------ Sehr geehrter Herr &amp;lt;NAME&amp;gt;, bezüglich Ihrer Anfrage vom &amp;lt;DATE&amp;gt; für Ihre Krankenakte &amp;lt;MEDICALRECORD&amp;gt; möchte ich bestätigen, dass Ihre Sozialversicherungsnummer &amp;lt;SSN&amp;gt; und Ihre Versichertennummer &amp;lt;ID&amp;gt; korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen &amp;lt;PLATE&amp;gt; ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter &amp;lt;PHONE&amp;gt; oder schreiben Sie an &amp;lt;EMAIL&amp;gt;. Unsere Details finden Sie auch unter &amp;lt;URL&amp;gt;. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto &amp;lt;ACCOUNT&amp;gt;. -------------OBFUSCATED----------- Sehr geehrter Herr Johan, bezüglich Ihrer Anfrage vom 14.05.2024 für Ihre Krankenakte 149921 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 68667052J030und Ihre Versichertennummer C1001142Q4S korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen L-PM 6981 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +81 21 36587092 oder schreiben Sie an Aeron@google.com. Unsere Details finden Sie auch unter PoliticalMakeover.com.ee. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto IJ32 6059 5599 5867 5465 55. Model Information Model Name: clinical_deidentification_docwise_wip Type: pipeline Compatibility: Healthcare NLP 5.5.1+ License: Licensed Edition: Official Language: de Size: 1.3 GB Included Models DocumentAssembler InternalDocumentSplitter TokenizerModel WordEmbeddingsModel MedicalNerModel NerConverterInternalModel MedicalNerModel NerConverterInternalModel MedicalNerModel NerConverterInternalModel RegexMatcherInternalModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel RegexMatcherInternalModel RegexMatcherInternalModel TextMatcherInternalModel TextMatcherInternalModel TextMatcherInternalModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel RegexMatcherModel RegexMatcherModel RegexMatcherModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ChunkMergeModel ChunkMergeModel ChunkMergeModel ChunkMergeModel ChunkMergeModel ChunkFilterer LightDeIdentification LightDeIdentification</summary></entry><entry><title type="html">Clinical Deidentification Pipeline (Document Wise)</title><link href="/2024/11/29/clinical_deidentification_docwise_wip_en.html" rel="alternate" type="text/html" title="Clinical Deidentification Pipeline (Document Wise)" /><published>2024-11-29T00:00:00+00:00</published><updated>2024-11-29T00:00:00+00:00</updated><id>/2024/11/29/clinical_deidentification_docwise_wip_en</id><content type="html" xml:base="/2024/11/29/clinical_deidentification_docwise_wip_en.html">## Description
+
+This pipeline can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text.
+The pipeline can mask and obfuscate `LOCATION`, `CONTACT`, `PROFESSION`, `NAME`, `DATE`, `AGE`, `MEDICALRECORD`, `ORGANIZATION`, `HEALTHPLAN`, `DOCTOR`, `USERNAME`,
+`LOCATION-OTHER`, `URL`, `DEVICE`, `CITY`, `ZIP`, `STATE`, `PATIENT`, `COUNTRY`, `STREET`, `PHONE`, `HOSPITAL`, `EMAIL`, `IDNUM`, `BIOID`, `FAX`, `LOCATION_OTHER`, `DLN`,
+`SSN`, `ACCOUNT`, `PLATE`, `VIN`, `LICENSE`, `IP` entities.
+
+## Predicted Entities
+
+`ACCOUNT`, `AGE`, `CITY`, `CONTACT`, `COUNTRY`, `DATE`, `DLN`, `DOCTOR`, `EMAIL`, `EXCLUDED`, `HOSPITAL`, `ID`, `LICENSE`, `LOCATION`, `MEDICALRECORD`, `NAME`, `ORGANIZATION`, `PATIENT`, `PHONE`, `PLATE`, `PROFESSION`, `SSN`, `STATE`, `STREET`, `URL`, `USERNAME`, `VIN`, `ZIP`
+
+
+{:.btn-box}
+&lt;button class=&quot;button button-orange&quot; disabled&gt;Live Demo&lt;/button&gt;
+&lt;button class=&quot;button button-orange&quot; disabled&gt;Open in Colab&lt;/button&gt;
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_wip_en_5.5.0_3.2_1732869574315.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_wip_en_5.5.0_3.2_1732869574315.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+&lt;div class=&quot;tabs-box&quot; markdown=&quot;1&quot;&gt;
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+  
+```python
+
+from sparknlp.pretrained import PretrainedPipeline
+
+deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;)
+
+text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
 The patient’s medical record number is 56467890.
-The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;&lt;/span&gt;
+The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;
+
+deid_result = deid_pipeline.fullAnnotate(text)
 
-&lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;fullAnnotate&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
+print(''.join([i.result for i in deid_result['mask_entity']]))
+print(''.join([i.result for i in deid_result['obfuscated']]))
 
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'mask_entity'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'obfuscated'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
 
+```
 
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;  &lt;/div&gt;
+{:.jsl-block}
+```python
 
-  &lt;div class=&quot;language-python jsl-block highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-&lt;span class=&quot;kn&quot;&gt;from&lt;/span&gt; &lt;span class=&quot;nn&quot;&gt;sparknlp.pretrained&lt;/span&gt; &lt;span class=&quot;kn&quot;&gt;import&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;
+from sparknlp.pretrained import PretrainedPipeline
 
-&lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;nlp&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;clinical_deidentification_docwise_wip&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;en&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;clinical/models&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
+deid_pipeline = nlp.PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;)
 
-&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
+text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
 The patient’s medical record number is 56467890.
-The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;&lt;/span&gt;
+The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;
 
-&lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;fullAnnotate&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
+deid_result = deid_pipeline.fullAnnotate(text)
 
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'mask_entity'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'obfuscated'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
+print(''.join([i.result for i in deid_result['mask_entity']]))
+print(''.join([i.result for i in deid_result['obfuscated']]))
 
 
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;  &lt;/div&gt;
-  &lt;div class=&quot;language-scala highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-&lt;span class=&quot;k&quot;&gt;import&lt;/span&gt; &lt;span class=&quot;nn&quot;&gt;com.johnsnowlabs.nlp.pretrained.PretrainedPipeline&lt;/span&gt;
+```
+```scala
 
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_pipeline&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;nc&quot;&gt;PretrainedPipeline&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;clinical_deidentification_docwise_wip&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;en&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;clinical/models&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;text&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
+val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;)
+
+val text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
 The patient’s medical record number is 56467890.
-The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;&lt;/span&gt;
+The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;
 
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_pipeline&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;fullAnnotate&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
+val deid_result = deid_pipeline.fullAnnotate(text)
 
-&lt;span class=&quot;nf&quot;&gt;println&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;mask_entity&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;map&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;_&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;result&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;toString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;mkString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;))&lt;/span&gt;
-&lt;span class=&quot;nf&quot;&gt;println&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;obfuscated&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;map&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;_&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;result&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;toString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;mkString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;))&lt;/span&gt;
+println(deid_result(&quot;mask_entity&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;))
+println(deid_result(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;))
 
 
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;  &lt;/div&gt;
+```
 &lt;/div&gt;
 
-&lt;h2 id=&quot;results&quot;&gt;Results&lt;/h2&gt;
+## Results
+
+```bash
 
-&lt;div class=&quot;language-bash highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
 Masked with entity labels
-&lt;span class=&quot;nt&quot;&gt;------------------------------&lt;/span&gt;
-Dr. &amp;lt;DOCTOR&amp;gt;, from &amp;lt;HOSPITAL&amp;gt; &lt;span class=&quot;k&quot;&gt;in&lt;/span&gt; &amp;lt;CITY&amp;gt;,  attended to the patient on &amp;lt;DATE&amp;gt;.
-The patient’s medical record number is &amp;lt;MEDICALRECORD&amp;gt;
-patient, &amp;lt;PATIENT&amp;gt;, is &amp;lt;AGE&amp;gt; years old,  her Contact number: &amp;lt;PHONE&amp;gt; &lt;span class=&quot;nb&quot;&gt;.&lt;/span&gt;
+------------------------------
+Dr. &lt;DOCTOR&gt;, from &lt;HOSPITAL&gt; in &lt;CITY&gt;,  attended to the patient on &lt;DATE&gt;.
+The patient’s medical record number is &lt;MEDICALRECORD&gt;
+patient, &lt;PATIENT&gt;, is &lt;AGE&gt; years old,  her Contact number: &lt;PHONE&gt; .
 
 Obfuscated
-&lt;span class=&quot;nt&quot;&gt;------------------------------&lt;/span&gt;
-Dr. Edwardo Graft, from MCBRIDE ORTHOPEDIC HOSPITAL &lt;span class=&quot;k&quot;&gt;in &lt;/span&gt;CLAMART,  attended to the patient on 14/06/2024.
+------------------------------
+Dr. Edwardo Graft, from MCBRIDE ORTHOPEDIC HOSPITAL in CLAMART,  attended to the patient on 14/06/2024.
 The patient’s medical record number is 78295621.
-The patient, Nathaneil Bakes, is 43 years old,  her Contact number: 308-657-8469 &lt;span class=&quot;nb&quot;&gt;.&lt;/span&gt;
-
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;/div&gt;
-
-&lt;h2 class=&quot;model-param&quot; id=&quot;model-information&quot;&gt;Model Information&lt;/h2&gt;
-
-&lt;table class=&quot;table-model&quot;&gt;
-  &lt;tbody&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Model Name:&lt;/td&gt;
-      &lt;td&gt;clinical_deidentification_docwise_wip&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Type:&lt;/td&gt;
-      &lt;td&gt;pipeline&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Compatibility:&lt;/td&gt;
-      &lt;td&gt;Healthcare NLP 5.5.0+&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;License:&lt;/td&gt;
-      &lt;td&gt;Licensed&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Edition:&lt;/td&gt;
-      &lt;td&gt;Official&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Language:&lt;/td&gt;
-      &lt;td&gt;en&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Size:&lt;/td&gt;
-      &lt;td&gt;1.8 GB&lt;/td&gt;
-    &lt;/tr&gt;
-  &lt;/tbody&gt;
-&lt;/table&gt;
-
-&lt;h2 id=&quot;included-models&quot;&gt;Included Models&lt;/h2&gt;
-
-&lt;ul&gt;
-  &lt;li&gt;DocumentAssembler&lt;/li&gt;
-  &lt;li&gt;InternalDocumentSplitter&lt;/li&gt;
-  &lt;li&gt;TokenizerModel&lt;/li&gt;
-  &lt;li&gt;WordEmbeddingsModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;LightDeIdentification&lt;/li&gt;
-  &lt;li&gt;LightDeIdentification&lt;/li&gt;
-&lt;/ul&gt;</content><author><name>John Snow Labs</name></author><category term="deidentification" /><category term="deid" /><category term="en" /><category term="licensed" /><category term="clinical" /><category term="pipeline" /><category term="docwise" /><summary type="html">Description This pipeline can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate LOCATION, CONTACT, PROFESSION, NAME, DATE, AGE, MEDICALRECORD, ORGANIZATION, HEALTHPLAN, DOCTOR, USERNAME, LOCATION-OTHER, URL, DEVICE, CITY, ZIP, STATE, PATIENT, COUNTRY, STREET, PHONE, HOSPITAL, EMAIL, IDNUM, BIOID, FAX, LOCATION_OTHER, DLN, SSN, ACCOUNT, PLATE, VIN, LICENSE, IP entities. Predicted Entities ACCOUNT, AGE, CITY, CONTACT, COUNTRY, DATE, DLN, DOCTOR, EMAIL, EXCLUDED, HOSPITAL, ID, LICENSE, LOCATION, MEDICALRECORD, NAME, ORGANIZATION, PATIENT, PHONE, PLATE, PROFESSION, SSN, STATE, STREET, URL, USERNAME, VIN, ZIP Live Demo Open in Colab Download Copy S3 URI How to use PythonHealthcare NLPPythonJohnSnowLabsScalaNLU from sparknlp.pretrained import PretrainedPipeline deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.result for i in deid_result['mask_entity']])) print(''.join([i.result for i in deid_result['obfuscated']])) from sparknlp.pretrained import PretrainedPipeline deid_pipeline = nlp.PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.result for i in deid_result['mask_entity']])) print(''.join([i.result for i in deid_result['obfuscated']])) import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;) val text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; val deid_result = deid_pipeline.fullAnnotate(text) println(deid_result(&quot;mask_entity&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) println(deid_result(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) Results Masked with entity labels ------------------------------ Dr. &amp;lt;DOCTOR&amp;gt;, from &amp;lt;HOSPITAL&amp;gt; in &amp;lt;CITY&amp;gt;, attended to the patient on &amp;lt;DATE&amp;gt;. The patient’s medical record number is &amp;lt;MEDICALRECORD&amp;gt; patient, &amp;lt;PATIENT&amp;gt;, is &amp;lt;AGE&amp;gt; years old, her Contact number: &amp;lt;PHONE&amp;gt; . Obfuscated ------------------------------ Dr. Edwardo Graft, from MCBRIDE ORTHOPEDIC HOSPITAL in CLAMART, attended to the patient on 14/06/2024. The patient’s medical record number is 78295621. The patient, Nathaneil Bakes, is 43 years old, her Contact number: 308-657-8469 . Model Information Model Name: clinical_deidentification_docwise_wip Type: pipeline Compatibility: Healthcare NLP 5.5.0+ License: Licensed Edition: Official Language: en Size: 1.8 GB Included Models DocumentAssembler InternalDocumentSplitter TokenizerModel WordEmbeddingsModel MedicalNerModel NerConverterInternalModel MedicalNerModel MedicalNerModel MedicalNerModel NerConverterInternalModel NerConverterInternalModel NerConverterInternalModel ChunkMergeModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel TextMatcherInternalModel TextMatcherInternalModel TextMatcherInternalModel ContextualParserModel RegexMatcherInternalModel ContextualParserModel ContextualParserModel ContextualParserModel RegexMatcherInternalModel RegexMatcherInternalModel RegexMatcherInternalModel TextMatcherInternalModel ChunkMergeModel ChunkMergeModel LightDeIdentification LightDeIdentification</summary></entry><entry><title type="html">Clinical Deidentification Pipeline (Document Wise)</title><link href="/2024/11/29/clinical_deidentification_docwise_wip_v2_en.html" rel="alternate" type="text/html" title="Clinical Deidentification Pipeline (Document Wise)" /><published>2024-11-29T00:00:00+00:00</published><updated>2024-11-29T00:00:00+00:00</updated><id>/2024/11/29/clinical_deidentification_docwise_wip_v2_en</id><content type="html" xml:base="/2024/11/29/clinical_deidentification_docwise_wip_v2_en.html">&lt;h2 id=&quot;description&quot;&gt;Description&lt;/h2&gt;
-
-&lt;p&gt;This pipeline can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text.
-The pipeline can mask and obfuscate &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LOCATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CONTACT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PROFESSION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;NAME&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;AGE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;MEDICALRECORD&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ORGANIZATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;HEALTHPLAN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DOCTOR&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;USERNAME&lt;/code&gt;,
-&lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LOCATION-OTHER&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;URL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DEVICE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CITY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ZIP&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PATIENT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;COUNTRY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STREET&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PHONE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;HOSPITAL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;EMAIL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;IDNUM&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;BIOID&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;FAX&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LOCATION_OTHER&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DLN&lt;/code&gt;,
-&lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;SSN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ACCOUNT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PLATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;VIN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LICENSE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;IP&lt;/code&gt; entities.&lt;/p&gt;
-
-&lt;h2 id=&quot;predicted-entities&quot;&gt;Predicted Entities&lt;/h2&gt;
-
-&lt;p&gt;&lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;NAME&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;IDNUM&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ZIP&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;SSN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ACCOUNT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LICENSE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;AGE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PHONE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;COUNTRY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CITY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PLATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;VIN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;MEDICALRECORD&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;EMAIL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;URL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LOCATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PROFESSION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CONTACT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PATIENT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;HOSPITAL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ORGANIZATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STREET&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DOCTOR&lt;/code&gt;&lt;/p&gt;
-
-&lt;p class=&quot;btn-box&quot;&gt;&lt;button class=&quot;button button-orange&quot; disabled=&quot;&quot;&gt;Live Demo&lt;/button&gt;
-&lt;button class=&quot;button button-orange&quot; disabled=&quot;&quot;&gt;Open in Colab&lt;/button&gt;
-&lt;a href=&quot;https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_wip_v2_en_5.5.1_3.2_1732875915816.zip&quot; class=&quot;button button-orange button-orange-trans arr button-icon hidden&quot;&gt;Download&lt;/a&gt;
-&lt;a href=&quot;s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_wip_v2_en_5.5.1_3.2_1732875915816.zip&quot; class=&quot;button button-orange button-orange-trans button-icon button-copy-s3&quot;&gt;Copy S3 URI&lt;/a&gt;&lt;/p&gt;
-
-&lt;h2 id=&quot;how-to-use&quot;&gt;How to use&lt;/h2&gt;
-
-&lt;div class=&quot;tabs-box&quot;&gt;
-  &lt;div class=&quot;tabs-model-aproach-head&quot;&gt;&lt;button class=&quot;tab-li-model-aproach tabheader_active&quot;&gt;Python&lt;strong&gt;Healthcare NLP&lt;/strong&gt;&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach tab-li-jsl&quot;&gt;Python&lt;strong&gt;JohnSnowLabs&lt;/strong&gt;&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach&quot;&gt;Scala&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach tab-li-nlu&quot;&gt;NLU&lt;/button&gt;&lt;/div&gt;
-
-  &lt;div class=&quot;language-python highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-&lt;span class=&quot;kn&quot;&gt;from&lt;/span&gt; &lt;span class=&quot;nn&quot;&gt;sparknlp.pretrained&lt;/span&gt; &lt;span class=&quot;kn&quot;&gt;import&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;clinical_deidentification_docwise_wip_v2&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;en&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;clinical/models&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
+The patient, Nathaneil Bakes, is 43 years old,  her Contact number: 308-657-8469 .
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|clinical_deidentification_docwise_wip|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 5.5.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+|Size:|1.8 GB|
+
+## Included Models
+
+- DocumentAssembler
+- InternalDocumentSplitter
+- TokenizerModel
+- WordEmbeddingsModel
+- MedicalNerModel
+- NerConverterInternalModel
+- MedicalNerModel
+- MedicalNerModel
+- MedicalNerModel
+- NerConverterInternalModel
+- NerConverterInternalModel
+- NerConverterInternalModel
+- ChunkMergeModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- TextMatcherInternalModel
+- TextMatcherInternalModel
+- TextMatcherInternalModel
+- ContextualParserModel
+- RegexMatcherInternalModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- RegexMatcherInternalModel
+- RegexMatcherInternalModel
+- RegexMatcherInternalModel
+- TextMatcherInternalModel
+- ChunkMergeModel
+- ChunkMergeModel
+- LightDeIdentification
+- LightDeIdentification</content><author><name>John Snow Labs</name></author><category term="deidentification" /><category term="deid" /><category term="en" /><category term="licensed" /><category term="clinical" /><category term="pipeline" /><category term="docwise" /><summary type="html">Description This pipeline can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate LOCATION, CONTACT, PROFESSION, NAME, DATE, AGE, MEDICALRECORD, ORGANIZATION, HEALTHPLAN, DOCTOR, USERNAME, LOCATION-OTHER, URL, DEVICE, CITY, ZIP, STATE, PATIENT, COUNTRY, STREET, PHONE, HOSPITAL, EMAIL, IDNUM, BIOID, FAX, LOCATION_OTHER, DLN, SSN, ACCOUNT, PLATE, VIN, LICENSE, IP entities. Predicted Entities ACCOUNT, AGE, CITY, CONTACT, COUNTRY, DATE, DLN, DOCTOR, EMAIL, EXCLUDED, HOSPITAL, ID, LICENSE, LOCATION, MEDICALRECORD, NAME, ORGANIZATION, PATIENT, PHONE, PLATE, PROFESSION, SSN, STATE, STREET, URL, USERNAME, VIN, ZIP Live Demo Open in Colab Download Copy S3 URI How to use PythonHealthcare NLPPythonJohnSnowLabsScalaNLU from sparknlp.pretrained import PretrainedPipeline deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.result for i in deid_result['mask_entity']])) print(''.join([i.result for i in deid_result['obfuscated']])) from sparknlp.pretrained import PretrainedPipeline deid_pipeline = nlp.PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.result for i in deid_result['mask_entity']])) print(''.join([i.result for i in deid_result['obfuscated']])) import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;) val text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; val deid_result = deid_pipeline.fullAnnotate(text) println(deid_result(&quot;mask_entity&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) println(deid_result(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) Results Masked with entity labels ------------------------------ Dr. &amp;lt;DOCTOR&amp;gt;, from &amp;lt;HOSPITAL&amp;gt; in &amp;lt;CITY&amp;gt;, attended to the patient on &amp;lt;DATE&amp;gt;. The patient’s medical record number is &amp;lt;MEDICALRECORD&amp;gt; patient, &amp;lt;PATIENT&amp;gt;, is &amp;lt;AGE&amp;gt; years old, her Contact number: &amp;lt;PHONE&amp;gt; . Obfuscated ------------------------------ Dr. Edwardo Graft, from MCBRIDE ORTHOPEDIC HOSPITAL in CLAMART, attended to the patient on 14/06/2024. The patient’s medical record number is 78295621. The patient, Nathaneil Bakes, is 43 years old, her Contact number: 308-657-8469 . Model Information Model Name: clinical_deidentification_docwise_wip Type: pipeline Compatibility: Healthcare NLP 5.5.0+ License: Licensed Edition: Official Language: en Size: 1.8 GB Included Models DocumentAssembler InternalDocumentSplitter TokenizerModel WordEmbeddingsModel MedicalNerModel NerConverterInternalModel MedicalNerModel MedicalNerModel MedicalNerModel NerConverterInternalModel NerConverterInternalModel NerConverterInternalModel ChunkMergeModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel TextMatcherInternalModel TextMatcherInternalModel TextMatcherInternalModel ContextualParserModel RegexMatcherInternalModel ContextualParserModel ContextualParserModel ContextualParserModel RegexMatcherInternalModel RegexMatcherInternalModel RegexMatcherInternalModel TextMatcherInternalModel ChunkMergeModel ChunkMergeModel LightDeIdentification LightDeIdentification</summary></entry><entry><title type="html">Clinical Deidentification Pipeline (Document Wise)</title><link href="/2024/11/29/clinical_deidentification_docwise_wip_v2_en.html" rel="alternate" type="text/html" title="Clinical Deidentification Pipeline (Document Wise)" /><published>2024-11-29T00:00:00+00:00</published><updated>2024-11-29T00:00:00+00:00</updated><id>/2024/11/29/clinical_deidentification_docwise_wip_v2_en</id><content type="html" xml:base="/2024/11/29/clinical_deidentification_docwise_wip_v2_en.html">## Description
+
+This pipeline can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text.
+The pipeline can mask and obfuscate `LOCATION`, `CONTACT`, `PROFESSION`, `NAME`, `DATE`, `AGE`, `MEDICALRECORD`, `ORGANIZATION`, `HEALTHPLAN`, `DOCTOR`, `USERNAME`,
+`LOCATION-OTHER`, `URL`, `DEVICE`, `CITY`, `ZIP`, `STATE`, `PATIENT`, `COUNTRY`, `STREET`, `PHONE`, `HOSPITAL`, `EMAIL`, `IDNUM`, `BIOID`, `FAX`, `LOCATION_OTHER`, `DLN`,
+`SSN`, `ACCOUNT`, `PLATE`, `VIN`, `LICENSE`, `IP` entities.
+
+## Predicted Entities
+
+`NAME`, `DATE`, `IDNUM`, `ZIP`, `SSN`, `ACCOUNT`, `LICENSE`, `AGE`, `PHONE`, `COUNTRY`, `STATE`, `CITY`, `PLATE`, `VIN`, `MEDICALRECORD`, `EMAIL`, `URL`, `LOCATION`, `PROFESSION`, `CONTACT`, `PATIENT`, `HOSPITAL`, `ORGANIZATION`, `STREET`, `DOCTOR`
+
+{:.btn-box}
+&lt;button class=&quot;button button-orange&quot; disabled&gt;Live Demo&lt;/button&gt;
+&lt;button class=&quot;button button-orange&quot; disabled&gt;Open in Colab&lt;/button&gt;
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_wip_v2_en_5.5.1_3.2_1732875915816.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_wip_v2_en_5.5.1_3.2_1732875915816.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+&lt;div class=&quot;tabs-box&quot; markdown=&quot;1&quot;&gt;
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+  
+```python
+
+from sparknlp.pretrained import PretrainedPipeline
+
+deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip_v2&quot;, &quot;en&quot;, &quot;clinical/models&quot;)
+
+text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
 The patient’s medical record number is 56467890.
-The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;&lt;/span&gt;
+The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;
 
-&lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;fullAnnotate&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
+deid_result = deid_pipeline.fullAnnotate(text)
 
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'mask_entity'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'obfuscated'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
+print(''.join([i.result for i in deid_result['mask_entity']]))
+print(''.join([i.result for i in deid_result['obfuscated']]))
 
 
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;  &lt;/div&gt;
+```
 
-  &lt;div class=&quot;language-python jsl-block highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-&lt;span class=&quot;kn&quot;&gt;from&lt;/span&gt; &lt;span class=&quot;nn&quot;&gt;sparknlp.pretrained&lt;/span&gt; &lt;span class=&quot;kn&quot;&gt;import&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;
+{:.jsl-block}
+```python
 
-&lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;nlp&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;clinical_deidentification_docwise_wip_v2&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;en&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;clinical/models&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
+from sparknlp.pretrained import PretrainedPipeline
 
-&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
+deid_pipeline = nlp.PretrainedPipeline(&quot;clinical_deidentification_docwise_wip_v2&quot;, &quot;en&quot;, &quot;clinical/models&quot;)
+
+text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
 The patient’s medical record number is 56467890.
-The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;&lt;/span&gt;
+The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;
+
+deid_result = deid_pipeline.fullAnnotate(text)
 
-&lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;fullAnnotate&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
+print(''.join([i.result for i in deid_result['mask_entity']]))
+print(''.join([i.result for i in deid_result['obfuscated']]))
 
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'mask_entity'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'obfuscated'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
 
+```
+```scala
 
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;  &lt;/div&gt;
-  &lt;div class=&quot;language-scala highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-&lt;span class=&quot;k&quot;&gt;import&lt;/span&gt; &lt;span class=&quot;nn&quot;&gt;com.johnsnowlabs.nlp.pretrained.PretrainedPipeline&lt;/span&gt;
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_pipeline&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;nc&quot;&gt;PretrainedPipeline&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;clinical_deidentification_docwise_wip_v2&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;en&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;clinical/models&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
+val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip_v2&quot;, &quot;en&quot;, &quot;clinical/models&quot;)
 
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;text&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
+val text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
 The patient’s medical record number is 56467890.
-The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;&lt;/span&gt;
+The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;
 
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_pipeline&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;fullAnnotate&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
+val deid_result = deid_pipeline.fullAnnotate(text)
 
-&lt;span class=&quot;nf&quot;&gt;println&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;mask_entity&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;map&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;_&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;result&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;toString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;mkString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;))&lt;/span&gt;
-&lt;span class=&quot;nf&quot;&gt;println&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;obfuscated&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;map&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;_&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;result&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;toString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;mkString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;))&lt;/span&gt;
+println(deid_result(&quot;mask_entity&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;))
+println(deid_result(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;))
 
 
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;  &lt;/div&gt;
+```
 &lt;/div&gt;
 
-&lt;h2 id=&quot;results&quot;&gt;Results&lt;/h2&gt;
+## Results
+
+```bash
 
-&lt;div class=&quot;language-bash highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
 Masked with entity labels
-&lt;span class=&quot;nt&quot;&gt;------------------------------&lt;/span&gt;
-Dr. &amp;lt;DOCTOR&amp;gt;, from &amp;lt;HOSPITAL&amp;gt; &lt;span class=&quot;k&quot;&gt;in&lt;/span&gt; &amp;lt;CITY&amp;gt;,  attended to the patient on &amp;lt;DATE&amp;gt;.
-The patient’s medical record number is &amp;lt;MEDICALRECORD&amp;gt;
-patient, &amp;lt;PATIENT&amp;gt;, is &amp;lt;AGE&amp;gt; years old,  her Contact number: &amp;lt;PHONE&amp;gt; &lt;span class=&quot;nb&quot;&gt;.&lt;/span&gt;
+------------------------------
+Dr. &lt;DOCTOR&gt;, from &lt;HOSPITAL&gt; in &lt;CITY&gt;,  attended to the patient on &lt;DATE&gt;.
+The patient’s medical record number is &lt;MEDICALRECORD&gt;
+patient, &lt;PATIENT&gt;, is &lt;AGE&gt; years old,  her Contact number: &lt;PHONE&gt; .
 
 Obfuscated
-&lt;span class=&quot;nt&quot;&gt;------------------------------&lt;/span&gt;
-Dr. Edwardo Graft, from MCBRIDE ORTHOPEDIC HOSPITAL &lt;span class=&quot;k&quot;&gt;in &lt;/span&gt;CLAMART,  attended to the patient on 14/06/2024.
+------------------------------
+Dr. Edwardo Graft, from MCBRIDE ORTHOPEDIC HOSPITAL in CLAMART,  attended to the patient on 14/06/2024.
 The patient’s medical record number is 78295621.
-The patient, Nathaneil Bakes, is 43 years old,  her Contact number: 308-657-8469 &lt;span class=&quot;nb&quot;&gt;.&lt;/span&gt;
-
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;/div&gt;
-
-&lt;h2 class=&quot;model-param&quot; id=&quot;model-information&quot;&gt;Model Information&lt;/h2&gt;
-
-&lt;table class=&quot;table-model&quot;&gt;
-  &lt;tbody&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Model Name:&lt;/td&gt;
-      &lt;td&gt;clinical_deidentification_docwise_wip_v2&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Type:&lt;/td&gt;
-      &lt;td&gt;pipeline&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Compatibility:&lt;/td&gt;
-      &lt;td&gt;Healthcare NLP 5.5.1+&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;License:&lt;/td&gt;
-      &lt;td&gt;Licensed&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Edition:&lt;/td&gt;
-      &lt;td&gt;Official&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Language:&lt;/td&gt;
-      &lt;td&gt;en&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Size:&lt;/td&gt;
-      &lt;td&gt;2.5 GB&lt;/td&gt;
-    &lt;/tr&gt;
-  &lt;/tbody&gt;
-&lt;/table&gt;
-
-&lt;h2 id=&quot;included-models&quot;&gt;Included Models&lt;/h2&gt;
-
-&lt;ul&gt;
-  &lt;li&gt;DocumentAssembler&lt;/li&gt;
-  &lt;li&gt;InternalDocumentSplitter&lt;/li&gt;
-  &lt;li&gt;TokenizerModel&lt;/li&gt;
-  &lt;li&gt;WordEmbeddingsModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;PretrainedZeroShotNER&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;LightDeIdentification&lt;/li&gt;
-  &lt;li&gt;LightDeIdentification&lt;/li&gt;
-&lt;/ul&gt;</content><author><name>John Snow Labs</name></author><category term="deidentification" /><category term="deid" /><category term="en" /><category term="licensed" /><category term="clinical" /><category term="pipeline" /><category term="docwise" /><summary type="html">Description This pipeline can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate LOCATION, CONTACT, PROFESSION, NAME, DATE, AGE, MEDICALRECORD, ORGANIZATION, HEALTHPLAN, DOCTOR, USERNAME, LOCATION-OTHER, URL, DEVICE, CITY, ZIP, STATE, PATIENT, COUNTRY, STREET, PHONE, HOSPITAL, EMAIL, IDNUM, BIOID, FAX, LOCATION_OTHER, DLN, SSN, ACCOUNT, PLATE, VIN, LICENSE, IP entities. Predicted Entities NAME, DATE, IDNUM, ZIP, SSN, ACCOUNT, LICENSE, AGE, PHONE, COUNTRY, STATE, CITY, PLATE, VIN, MEDICALRECORD, EMAIL, URL, LOCATION, PROFESSION, CONTACT, PATIENT, HOSPITAL, ORGANIZATION, STREET, DOCTOR Live Demo Open in Colab Download Copy S3 URI How to use PythonHealthcare NLPPythonJohnSnowLabsScalaNLU from sparknlp.pretrained import PretrainedPipeline deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip_v2&quot;, &quot;en&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.result for i in deid_result['mask_entity']])) print(''.join([i.result for i in deid_result['obfuscated']])) from sparknlp.pretrained import PretrainedPipeline deid_pipeline = nlp.PretrainedPipeline(&quot;clinical_deidentification_docwise_wip_v2&quot;, &quot;en&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.result for i in deid_result['mask_entity']])) print(''.join([i.result for i in deid_result['obfuscated']])) import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip_v2&quot;, &quot;en&quot;, &quot;clinical/models&quot;) val text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; val deid_result = deid_pipeline.fullAnnotate(text) println(deid_result(&quot;mask_entity&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) println(deid_result(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) Results Masked with entity labels ------------------------------ Dr. &amp;lt;DOCTOR&amp;gt;, from &amp;lt;HOSPITAL&amp;gt; in &amp;lt;CITY&amp;gt;, attended to the patient on &amp;lt;DATE&amp;gt;. The patient’s medical record number is &amp;lt;MEDICALRECORD&amp;gt; patient, &amp;lt;PATIENT&amp;gt;, is &amp;lt;AGE&amp;gt; years old, her Contact number: &amp;lt;PHONE&amp;gt; . Obfuscated ------------------------------ Dr. Edwardo Graft, from MCBRIDE ORTHOPEDIC HOSPITAL in CLAMART, attended to the patient on 14/06/2024. The patient’s medical record number is 78295621. The patient, Nathaneil Bakes, is 43 years old, her Contact number: 308-657-8469 . Model Information Model Name: clinical_deidentification_docwise_wip_v2 Type: pipeline Compatibility: Healthcare NLP 5.5.1+ License: Licensed Edition: Official Language: en Size: 2.5 GB Included Models DocumentAssembler InternalDocumentSplitter TokenizerModel WordEmbeddingsModel MedicalNerModel NerConverterInternalModel MedicalNerModel MedicalNerModel MedicalNerModel NerConverterInternalModel NerConverterInternalModel NerConverterInternalModel PretrainedZeroShotNER NerConverterInternalModel ChunkMergeModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel TextMatcherInternalModel TextMatcherInternalModel TextMatcherInternalModel ContextualParserModel RegexMatcherInternalModel ContextualParserModel ContextualParserModel ContextualParserModel RegexMatcherInternalModel RegexMatcherInternalModel RegexMatcherInternalModel TextMatcherInternalModel ChunkMergeModel ChunkMergeModel LightDeIdentification LightDeIdentification</summary></entry><entry><title type="html">Clinical Deidentification Pipeline (Sentence Wise)</title><link href="/2024/11/29/clinical_deidentification_v2_wip_en.html" rel="alternate" type="text/html" title="Clinical Deidentification Pipeline (Sentence Wise)" /><published>2024-11-29T00:00:00+00:00</published><updated>2024-11-29T00:00:00+00:00</updated><id>/2024/11/29/clinical_deidentification_v2_wip_en</id><content type="html" xml:base="/2024/11/29/clinical_deidentification_v2_wip_en.html">&lt;h2 id=&quot;description&quot;&gt;Description&lt;/h2&gt;
-
-&lt;p&gt;This pipeline can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text.
-The pipeline can mask and obfuscate &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;MEDICALRECORD&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ORGANIZATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PROFESSION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;HEALTHPLAN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DOCTOR&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;USERNAME&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LOCATION-OTHER&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;URL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DEVICE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CITY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DATE&lt;/code&gt;,
-&lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ZIP&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PATIENT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;COUNTRY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STREET&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PHONE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;HOSPITAL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;EMAIL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;IDNUM&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;BIOID&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;FAX&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;AGE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LOCATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LOCATION_OTHER&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DLN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CONTACT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;NAME&lt;/code&gt;,
-&lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;SSN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ACCOUNT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PLATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;VIN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LICENSE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;IP&lt;/code&gt; entities.&lt;/p&gt;
-
-&lt;h2 id=&quot;predicted-entities&quot;&gt;Predicted Entities&lt;/h2&gt;
-
-&lt;p&gt;&lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;MEDICALRECORD&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ORGANIZATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PROFESSION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;HEALTHPLAN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DOCTOR&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;USERNAME&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LOCATION-OTHER&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;URL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DEVICE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CITY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DATE&lt;/code&gt;,
-&lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ZIP&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PATIENT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;COUNTRY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STREET&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PHONE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;HOSPITAL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;EMAIL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;IDNUM&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;BIOID&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;FAX&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;AGE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LOCATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LOCATION_OTHER&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DLN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CONTACT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;NAME&lt;/code&gt;,
-&lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;SSN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ACCOUNT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PLATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;VIN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LICENSE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;IP&lt;/code&gt;&lt;/p&gt;
-
-&lt;p class=&quot;btn-box&quot;&gt;&lt;button class=&quot;button button-orange&quot; disabled=&quot;&quot;&gt;Live Demo&lt;/button&gt;
-&lt;button class=&quot;button button-orange&quot; disabled=&quot;&quot;&gt;Open in Colab&lt;/button&gt;
-&lt;a href=&quot;https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_v2_wip_en_5.5.0_3.2_1732870686118.zip&quot; class=&quot;button button-orange button-orange-trans arr button-icon hidden&quot;&gt;Download&lt;/a&gt;
-&lt;a href=&quot;s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_v2_wip_en_5.5.0_3.2_1732870686118.zip&quot; class=&quot;button button-orange button-orange-trans button-icon button-copy-s3&quot;&gt;Copy S3 URI&lt;/a&gt;&lt;/p&gt;
-
-&lt;h2 id=&quot;how-to-use&quot;&gt;How to use&lt;/h2&gt;
-
-&lt;div class=&quot;tabs-box&quot;&gt;
-  &lt;div class=&quot;tabs-model-aproach-head&quot;&gt;&lt;button class=&quot;tab-li-model-aproach tabheader_active&quot;&gt;Python&lt;strong&gt;Healthcare NLP&lt;/strong&gt;&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach tab-li-jsl&quot;&gt;Python&lt;strong&gt;JohnSnowLabs&lt;/strong&gt;&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach&quot;&gt;Scala&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach tab-li-nlu&quot;&gt;NLU&lt;/button&gt;&lt;/div&gt;
-
-  &lt;div class=&quot;language-python highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-&lt;span class=&quot;kn&quot;&gt;from&lt;/span&gt; &lt;span class=&quot;nn&quot;&gt;sparknlp.pretrained&lt;/span&gt; &lt;span class=&quot;kn&quot;&gt;import&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;clinical_deidentification_v2_wip&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;en&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;clinical/models&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
+The patient, Nathaneil Bakes, is 43 years old,  her Contact number: 308-657-8469 .
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|clinical_deidentification_docwise_wip_v2|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 5.5.1+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+|Size:|2.5 GB|
+
+## Included Models
+
+- DocumentAssembler
+- InternalDocumentSplitter
+- TokenizerModel
+- WordEmbeddingsModel
+- MedicalNerModel
+- NerConverterInternalModel
+- MedicalNerModel
+- MedicalNerModel
+- MedicalNerModel
+- NerConverterInternalModel
+- NerConverterInternalModel
+- NerConverterInternalModel
+- PretrainedZeroShotNER
+- NerConverterInternalModel
+- ChunkMergeModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- TextMatcherInternalModel
+- TextMatcherInternalModel
+- TextMatcherInternalModel
+- ContextualParserModel
+- RegexMatcherInternalModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- RegexMatcherInternalModel
+- RegexMatcherInternalModel
+- RegexMatcherInternalModel
+- TextMatcherInternalModel
+- ChunkMergeModel
+- ChunkMergeModel
+- LightDeIdentification
+- LightDeIdentification</content><author><name>John Snow Labs</name></author><category term="deidentification" /><category term="deid" /><category term="en" /><category term="licensed" /><category term="clinical" /><category term="pipeline" /><category term="docwise" /><summary type="html">Description This pipeline can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate LOCATION, CONTACT, PROFESSION, NAME, DATE, AGE, MEDICALRECORD, ORGANIZATION, HEALTHPLAN, DOCTOR, USERNAME, LOCATION-OTHER, URL, DEVICE, CITY, ZIP, STATE, PATIENT, COUNTRY, STREET, PHONE, HOSPITAL, EMAIL, IDNUM, BIOID, FAX, LOCATION_OTHER, DLN, SSN, ACCOUNT, PLATE, VIN, LICENSE, IP entities. Predicted Entities NAME, DATE, IDNUM, ZIP, SSN, ACCOUNT, LICENSE, AGE, PHONE, COUNTRY, STATE, CITY, PLATE, VIN, MEDICALRECORD, EMAIL, URL, LOCATION, PROFESSION, CONTACT, PATIENT, HOSPITAL, ORGANIZATION, STREET, DOCTOR Live Demo Open in Colab Download Copy S3 URI How to use PythonHealthcare NLPPythonJohnSnowLabsScalaNLU from sparknlp.pretrained import PretrainedPipeline deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip_v2&quot;, &quot;en&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.result for i in deid_result['mask_entity']])) print(''.join([i.result for i in deid_result['obfuscated']])) from sparknlp.pretrained import PretrainedPipeline deid_pipeline = nlp.PretrainedPipeline(&quot;clinical_deidentification_docwise_wip_v2&quot;, &quot;en&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.result for i in deid_result['mask_entity']])) print(''.join([i.result for i in deid_result['obfuscated']])) import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip_v2&quot;, &quot;en&quot;, &quot;clinical/models&quot;) val text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; val deid_result = deid_pipeline.fullAnnotate(text) println(deid_result(&quot;mask_entity&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) println(deid_result(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) Results Masked with entity labels ------------------------------ Dr. &amp;lt;DOCTOR&amp;gt;, from &amp;lt;HOSPITAL&amp;gt; in &amp;lt;CITY&amp;gt;, attended to the patient on &amp;lt;DATE&amp;gt;. The patient’s medical record number is &amp;lt;MEDICALRECORD&amp;gt; patient, &amp;lt;PATIENT&amp;gt;, is &amp;lt;AGE&amp;gt; years old, her Contact number: &amp;lt;PHONE&amp;gt; . Obfuscated ------------------------------ Dr. Edwardo Graft, from MCBRIDE ORTHOPEDIC HOSPITAL in CLAMART, attended to the patient on 14/06/2024. The patient’s medical record number is 78295621. The patient, Nathaneil Bakes, is 43 years old, her Contact number: 308-657-8469 . Model Information Model Name: clinical_deidentification_docwise_wip_v2 Type: pipeline Compatibility: Healthcare NLP 5.5.1+ License: Licensed Edition: Official Language: en Size: 2.5 GB Included Models DocumentAssembler InternalDocumentSplitter TokenizerModel WordEmbeddingsModel MedicalNerModel NerConverterInternalModel MedicalNerModel MedicalNerModel MedicalNerModel NerConverterInternalModel NerConverterInternalModel NerConverterInternalModel PretrainedZeroShotNER NerConverterInternalModel ChunkMergeModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel TextMatcherInternalModel TextMatcherInternalModel TextMatcherInternalModel ContextualParserModel RegexMatcherInternalModel ContextualParserModel ContextualParserModel ContextualParserModel RegexMatcherInternalModel RegexMatcherInternalModel RegexMatcherInternalModel TextMatcherInternalModel ChunkMergeModel ChunkMergeModel LightDeIdentification LightDeIdentification</summary></entry><entry><title type="html">Clinical Deidentification Pipeline (Sentence Wise)</title><link href="/2024/11/29/clinical_deidentification_v2_wip_en.html" rel="alternate" type="text/html" title="Clinical Deidentification Pipeline (Sentence Wise)" /><published>2024-11-29T00:00:00+00:00</published><updated>2024-11-29T00:00:00+00:00</updated><id>/2024/11/29/clinical_deidentification_v2_wip_en</id><content type="html" xml:base="/2024/11/29/clinical_deidentification_v2_wip_en.html">## Description
+
+This pipeline can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text.
+The pipeline can mask and obfuscate `MEDICALRECORD`, `ORGANIZATION`, `PROFESSION`, `HEALTHPLAN`, `DOCTOR`, `USERNAME`, `LOCATION-OTHER`, `URL`, `DEVICE`, `CITY`, `DATE`,
+`ZIP`, `STATE`, `PATIENT`, `COUNTRY`, `STREET`, `PHONE`, `HOSPITAL`, `EMAIL`, `IDNUM`, `BIOID`, `FAX`, `AGE`, `LOCATION`, `LOCATION_OTHER`, `DLN`, `CONTACT`, `NAME`,
+`SSN`, `ACCOUNT`, `PLATE`, `VIN`, `LICENSE`, `IP` entities.
+
+## Predicted Entities
+
+`MEDICALRECORD`, `ORGANIZATION`, `PROFESSION`, `HEALTHPLAN`, `DOCTOR`, `USERNAME`, `LOCATION-OTHER`, `URL`, `DEVICE`, `CITY`, `DATE`,
+`ZIP`, `STATE`, `PATIENT`, `COUNTRY`, `STREET`, `PHONE`, `HOSPITAL`, `EMAIL`, `IDNUM`, `BIOID`, `FAX`, `AGE`, `LOCATION`, `LOCATION_OTHER`, `DLN`, `CONTACT`, `NAME`,
+`SSN`, `ACCOUNT`, `PLATE`, `VIN`, `LICENSE`, `IP`
+
+{:.btn-box}
+&lt;button class=&quot;button button-orange&quot; disabled&gt;Live Demo&lt;/button&gt;
+&lt;button class=&quot;button button-orange&quot; disabled&gt;Open in Colab&lt;/button&gt;
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_v2_wip_en_5.5.0_3.2_1732870686118.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_v2_wip_en_5.5.0_3.2_1732870686118.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+&lt;div class=&quot;tabs-box&quot; markdown=&quot;1&quot;&gt;
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+  
+```python
+
+from sparknlp.pretrained import PretrainedPipeline
+
+deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_v2_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;)
+
+text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
 The patient’s medical record number is 56467890.
-The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;&lt;/span&gt;
+The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;
+
+deid_result = deid_pipeline.fullAnnotate(text)
 
-&lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;fullAnnotate&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
+print(''.join([i.metadata['masked'] for i in deid_result['obfuscated']]))
+print(''.join([i.result for i in deid_result['obfuscated']]))
 
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;metadata&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'masked'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'obfuscated'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'obfuscated'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
 
+```
 
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;  &lt;/div&gt;
+{:.jsl-block}
+```python
 
-  &lt;div class=&quot;language-python jsl-block highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-&lt;span class=&quot;kn&quot;&gt;from&lt;/span&gt; &lt;span class=&quot;nn&quot;&gt;sparknlp.pretrained&lt;/span&gt; &lt;span class=&quot;kn&quot;&gt;import&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;
+from sparknlp.pretrained import PretrainedPipeline
 
-&lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;nlp&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;clinical_deidentification_v2_wip&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;en&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;clinical/models&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
+deid_pipeline = nlp.PretrainedPipeline(&quot;clinical_deidentification_v2_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;)
 
-&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
+text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
 The patient’s medical record number is 56467890.
-The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;&lt;/span&gt;
+The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;
 
-&lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;fullAnnotate&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
+deid_result = deid_pipeline.fullAnnotate(text)
 
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;metadata&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'masked'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'obfuscated'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'obfuscated'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
+print(''.join([i.metadata['masked'] for i in deid_result['obfuscated']]))
+print(''.join([i.result for i in deid_result['obfuscated']]))
 
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;  &lt;/div&gt;
-  &lt;div class=&quot;language-scala highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-&lt;span class=&quot;k&quot;&gt;import&lt;/span&gt; &lt;span class=&quot;nn&quot;&gt;com.johnsnowlabs.nlp.pretrained.PretrainedPipeline&lt;/span&gt;
+```
+```scala
 
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_pipeline&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;nc&quot;&gt;PretrainedPipeline&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;clinical_deidentification_v2_wip&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;en&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;clinical/models&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;text&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
+val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_v2_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;)
+
+val text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
 The patient’s medical record number is 56467890.
-The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;&lt;/span&gt;
+The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;
 
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_pipeline&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;fullAnnotate&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
+val deid_result = deid_pipeline.fullAnnotate(text)
 
-&lt;span class=&quot;nf&quot;&gt;println&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;obfuscated&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;map&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;_&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;metadata&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;masked&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;toString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;mkString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;))&lt;/span&gt;
-&lt;span class=&quot;nf&quot;&gt;println&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;obfuscated&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;map&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;_&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;result&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;toString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;mkString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;))&lt;/span&gt;
+println(deid_result(&quot;obfuscated&quot;).map(_(&quot;metadata&quot;)(&quot;masked&quot;).toString).mkString(&quot;&quot;))
+println(deid_result(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;))
 
 
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;  &lt;/div&gt;
+```
 &lt;/div&gt;
 
-&lt;h2 id=&quot;results&quot;&gt;Results&lt;/h2&gt;
+## Results
 
-&lt;div class=&quot;language-bash highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;Masked with entity labels
-&lt;span class=&quot;nt&quot;&gt;------------------------------&lt;/span&gt;
-Dr. &amp;lt;DOCTOR&amp;gt;, from &amp;lt;HOSPITAL&amp;gt; &lt;span class=&quot;k&quot;&gt;in&lt;/span&gt; &amp;lt;CITY&amp;gt;,  attended to the patient on &amp;lt;DATE&amp;gt;.
-The patient’s medical record number is &amp;lt;MEDICALRECORD&amp;gt;.
-The patient, &amp;lt;PATIENT&amp;gt;, is &amp;lt;AGE&amp;gt; years old,  her Contact number: &amp;lt;PHONE&amp;gt; &lt;span class=&quot;nb&quot;&gt;.&lt;/span&gt;
+```bash
+Masked with entity labels
+------------------------------
+Dr. &lt;DOCTOR&gt;, from &lt;HOSPITAL&gt; in &lt;CITY&gt;,  attended to the patient on &lt;DATE&gt;.
+The patient’s medical record number is &lt;MEDICALRECORD&gt;.
+The patient, &lt;PATIENT&gt;, is &lt;AGE&gt; years old,  her Contact number: &lt;PHONE&gt; .
 
 Obfuscated
-&lt;span class=&quot;nt&quot;&gt;------------------------------&lt;/span&gt;
-Dr. Alissa Irving, from KINDRED HOSPITAL SEATTLE &lt;span class=&quot;k&quot;&gt;in &lt;/span&gt;Geleen,  attended to the patient on 22/06/2024.
+------------------------------
+Dr. Alissa Irving, from KINDRED HOSPITAL SEATTLE in Geleen,  attended to the patient on 22/06/2024.
 The patient’s medical record number is 16109604.
-The patient, Burnette Carte, is 49 years old,  her Contact number: 540-981-1914 &lt;span class=&quot;nb&quot;&gt;.&lt;/span&gt;
-
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;/div&gt;
-
-&lt;h2 class=&quot;model-param&quot; id=&quot;model-information&quot;&gt;Model Information&lt;/h2&gt;
-
-&lt;table class=&quot;table-model&quot;&gt;
-  &lt;tbody&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Model Name:&lt;/td&gt;
-      &lt;td&gt;clinical_deidentification_v2_wip&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Type:&lt;/td&gt;
-      &lt;td&gt;pipeline&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Compatibility:&lt;/td&gt;
-      &lt;td&gt;Healthcare NLP 5.5.0+&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;License:&lt;/td&gt;
-      &lt;td&gt;Licensed&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Edition:&lt;/td&gt;
-      &lt;td&gt;Official&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Language:&lt;/td&gt;
-      &lt;td&gt;en&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Size:&lt;/td&gt;
-      &lt;td&gt;1.8 GB&lt;/td&gt;
-    &lt;/tr&gt;
-  &lt;/tbody&gt;
-&lt;/table&gt;
-
-&lt;h2 id=&quot;included-models&quot;&gt;Included Models&lt;/h2&gt;
-
-&lt;ul&gt;
-  &lt;li&gt;DocumentAssembler&lt;/li&gt;
-  &lt;li&gt;SentenceDetectorDLModel&lt;/li&gt;
-  &lt;li&gt;TokenizerModel&lt;/li&gt;
-  &lt;li&gt;WordEmbeddingsModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;DeIdentificationModel&lt;/li&gt;
-  &lt;li&gt;Finisher&lt;/li&gt;
-&lt;/ul&gt;</content><author><name>John Snow Labs</name></author><category term="deidentification" /><category term="deid" /><category term="en" /><category term="licensed" /><category term="clinical" /><category term="pipeline" /><category term="sent_wise" /><summary type="html">Description This pipeline can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate MEDICALRECORD, ORGANIZATION, PROFESSION, HEALTHPLAN, DOCTOR, USERNAME, LOCATION-OTHER, URL, DEVICE, CITY, DATE, ZIP, STATE, PATIENT, COUNTRY, STREET, PHONE, HOSPITAL, EMAIL, IDNUM, BIOID, FAX, AGE, LOCATION, LOCATION_OTHER, DLN, CONTACT, NAME, SSN, ACCOUNT, PLATE, VIN, LICENSE, IP entities. Predicted Entities MEDICALRECORD, ORGANIZATION, PROFESSION, HEALTHPLAN, DOCTOR, USERNAME, LOCATION-OTHER, URL, DEVICE, CITY, DATE, ZIP, STATE, PATIENT, COUNTRY, STREET, PHONE, HOSPITAL, EMAIL, IDNUM, BIOID, FAX, AGE, LOCATION, LOCATION_OTHER, DLN, CONTACT, NAME, SSN, ACCOUNT, PLATE, VIN, LICENSE, IP Live Demo Open in Colab Download Copy S3 URI How to use PythonHealthcare NLPPythonJohnSnowLabsScalaNLU from sparknlp.pretrained import PretrainedPipeline deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_v2_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.metadata['masked'] for i in deid_result['obfuscated']])) print(''.join([i.result for i in deid_result['obfuscated']])) from sparknlp.pretrained import PretrainedPipeline deid_pipeline = nlp.PretrainedPipeline(&quot;clinical_deidentification_v2_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.metadata['masked'] for i in deid_result['obfuscated']])) print(''.join([i.result for i in deid_result['obfuscated']])) import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_v2_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;) val text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; val deid_result = deid_pipeline.fullAnnotate(text) println(deid_result(&quot;obfuscated&quot;).map(_(&quot;metadata&quot;)(&quot;masked&quot;).toString).mkString(&quot;&quot;)) println(deid_result(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) Results Masked with entity labels ------------------------------ Dr. &amp;lt;DOCTOR&amp;gt;, from &amp;lt;HOSPITAL&amp;gt; in &amp;lt;CITY&amp;gt;, attended to the patient on &amp;lt;DATE&amp;gt;. The patient’s medical record number is &amp;lt;MEDICALRECORD&amp;gt;. The patient, &amp;lt;PATIENT&amp;gt;, is &amp;lt;AGE&amp;gt; years old, her Contact number: &amp;lt;PHONE&amp;gt; . Obfuscated ------------------------------ Dr. Alissa Irving, from KINDRED HOSPITAL SEATTLE in Geleen, attended to the patient on 22/06/2024. The patient’s medical record number is 16109604. The patient, Burnette Carte, is 49 years old, her Contact number: 540-981-1914 . Model Information Model Name: clinical_deidentification_v2_wip Type: pipeline Compatibility: Healthcare NLP 5.5.0+ License: Licensed Edition: Official Language: en Size: 1.8 GB Included Models DocumentAssembler SentenceDetectorDLModel TokenizerModel WordEmbeddingsModel MedicalNerModel NerConverterInternalModel MedicalNerModel NerConverterInternalModel MedicalNerModel NerConverterInternalModel MedicalNerModel NerConverterInternalModel ChunkMergeModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel RegexMatcherInternalModel ContextualParserModel TextMatcherInternalModel TextMatcherInternalModel TextMatcherInternalModel ContextualParserModel RegexMatcherInternalModel RegexMatcherInternalModel RegexMatcherInternalModel ChunkMergeModel ChunkMergeModel DeIdentificationModel Finisher</summary></entry><entry><title type="html">Clinical Deidentification Pipeline (German, Document Wise)</title><link href="/2024/11/29/clinical_deidentification_docwise_large_wip_de.html" rel="alternate" type="text/html" title="Clinical Deidentification Pipeline (German, Document Wise)" /><published>2024-11-29T00:00:00+00:00</published><updated>2024-11-29T00:00:00+00:00</updated><id>/2024/11/29/clinical_deidentification_docwise_large_wip_de</id><content type="html" xml:base="/2024/11/29/clinical_deidentification_docwise_large_wip_de.html">&lt;h2 id=&quot;description&quot;&gt;Description&lt;/h2&gt;
-
-&lt;p&gt;This pipeline can be used to deidentify PHI information from medical texts in German language. The PHI information will be masked and obfuscated in the resulting text.
-The pipeline can mask and obfuscate: &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LOCATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;NAME&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ID&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;AGE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PROFESSION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CONTACT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ORGANIZATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DOCTOR&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CITY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;COUNTRY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STREET&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PATIENT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PHONE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;HOSPITAL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DLN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;SSN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ZIP&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ACCOUNT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LICENSE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PLATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;VIN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;MEDICALRECORD&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;EMAIL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;URL&lt;/code&gt; entities.&lt;/p&gt;
-
-&lt;p class=&quot;btn-box&quot;&gt;&lt;button class=&quot;button button-orange&quot; disabled=&quot;&quot;&gt;Live Demo&lt;/button&gt;
-&lt;button class=&quot;button button-orange&quot; disabled=&quot;&quot;&gt;Open in Colab&lt;/button&gt;
-&lt;a href=&quot;https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_large_wip_de_5.5.1_3.0_1732886192966.zip&quot; class=&quot;button button-orange button-orange-trans arr button-icon hidden&quot;&gt;Download&lt;/a&gt;
-&lt;a href=&quot;s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_large_wip_de_5.5.1_3.0_1732886192966.zip&quot; class=&quot;button button-orange button-orange-trans button-icon button-copy-s3&quot;&gt;Copy S3 URI&lt;/a&gt;&lt;/p&gt;
-
-&lt;h2 id=&quot;how-to-use&quot;&gt;How to use&lt;/h2&gt;
-
-&lt;div class=&quot;tabs-box&quot;&gt;
-  &lt;div class=&quot;tabs-model-aproach-head&quot;&gt;&lt;button class=&quot;tab-li-model-aproach tabheader_active&quot;&gt;Python&lt;strong&gt;Healthcare NLP&lt;/strong&gt;&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach tab-li-jsl&quot;&gt;Python&lt;strong&gt;JohnSnowLabs&lt;/strong&gt;&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach&quot;&gt;Scala&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach tab-li-nlu&quot;&gt;NLU&lt;/button&gt;&lt;/div&gt;
-  &lt;div class=&quot;language-python highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-
-&lt;span class=&quot;kn&quot;&gt;from&lt;/span&gt; &lt;span class=&quot;nn&quot;&gt;sparknlp.pretrained&lt;/span&gt; &lt;span class=&quot;kn&quot;&gt;import&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;clinical_deidentification_docwise_large_wip&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;de&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;clinical/models&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot;&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;fullAnnotate&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;mi&quot;&gt;0&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;][&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'document'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;mi&quot;&gt;0&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;][&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'masked'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;mi&quot;&gt;0&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;][&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'obfuscated'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
-
-
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;  &lt;/div&gt;
-  &lt;div class=&quot;language-scala highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-
-&lt;span class=&quot;k&quot;&gt;import&lt;/span&gt; &lt;span class=&quot;nn&quot;&gt;com.johnsnowlabs.nlp.pretrained.PretrainedPipeline&lt;/span&gt;
-
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_pipeline&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;nc&quot;&gt;PretrainedPipeline&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;clinical_deidentification_docwise_large_wip&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;de&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;clinical/models&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;text&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot;&lt;/span&gt;
-
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_pipeline&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;fullAnnotate&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;nf&quot;&gt;println&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;mi&quot;&gt;0&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;document&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;map&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;_&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;result&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;toString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;mkString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;))&lt;/span&gt;
-&lt;span class=&quot;nf&quot;&gt;println&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;mi&quot;&gt;0&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;masked&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;map&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;_&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;result&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;toString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;mkString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;))&lt;/span&gt;
-&lt;span class=&quot;nf&quot;&gt;println&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;mi&quot;&gt;0&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;obfuscated&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;map&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;_&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;result&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;toString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;mkString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;))&lt;/span&gt;
-
-
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;  &lt;/div&gt;
+The patient, Burnette Carte, is 49 years old,  her Contact number: 540-981-1914 .
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|clinical_deidentification_v2_wip|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 5.5.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+|Size:|1.8 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetectorDLModel
+- TokenizerModel
+- WordEmbeddingsModel
+- MedicalNerModel
+- NerConverterInternalModel
+- MedicalNerModel
+- NerConverterInternalModel
+- MedicalNerModel
+- NerConverterInternalModel
+- MedicalNerModel
+- NerConverterInternalModel
+- ChunkMergeModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- RegexMatcherInternalModel
+- ContextualParserModel
+- TextMatcherInternalModel
+- TextMatcherInternalModel
+- TextMatcherInternalModel
+- ContextualParserModel
+- RegexMatcherInternalModel
+- RegexMatcherInternalModel
+- RegexMatcherInternalModel
+- ChunkMergeModel
+- ChunkMergeModel
+- DeIdentificationModel
+- Finisher</content><author><name>John Snow Labs</name></author><category term="deidentification" /><category term="deid" /><category term="en" /><category term="licensed" /><category term="clinical" /><category term="pipeline" /><category term="sent_wise" /><summary type="html">Description This pipeline can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate MEDICALRECORD, ORGANIZATION, PROFESSION, HEALTHPLAN, DOCTOR, USERNAME, LOCATION-OTHER, URL, DEVICE, CITY, DATE, ZIP, STATE, PATIENT, COUNTRY, STREET, PHONE, HOSPITAL, EMAIL, IDNUM, BIOID, FAX, AGE, LOCATION, LOCATION_OTHER, DLN, CONTACT, NAME, SSN, ACCOUNT, PLATE, VIN, LICENSE, IP entities. Predicted Entities MEDICALRECORD, ORGANIZATION, PROFESSION, HEALTHPLAN, DOCTOR, USERNAME, LOCATION-OTHER, URL, DEVICE, CITY, DATE, ZIP, STATE, PATIENT, COUNTRY, STREET, PHONE, HOSPITAL, EMAIL, IDNUM, BIOID, FAX, AGE, LOCATION, LOCATION_OTHER, DLN, CONTACT, NAME, SSN, ACCOUNT, PLATE, VIN, LICENSE, IP Live Demo Open in Colab Download Copy S3 URI How to use PythonHealthcare NLPPythonJohnSnowLabsScalaNLU from sparknlp.pretrained import PretrainedPipeline deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_v2_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.metadata['masked'] for i in deid_result['obfuscated']])) print(''.join([i.result for i in deid_result['obfuscated']])) from sparknlp.pretrained import PretrainedPipeline deid_pipeline = nlp.PretrainedPipeline(&quot;clinical_deidentification_v2_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.metadata['masked'] for i in deid_result['obfuscated']])) print(''.join([i.result for i in deid_result['obfuscated']])) import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_v2_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;) val text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; val deid_result = deid_pipeline.fullAnnotate(text) println(deid_result(&quot;obfuscated&quot;).map(_(&quot;metadata&quot;)(&quot;masked&quot;).toString).mkString(&quot;&quot;)) println(deid_result(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) Results Masked with entity labels ------------------------------ Dr. &amp;lt;DOCTOR&amp;gt;, from &amp;lt;HOSPITAL&amp;gt; in &amp;lt;CITY&amp;gt;, attended to the patient on &amp;lt;DATE&amp;gt;. The patient’s medical record number is &amp;lt;MEDICALRECORD&amp;gt;. The patient, &amp;lt;PATIENT&amp;gt;, is &amp;lt;AGE&amp;gt; years old, her Contact number: &amp;lt;PHONE&amp;gt; . Obfuscated ------------------------------ Dr. Alissa Irving, from KINDRED HOSPITAL SEATTLE in Geleen, attended to the patient on 22/06/2024. The patient’s medical record number is 16109604. The patient, Burnette Carte, is 49 years old, her Contact number: 540-981-1914 . Model Information Model Name: clinical_deidentification_v2_wip Type: pipeline Compatibility: Healthcare NLP 5.5.0+ License: Licensed Edition: Official Language: en Size: 1.8 GB Included Models DocumentAssembler SentenceDetectorDLModel TokenizerModel WordEmbeddingsModel MedicalNerModel NerConverterInternalModel MedicalNerModel NerConverterInternalModel MedicalNerModel NerConverterInternalModel MedicalNerModel NerConverterInternalModel ChunkMergeModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel RegexMatcherInternalModel ContextualParserModel TextMatcherInternalModel TextMatcherInternalModel TextMatcherInternalModel ContextualParserModel RegexMatcherInternalModel RegexMatcherInternalModel RegexMatcherInternalModel ChunkMergeModel ChunkMergeModel DeIdentificationModel Finisher</summary></entry><entry><title type="html">Clinical Deidentification Pipeline (German, Document Wise)</title><link href="/2024/11/29/clinical_deidentification_docwise_large_wip_de.html" rel="alternate" type="text/html" title="Clinical Deidentification Pipeline (German, Document Wise)" /><published>2024-11-29T00:00:00+00:00</published><updated>2024-11-29T00:00:00+00:00</updated><id>/2024/11/29/clinical_deidentification_docwise_large_wip_de</id><content type="html" xml:base="/2024/11/29/clinical_deidentification_docwise_large_wip_de.html">## Description
+
+This pipeline can be used to deidentify PHI information from medical texts in German language. The PHI information will be masked and obfuscated in the resulting text.
+The pipeline can mask and obfuscate: `LOCATION`, `DATE`, `NAME`, `ID`, `AGE`, `PROFESSION`, `CONTACT`, `ORGANIZATION`, `DOCTOR`, `CITY`, `COUNTRY`, `STREET`, `PATIENT`, `PHONE`, `HOSPITAL`, `STATE`, `DLN`, `SSN`, `ZIP`, `ACCOUNT`, `LICENSE`, `PLATE`, `VIN`, `MEDICALRECORD`, `EMAIL`, `URL` entities.
+
+{:.btn-box}
+&lt;button class=&quot;button button-orange&quot; disabled&gt;Live Demo&lt;/button&gt;
+&lt;button class=&quot;button button-orange&quot; disabled&gt;Open in Colab&lt;/button&gt;
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_large_wip_de_5.5.1_3.0_1732886192966.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_large_wip_de_5.5.1_3.0_1732886192966.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+&lt;div class=&quot;tabs-box&quot; markdown=&quot;1&quot;&gt;
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+
+from sparknlp.pretrained import PretrainedPipeline
+
+deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_large_wip&quot;, &quot;de&quot;, &quot;clinical/models&quot;)
+
+text = &quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot;
+
+deid_result = deid_pipeline.fullAnnotate(text)
+
+print(''.join([i.result for i in deid_result[0]['document']]))
+print(''.join([i.result for i in deid_result[0]['masked']]))
+print(''.join([i.result for i in deid_result[0]['obfuscated']]))
+
+
+```
+```scala
+
+
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_large_wip&quot;, &quot;de&quot;, &quot;clinical/models&quot;)
+
+val text = &quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot;
+
+val deid_result = deid_pipeline.fullAnnotate(text)
+
+println(deid_result(0)(&quot;document&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;))
+println(deid_result(0)(&quot;masked&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;))
+println(deid_result(0)(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;))
+
+
+```
 &lt;/div&gt;
 
-&lt;h2 id=&quot;results&quot;&gt;Results&lt;/h2&gt;
-
-&lt;div class=&quot;language-bash highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt &lt;span class=&quot;k&quot;&gt;in &lt;/span&gt;unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.
-&lt;span class=&quot;nt&quot;&gt;-------------MASKED------------&lt;/span&gt;
-Sehr geehrter Herr &amp;lt;NAME&amp;gt;, bezüglich Ihrer Anfrage vom &amp;lt;DATE&amp;gt; für Ihre Krankenakte &amp;lt;MEDICALRECORD&amp;gt; möchte ich bestätigen, dass Ihre Sozialversicherungsnummer &amp;lt;SSN&amp;gt; und Ihre Versichertennummer &amp;lt;ID&amp;gt; korrekt &lt;span class=&quot;k&quot;&gt;in &lt;/span&gt;unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen &amp;lt;PLATE&amp;gt; ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter &amp;lt;PHONE&amp;gt; oder schreiben Sie an &amp;lt;EMAIL&amp;gt;. Unsere Details finden Sie auch unter &amp;lt;URL&amp;gt;. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto &amp;lt;ACCOUNT&amp;gt;.
-&lt;span class=&quot;nt&quot;&gt;-------------OBFUSCATED-----------&lt;/span&gt;
-Sehr geehrter Herr Berta, bezüglich Ihrer Anfrage vom 22.03.2024 für Ihre Krankenakte 816698 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 91990385M363und Ihre Versichertennummer B6556697P9R korrekt &lt;span class=&quot;k&quot;&gt;in &lt;/span&gt;unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen N-RO 4769 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +70 10 25476981 oder schreiben Sie an Flaminia@google.com. Unsere Details finden Sie auch unter http://www.duran-brown.com/. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto XY21 5948 4488 4756 4354 44.
-
-
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;/div&gt;
-
-&lt;h2 class=&quot;model-param&quot; id=&quot;model-information&quot;&gt;Model Information&lt;/h2&gt;
-
-&lt;table class=&quot;table-model&quot;&gt;
-  &lt;tbody&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Model Name:&lt;/td&gt;
-      &lt;td&gt;clinical_deidentification_docwise_large_wip&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Type:&lt;/td&gt;
-      &lt;td&gt;pipeline&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Compatibility:&lt;/td&gt;
-      &lt;td&gt;Healthcare NLP 5.5.1+&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;License:&lt;/td&gt;
-      &lt;td&gt;Licensed&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Edition:&lt;/td&gt;
-      &lt;td&gt;Official&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Language:&lt;/td&gt;
-      &lt;td&gt;de&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Size:&lt;/td&gt;
-      &lt;td&gt;2.9 GB&lt;/td&gt;
-    &lt;/tr&gt;
-  &lt;/tbody&gt;
-&lt;/table&gt;
-
-&lt;h2 id=&quot;included-models&quot;&gt;Included Models&lt;/h2&gt;
-
-&lt;ul&gt;
-  &lt;li&gt;DocumentAssembler&lt;/li&gt;
-  &lt;li&gt;InternalDocumentSplitter&lt;/li&gt;
-  &lt;li&gt;TokenizerModel&lt;/li&gt;
-  &lt;li&gt;WordEmbeddingsModel&lt;/li&gt;
-  &lt;li&gt;PretrainedZeroShotNER&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ChunkFilterer&lt;/li&gt;
-  &lt;li&gt;LightDeIdentification&lt;/li&gt;
-  &lt;li&gt;LightDeIdentification&lt;/li&gt;
-&lt;/ul&gt;</content><author><name>John Snow Labs</name></author><category term="deidentification" /><category term="deid" /><category term="de" /><category term="licensed" /><category term="clinical" /><category term="pipeline" /><category term="docwise" /><category term="zero_shot" /><summary type="html">Description This pipeline can be used to deidentify PHI information from medical texts in German language. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate: LOCATION, DATE, NAME, ID, AGE, PROFESSION, CONTACT, ORGANIZATION, DOCTOR, CITY, COUNTRY, STREET, PATIENT, PHONE, HOSPITAL, STATE, DLN, SSN, ZIP, ACCOUNT, LICENSE, PLATE, VIN, MEDICALRECORD, EMAIL, URL entities. Live Demo Open in Colab Download Copy S3 URI How to use PythonHealthcare NLPPythonJohnSnowLabsScalaNLU from sparknlp.pretrained import PretrainedPipeline deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_large_wip&quot;, &quot;de&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.result for i in deid_result[0]['document']])) print(''.join([i.result for i in deid_result[0]['masked']])) print(''.join([i.result for i in deid_result[0]['obfuscated']])) import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_large_wip&quot;, &quot;de&quot;, &quot;clinical/models&quot;) val text = &quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot; val deid_result = deid_pipeline.fullAnnotate(text) println(deid_result(0)(&quot;document&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) println(deid_result(0)(&quot;masked&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) println(deid_result(0)(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) Results Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00. -------------MASKED------------ Sehr geehrter Herr &amp;lt;NAME&amp;gt;, bezüglich Ihrer Anfrage vom &amp;lt;DATE&amp;gt; für Ihre Krankenakte &amp;lt;MEDICALRECORD&amp;gt; möchte ich bestätigen, dass Ihre Sozialversicherungsnummer &amp;lt;SSN&amp;gt; und Ihre Versichertennummer &amp;lt;ID&amp;gt; korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen &amp;lt;PLATE&amp;gt; ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter &amp;lt;PHONE&amp;gt; oder schreiben Sie an &amp;lt;EMAIL&amp;gt;. Unsere Details finden Sie auch unter &amp;lt;URL&amp;gt;. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto &amp;lt;ACCOUNT&amp;gt;. -------------OBFUSCATED----------- Sehr geehrter Herr Berta, bezüglich Ihrer Anfrage vom 22.03.2024 für Ihre Krankenakte 816698 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 91990385M363und Ihre Versichertennummer B6556697P9R korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen N-RO 4769 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +70 10 25476981 oder schreiben Sie an Flaminia@google.com. Unsere Details finden Sie auch unter http://www.duran-brown.com/. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto XY21 5948 4488 4756 4354 44. Model Information Model Name: clinical_deidentification_docwise_large_wip Type: pipeline Compatibility: Healthcare NLP 5.5.1+ License: Licensed Edition: Official Language: de Size: 2.9 GB Included Models DocumentAssembler InternalDocumentSplitter TokenizerModel WordEmbeddingsModel PretrainedZeroShotNER NerConverterInternalModel MedicalNerModel NerConverterInternalModel MedicalNerModel NerConverterInternalModel RegexMatcherInternalModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel RegexMatcherInternalModel RegexMatcherInternalModel TextMatcherInternalModel TextMatcherInternalModel TextMatcherInternalModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel RegexMatcherModel RegexMatcherModel RegexMatcherModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ChunkMergeModel ChunkMergeModel ChunkMergeModel ChunkMergeModel ChunkMergeModel ChunkFilterer LightDeIdentification LightDeIdentification</summary></entry><entry><title type="html">Clinical Deidentification Pipeline (German, Document Wise)</title><link href="/2024/11/29/clinical_deidentification_docwise_wip_de.html" rel="alternate" type="text/html" title="Clinical Deidentification Pipeline (German, Document Wise)" /><published>2024-11-29T00:00:00+00:00</published><updated>2024-11-29T00:00:00+00:00</updated><id>/2024/11/29/clinical_deidentification_docwise_wip_de</id><content type="html" xml:base="/2024/11/29/clinical_deidentification_docwise_wip_de.html">&lt;h2 id=&quot;description&quot;&gt;Description&lt;/h2&gt;
-
-&lt;p&gt;This pipeline can be used to deidentify PHI information from medical texts in German language. The PHI information will be masked and obfuscated in the resulting text.
-The pipeline can mask and obfuscate: &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LOCATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;NAME&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ID&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;AGE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PROFESSION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CONTACT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ORGANIZATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DOCTOR&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CITY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;COUNTRY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STREET&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PATIENT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PHONE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;HOSPITAL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DLN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;SSN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ZIP&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ACCOUNT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LICENSE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PLATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;VIN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;MEDICALRECORD&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;EMAIL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;URL&lt;/code&gt; entities.&lt;/p&gt;
-
-&lt;p class=&quot;btn-box&quot;&gt;&lt;button class=&quot;button button-orange&quot; disabled=&quot;&quot;&gt;Live Demo&lt;/button&gt;
-&lt;button class=&quot;button button-orange&quot; disabled=&quot;&quot;&gt;Open in Colab&lt;/button&gt;
-&lt;a href=&quot;https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_wip_de_5.5.1_3.0_1732886237933.zip&quot; class=&quot;button button-orange button-orange-trans arr button-icon hidden&quot;&gt;Download&lt;/a&gt;
-&lt;a href=&quot;s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_wip_de_5.5.1_3.0_1732886237933.zip&quot; class=&quot;button button-orange button-orange-trans button-icon button-copy-s3&quot;&gt;Copy S3 URI&lt;/a&gt;&lt;/p&gt;
-
-&lt;h2 id=&quot;how-to-use&quot;&gt;How to use&lt;/h2&gt;
-
-&lt;div class=&quot;tabs-box&quot;&gt;
-  &lt;div class=&quot;tabs-model-aproach-head&quot;&gt;&lt;button class=&quot;tab-li-model-aproach tabheader_active&quot;&gt;Python&lt;strong&gt;Healthcare NLP&lt;/strong&gt;&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach tab-li-jsl&quot;&gt;Python&lt;strong&gt;JohnSnowLabs&lt;/strong&gt;&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach&quot;&gt;Scala&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach tab-li-nlu&quot;&gt;NLU&lt;/button&gt;&lt;/div&gt;
-  &lt;div class=&quot;language-python highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-
-&lt;span class=&quot;kn&quot;&gt;from&lt;/span&gt; &lt;span class=&quot;nn&quot;&gt;sparknlp.pretrained&lt;/span&gt; &lt;span class=&quot;kn&quot;&gt;import&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;clinical_deidentification_docwise_wip&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;de&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;clinical/models&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot;&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;fullAnnotate&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;mi&quot;&gt;0&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;][&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'document'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;mi&quot;&gt;0&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;][&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'masked'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;mi&quot;&gt;0&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;][&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'obfuscated'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
-
-
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;  &lt;/div&gt;
-  &lt;div class=&quot;language-scala highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-
-&lt;span class=&quot;k&quot;&gt;import&lt;/span&gt; &lt;span class=&quot;nn&quot;&gt;com.johnsnowlabs.nlp.pretrained.PretrainedPipeline&lt;/span&gt;
-
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_pipeline&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;nc&quot;&gt;PretrainedPipeline&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;clinical_deidentification_docwise_wip&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;de&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;clinical/models&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;text&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot;&lt;/span&gt;
-
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_pipeline&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;fullAnnotate&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;nf&quot;&gt;println&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;mi&quot;&gt;0&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;document&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;map&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;_&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;result&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;toString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;mkString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;))&lt;/span&gt;
-&lt;span class=&quot;nf&quot;&gt;println&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;mi&quot;&gt;0&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;masked&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;map&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;_&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;result&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;toString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;mkString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;))&lt;/span&gt;
-&lt;span class=&quot;nf&quot;&gt;println&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;mi&quot;&gt;0&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;obfuscated&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;map&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;_&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;result&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;toString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;mkString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;))&lt;/span&gt;
-
-
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;  &lt;/div&gt;
+## Results
+
+```bash
+
+Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.
+-------------MASKED------------
+Sehr geehrter Herr &lt;NAME&gt;, bezüglich Ihrer Anfrage vom &lt;DATE&gt; für Ihre Krankenakte &lt;MEDICALRECORD&gt; möchte ich bestätigen, dass Ihre Sozialversicherungsnummer &lt;SSN&gt; und Ihre Versichertennummer &lt;ID&gt; korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen &lt;PLATE&gt; ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter &lt;PHONE&gt; oder schreiben Sie an &lt;EMAIL&gt;. Unsere Details finden Sie auch unter &lt;URL&gt;. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto &lt;ACCOUNT&gt;.
+-------------OBFUSCATED-----------
+Sehr geehrter Herr Berta, bezüglich Ihrer Anfrage vom 22.03.2024 für Ihre Krankenakte 816698 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 91990385M363und Ihre Versichertennummer B6556697P9R korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen N-RO 4769 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +70 10 25476981 oder schreiben Sie an Flaminia@google.com. Unsere Details finden Sie auch unter http://www.duran-brown.com/. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto XY21 5948 4488 4756 4354 44.
+
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|clinical_deidentification_docwise_large_wip|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 5.5.1+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|de|
+|Size:|2.9 GB|
+
+## Included Models
+
+- DocumentAssembler
+- InternalDocumentSplitter
+- TokenizerModel
+- WordEmbeddingsModel
+- PretrainedZeroShotNER
+- NerConverterInternalModel
+- MedicalNerModel
+- NerConverterInternalModel
+- MedicalNerModel
+- NerConverterInternalModel
+- RegexMatcherInternalModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- RegexMatcherInternalModel
+- RegexMatcherInternalModel
+- TextMatcherInternalModel
+- TextMatcherInternalModel
+- TextMatcherInternalModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- RegexMatcherModel
+- RegexMatcherModel
+- RegexMatcherModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ChunkMergeModel
+- ChunkMergeModel
+- ChunkMergeModel
+- ChunkMergeModel
+- ChunkMergeModel
+- ChunkFilterer
+- LightDeIdentification
+- LightDeIdentification</content><author><name>John Snow Labs</name></author><category term="deidentification" /><category term="deid" /><category term="de" /><category term="licensed" /><category term="clinical" /><category term="pipeline" /><category term="docwise" /><category term="zero_shot" /><summary type="html">Description This pipeline can be used to deidentify PHI information from medical texts in German language. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate: LOCATION, DATE, NAME, ID, AGE, PROFESSION, CONTACT, ORGANIZATION, DOCTOR, CITY, COUNTRY, STREET, PATIENT, PHONE, HOSPITAL, STATE, DLN, SSN, ZIP, ACCOUNT, LICENSE, PLATE, VIN, MEDICALRECORD, EMAIL, URL entities. Live Demo Open in Colab Download Copy S3 URI How to use PythonHealthcare NLPPythonJohnSnowLabsScalaNLU from sparknlp.pretrained import PretrainedPipeline deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_large_wip&quot;, &quot;de&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.result for i in deid_result[0]['document']])) print(''.join([i.result for i in deid_result[0]['masked']])) print(''.join([i.result for i in deid_result[0]['obfuscated']])) import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_large_wip&quot;, &quot;de&quot;, &quot;clinical/models&quot;) val text = &quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot; val deid_result = deid_pipeline.fullAnnotate(text) println(deid_result(0)(&quot;document&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) println(deid_result(0)(&quot;masked&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) println(deid_result(0)(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) Results Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00. -------------MASKED------------ Sehr geehrter Herr &amp;lt;NAME&amp;gt;, bezüglich Ihrer Anfrage vom &amp;lt;DATE&amp;gt; für Ihre Krankenakte &amp;lt;MEDICALRECORD&amp;gt; möchte ich bestätigen, dass Ihre Sozialversicherungsnummer &amp;lt;SSN&amp;gt; und Ihre Versichertennummer &amp;lt;ID&amp;gt; korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen &amp;lt;PLATE&amp;gt; ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter &amp;lt;PHONE&amp;gt; oder schreiben Sie an &amp;lt;EMAIL&amp;gt;. Unsere Details finden Sie auch unter &amp;lt;URL&amp;gt;. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto &amp;lt;ACCOUNT&amp;gt;. -------------OBFUSCATED----------- Sehr geehrter Herr Berta, bezüglich Ihrer Anfrage vom 22.03.2024 für Ihre Krankenakte 816698 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 91990385M363und Ihre Versichertennummer B6556697P9R korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen N-RO 4769 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +70 10 25476981 oder schreiben Sie an Flaminia@google.com. Unsere Details finden Sie auch unter http://www.duran-brown.com/. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto XY21 5948 4488 4756 4354 44. Model Information Model Name: clinical_deidentification_docwise_large_wip Type: pipeline Compatibility: Healthcare NLP 5.5.1+ License: Licensed Edition: Official Language: de Size: 2.9 GB Included Models DocumentAssembler InternalDocumentSplitter TokenizerModel WordEmbeddingsModel PretrainedZeroShotNER NerConverterInternalModel MedicalNerModel NerConverterInternalModel MedicalNerModel NerConverterInternalModel RegexMatcherInternalModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel RegexMatcherInternalModel RegexMatcherInternalModel TextMatcherInternalModel TextMatcherInternalModel TextMatcherInternalModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel RegexMatcherModel RegexMatcherModel RegexMatcherModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ChunkMergeModel ChunkMergeModel ChunkMergeModel ChunkMergeModel ChunkMergeModel ChunkFilterer LightDeIdentification LightDeIdentification</summary></entry><entry><title type="html">Clinical Deidentification Pipeline (German, Document Wise)</title><link href="/2024/11/29/clinical_deidentification_docwise_wip_de.html" rel="alternate" type="text/html" title="Clinical Deidentification Pipeline (German, Document Wise)" /><published>2024-11-29T00:00:00+00:00</published><updated>2024-11-29T00:00:00+00:00</updated><id>/2024/11/29/clinical_deidentification_docwise_wip_de</id><content type="html" xml:base="/2024/11/29/clinical_deidentification_docwise_wip_de.html">## Description
+
+This pipeline can be used to deidentify PHI information from medical texts in German language. The PHI information will be masked and obfuscated in the resulting text.
+The pipeline can mask and obfuscate: `LOCATION`, `DATE`, `NAME`, `ID`, `AGE`, `PROFESSION`, `CONTACT`, `ORGANIZATION`, `DOCTOR`, `CITY`, `COUNTRY`, `STREET`, `PATIENT`, `PHONE`, `HOSPITAL`, `STATE`, `DLN`, `SSN`, `ZIP`, `ACCOUNT`, `LICENSE`, `PLATE`, `VIN`, `MEDICALRECORD`, `EMAIL`, `URL` entities.
+
+{:.btn-box}
+&lt;button class=&quot;button button-orange&quot; disabled&gt;Live Demo&lt;/button&gt;
+&lt;button class=&quot;button button-orange&quot; disabled&gt;Open in Colab&lt;/button&gt;
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_wip_de_5.5.1_3.0_1732886237933.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_wip_de_5.5.1_3.0_1732886237933.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+&lt;div class=&quot;tabs-box&quot; markdown=&quot;1&quot;&gt;
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+
+
+from sparknlp.pretrained import PretrainedPipeline
+
+deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;de&quot;, &quot;clinical/models&quot;)
+
+text = &quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot;
+
+deid_result = deid_pipeline.fullAnnotate(text)
+
+print(''.join([i.result for i in deid_result[0]['document']]))
+print(''.join([i.result for i in deid_result[0]['masked']]))
+print(''.join([i.result for i in deid_result[0]['obfuscated']]))
+
+
+```
+```scala
+
+
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;de&quot;, &quot;clinical/models&quot;)
+
+val text = &quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot;
+
+val deid_result = deid_pipeline.fullAnnotate(text)
+
+println(deid_result(0)(&quot;document&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;))
+println(deid_result(0)(&quot;masked&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;))
+println(deid_result(0)(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;))
+
+
+```
 &lt;/div&gt;
 
-&lt;h2 id=&quot;results&quot;&gt;Results&lt;/h2&gt;
-
-&lt;div class=&quot;language-bash highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt &lt;span class=&quot;k&quot;&gt;in &lt;/span&gt;unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.
-&lt;span class=&quot;nt&quot;&gt;-------------MASKED------------&lt;/span&gt;
-Sehr geehrter Herr &amp;lt;NAME&amp;gt;, bezüglich Ihrer Anfrage vom &amp;lt;DATE&amp;gt; für Ihre Krankenakte &amp;lt;MEDICALRECORD&amp;gt; möchte ich bestätigen, dass Ihre Sozialversicherungsnummer &amp;lt;SSN&amp;gt; und Ihre Versichertennummer &amp;lt;ID&amp;gt; korrekt &lt;span class=&quot;k&quot;&gt;in &lt;/span&gt;unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen &amp;lt;PLATE&amp;gt; ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter &amp;lt;PHONE&amp;gt; oder schreiben Sie an &amp;lt;EMAIL&amp;gt;. Unsere Details finden Sie auch unter &amp;lt;URL&amp;gt;. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto &amp;lt;ACCOUNT&amp;gt;.
-&lt;span class=&quot;nt&quot;&gt;-------------OBFUSCATED-----------&lt;/span&gt;
-Sehr geehrter Herr Johan, bezüglich Ihrer Anfrage vom 14.05.2024 für Ihre Krankenakte 149921 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 68667052J030und Ihre Versichertennummer C1001142Q4S korrekt &lt;span class=&quot;k&quot;&gt;in &lt;/span&gt;unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen L-PM 6981 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +81 21 36587092 oder schreiben Sie an Aeron@google.com. Unsere Details finden Sie auch unter PoliticalMakeover.com.ee. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto IJ32 6059 5599 5867 5465 55.
-
-
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;/div&gt;
-
-&lt;h2 class=&quot;model-param&quot; id=&quot;model-information&quot;&gt;Model Information&lt;/h2&gt;
-
-&lt;table class=&quot;table-model&quot;&gt;
-  &lt;tbody&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Model Name:&lt;/td&gt;
-      &lt;td&gt;clinical_deidentification_docwise_wip&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Type:&lt;/td&gt;
-      &lt;td&gt;pipeline&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Compatibility:&lt;/td&gt;
-      &lt;td&gt;Healthcare NLP 5.5.1+&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;License:&lt;/td&gt;
-      &lt;td&gt;Licensed&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Edition:&lt;/td&gt;
-      &lt;td&gt;Official&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Language:&lt;/td&gt;
-      &lt;td&gt;de&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Size:&lt;/td&gt;
-      &lt;td&gt;1.3 GB&lt;/td&gt;
-    &lt;/tr&gt;
-  &lt;/tbody&gt;
-&lt;/table&gt;
-
-&lt;h2 id=&quot;included-models&quot;&gt;Included Models&lt;/h2&gt;
-
-&lt;ul&gt;
-  &lt;li&gt;DocumentAssembler&lt;/li&gt;
-  &lt;li&gt;InternalDocumentSplitter&lt;/li&gt;
-  &lt;li&gt;TokenizerModel&lt;/li&gt;
-  &lt;li&gt;WordEmbeddingsModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ChunkFilterer&lt;/li&gt;
-  &lt;li&gt;LightDeIdentification&lt;/li&gt;
-  &lt;li&gt;LightDeIdentification&lt;/li&gt;
-&lt;/ul&gt;</content><author><name>John Snow Labs</name></author><category term="deidentification" /><category term="deid" /><category term="de" /><category term="licensed" /><category term="clinical" /><category term="pipeline" /><category term="docwise" /><summary type="html">Description This pipeline can be used to deidentify PHI information from medical texts in German language. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate: LOCATION, DATE, NAME, ID, AGE, PROFESSION, CONTACT, ORGANIZATION, DOCTOR, CITY, COUNTRY, STREET, PATIENT, PHONE, HOSPITAL, STATE, DLN, SSN, ZIP, ACCOUNT, LICENSE, PLATE, VIN, MEDICALRECORD, EMAIL, URL entities. Live Demo Open in Colab Download Copy S3 URI How to use PythonHealthcare NLPPythonJohnSnowLabsScalaNLU from sparknlp.pretrained import PretrainedPipeline deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;de&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.result for i in deid_result[0]['document']])) print(''.join([i.result for i in deid_result[0]['masked']])) print(''.join([i.result for i in deid_result[0]['obfuscated']])) import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;de&quot;, &quot;clinical/models&quot;) val text = &quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot; val deid_result = deid_pipeline.fullAnnotate(text) println(deid_result(0)(&quot;document&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) println(deid_result(0)(&quot;masked&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) println(deid_result(0)(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) Results Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00. -------------MASKED------------ Sehr geehrter Herr &amp;lt;NAME&amp;gt;, bezüglich Ihrer Anfrage vom &amp;lt;DATE&amp;gt; für Ihre Krankenakte &amp;lt;MEDICALRECORD&amp;gt; möchte ich bestätigen, dass Ihre Sozialversicherungsnummer &amp;lt;SSN&amp;gt; und Ihre Versichertennummer &amp;lt;ID&amp;gt; korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen &amp;lt;PLATE&amp;gt; ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter &amp;lt;PHONE&amp;gt; oder schreiben Sie an &amp;lt;EMAIL&amp;gt;. Unsere Details finden Sie auch unter &amp;lt;URL&amp;gt;. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto &amp;lt;ACCOUNT&amp;gt;. -------------OBFUSCATED----------- Sehr geehrter Herr Johan, bezüglich Ihrer Anfrage vom 14.05.2024 für Ihre Krankenakte 149921 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 68667052J030und Ihre Versichertennummer C1001142Q4S korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen L-PM 6981 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +81 21 36587092 oder schreiben Sie an Aeron@google.com. Unsere Details finden Sie auch unter PoliticalMakeover.com.ee. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto IJ32 6059 5599 5867 5465 55. Model Information Model Name: clinical_deidentification_docwise_wip Type: pipeline Compatibility: Healthcare NLP 5.5.1+ License: Licensed Edition: Official Language: de Size: 1.3 GB Included Models DocumentAssembler InternalDocumentSplitter TokenizerModel WordEmbeddingsModel MedicalNerModel NerConverterInternalModel MedicalNerModel NerConverterInternalModel MedicalNerModel NerConverterInternalModel RegexMatcherInternalModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel RegexMatcherInternalModel RegexMatcherInternalModel TextMatcherInternalModel TextMatcherInternalModel TextMatcherInternalModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel RegexMatcherModel RegexMatcherModel RegexMatcherModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ChunkMergeModel ChunkMergeModel ChunkMergeModel ChunkMergeModel ChunkMergeModel ChunkFilterer LightDeIdentification LightDeIdentification</summary></entry><entry><title type="html">Clinical Deidentification Pipeline (Document Wise)</title><link href="/2024/11/29/clinical_deidentification_docwise_wip_en.html" rel="alternate" type="text/html" title="Clinical Deidentification Pipeline (Document Wise)" /><published>2024-11-29T00:00:00+00:00</published><updated>2024-11-29T00:00:00+00:00</updated><id>/2024/11/29/clinical_deidentification_docwise_wip_en</id><content type="html" xml:base="/2024/11/29/clinical_deidentification_docwise_wip_en.html">&lt;h2 id=&quot;description&quot;&gt;Description&lt;/h2&gt;
-
-&lt;p&gt;This pipeline can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text.
-The pipeline can mask and obfuscate &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LOCATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CONTACT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PROFESSION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;NAME&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;AGE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;MEDICALRECORD&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ORGANIZATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;HEALTHPLAN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DOCTOR&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;USERNAME&lt;/code&gt;,
-&lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LOCATION-OTHER&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;URL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DEVICE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CITY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ZIP&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PATIENT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;COUNTRY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STREET&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PHONE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;HOSPITAL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;EMAIL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;IDNUM&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;BIOID&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;FAX&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LOCATION_OTHER&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DLN&lt;/code&gt;,
-&lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;SSN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ACCOUNT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PLATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;VIN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LICENSE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;IP&lt;/code&gt; entities.&lt;/p&gt;
-
-&lt;h2 id=&quot;predicted-entities&quot;&gt;Predicted Entities&lt;/h2&gt;
-
-&lt;p&gt;&lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ACCOUNT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;AGE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CITY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CONTACT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;COUNTRY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DLN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DOCTOR&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;EMAIL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;EXCLUDED&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;HOSPITAL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ID&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LICENSE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LOCATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;MEDICALRECORD&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;NAME&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ORGANIZATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PATIENT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PHONE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PLATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PROFESSION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;SSN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STREET&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;URL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;USERNAME&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;VIN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ZIP&lt;/code&gt;&lt;/p&gt;
-
-&lt;p class=&quot;btn-box&quot;&gt;&lt;button class=&quot;button button-orange&quot; disabled=&quot;&quot;&gt;Live Demo&lt;/button&gt;
-&lt;button class=&quot;button button-orange&quot; disabled=&quot;&quot;&gt;Open in Colab&lt;/button&gt;
-&lt;a href=&quot;https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_wip_en_5.5.0_3.0_1732868771089.zip&quot; class=&quot;button button-orange button-orange-trans arr button-icon hidden&quot;&gt;Download&lt;/a&gt;
-&lt;a href=&quot;s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_wip_en_5.5.0_3.0_1732868771089.zip&quot; class=&quot;button button-orange button-orange-trans button-icon button-copy-s3&quot;&gt;Copy S3 URI&lt;/a&gt;&lt;/p&gt;
-
-&lt;h2 id=&quot;how-to-use&quot;&gt;How to use&lt;/h2&gt;
-
-&lt;div class=&quot;tabs-box&quot;&gt;
-  &lt;div class=&quot;tabs-model-aproach-head&quot;&gt;&lt;button class=&quot;tab-li-model-aproach tabheader_active&quot;&gt;Python&lt;strong&gt;Healthcare NLP&lt;/strong&gt;&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach tab-li-jsl&quot;&gt;Python&lt;strong&gt;JohnSnowLabs&lt;/strong&gt;&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach&quot;&gt;Scala&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach tab-li-nlu&quot;&gt;NLU&lt;/button&gt;&lt;/div&gt;
-
-  &lt;div class=&quot;language-python highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;&lt;span class=&quot;kn&quot;&gt;from&lt;/span&gt; &lt;span class=&quot;nn&quot;&gt;sparknlp.pretrained&lt;/span&gt; &lt;span class=&quot;kn&quot;&gt;import&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;clinical_deidentification_docwise_wip&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;en&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;clinical/models&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
+## Results
+
+```bash
+
+Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.
+-------------MASKED------------
+Sehr geehrter Herr &lt;NAME&gt;, bezüglich Ihrer Anfrage vom &lt;DATE&gt; für Ihre Krankenakte &lt;MEDICALRECORD&gt; möchte ich bestätigen, dass Ihre Sozialversicherungsnummer &lt;SSN&gt; und Ihre Versichertennummer &lt;ID&gt; korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen &lt;PLATE&gt; ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter &lt;PHONE&gt; oder schreiben Sie an &lt;EMAIL&gt;. Unsere Details finden Sie auch unter &lt;URL&gt;. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto &lt;ACCOUNT&gt;.
+-------------OBFUSCATED-----------
+Sehr geehrter Herr Johan, bezüglich Ihrer Anfrage vom 14.05.2024 für Ihre Krankenakte 149921 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 68667052J030und Ihre Versichertennummer C1001142Q4S korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen L-PM 6981 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +81 21 36587092 oder schreiben Sie an Aeron@google.com. Unsere Details finden Sie auch unter PoliticalMakeover.com.ee. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto IJ32 6059 5599 5867 5465 55.
+
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|clinical_deidentification_docwise_wip|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 5.5.1+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|de|
+|Size:|1.3 GB|
+
+## Included Models
+
+- DocumentAssembler
+- InternalDocumentSplitter
+- TokenizerModel
+- WordEmbeddingsModel
+- MedicalNerModel
+- NerConverterInternalModel
+- MedicalNerModel
+- NerConverterInternalModel
+- MedicalNerModel
+- NerConverterInternalModel
+- RegexMatcherInternalModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- RegexMatcherInternalModel
+- RegexMatcherInternalModel
+- TextMatcherInternalModel
+- TextMatcherInternalModel
+- TextMatcherInternalModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- RegexMatcherModel
+- RegexMatcherModel
+- RegexMatcherModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ChunkMergeModel
+- ChunkMergeModel
+- ChunkMergeModel
+- ChunkMergeModel
+- ChunkMergeModel
+- ChunkFilterer
+- LightDeIdentification
+- LightDeIdentification</content><author><name>John Snow Labs</name></author><category term="deidentification" /><category term="deid" /><category term="de" /><category term="licensed" /><category term="clinical" /><category term="pipeline" /><category term="docwise" /><summary type="html">Description This pipeline can be used to deidentify PHI information from medical texts in German language. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate: LOCATION, DATE, NAME, ID, AGE, PROFESSION, CONTACT, ORGANIZATION, DOCTOR, CITY, COUNTRY, STREET, PATIENT, PHONE, HOSPITAL, STATE, DLN, SSN, ZIP, ACCOUNT, LICENSE, PLATE, VIN, MEDICALRECORD, EMAIL, URL entities. Live Demo Open in Colab Download Copy S3 URI How to use PythonHealthcare NLPPythonJohnSnowLabsScalaNLU from sparknlp.pretrained import PretrainedPipeline deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;de&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.result for i in deid_result[0]['document']])) print(''.join([i.result for i in deid_result[0]['masked']])) print(''.join([i.result for i in deid_result[0]['obfuscated']])) import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;de&quot;, &quot;clinical/models&quot;) val text = &quot;&quot;&quot;Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00.&quot;&quot;&quot; val deid_result = deid_pipeline.fullAnnotate(text) println(deid_result(0)(&quot;document&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) println(deid_result(0)(&quot;masked&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) println(deid_result(0)(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) Results Sehr geehrter Herr Schmidt, bezüglich Ihrer Anfrage vom 15.03.2024 für Ihre Krankenakte 341123 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 13110587M565 und Ihre Versichertennummer T0110053F5D korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen M-AB 1234 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +49 89 12345678 oder schreiben Sie an support@beispiel.de. Unsere Details finden Sie auch unter https://www.beispiel-behoerde.de. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto DE89 3704 0044 0532 0130 00. -------------MASKED------------ Sehr geehrter Herr &amp;lt;NAME&amp;gt;, bezüglich Ihrer Anfrage vom &amp;lt;DATE&amp;gt; für Ihre Krankenakte &amp;lt;MEDICALRECORD&amp;gt; möchte ich bestätigen, dass Ihre Sozialversicherungsnummer &amp;lt;SSN&amp;gt; und Ihre Versichertennummer &amp;lt;ID&amp;gt; korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen &amp;lt;PLATE&amp;gt; ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter &amp;lt;PHONE&amp;gt; oder schreiben Sie an &amp;lt;EMAIL&amp;gt;. Unsere Details finden Sie auch unter &amp;lt;URL&amp;gt;. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto &amp;lt;ACCOUNT&amp;gt;. -------------OBFUSCATED----------- Sehr geehrter Herr Johan, bezüglich Ihrer Anfrage vom 14.05.2024 für Ihre Krankenakte 149921 möchte ich bestätigen, dass Ihre Sozialversicherungsnummer 68667052J030und Ihre Versichertennummer C1001142Q4S korrekt in unserem System hinterlegt sind. Ihr Fahrzeug mit dem Kennzeichen L-PM 6981 ist bereits registriert. Falls Sie Fragen haben, erreichen Sie uns unter +81 21 36587092 oder schreiben Sie an Aeron@google.com. Unsere Details finden Sie auch unter PoliticalMakeover.com.ee. Für Ihre Bankgeschäfte nutzen Sie weiterhin das Konto IJ32 6059 5599 5867 5465 55. Model Information Model Name: clinical_deidentification_docwise_wip Type: pipeline Compatibility: Healthcare NLP 5.5.1+ License: Licensed Edition: Official Language: de Size: 1.3 GB Included Models DocumentAssembler InternalDocumentSplitter TokenizerModel WordEmbeddingsModel MedicalNerModel NerConverterInternalModel MedicalNerModel NerConverterInternalModel MedicalNerModel NerConverterInternalModel RegexMatcherInternalModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel RegexMatcherInternalModel RegexMatcherInternalModel TextMatcherInternalModel TextMatcherInternalModel TextMatcherInternalModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel RegexMatcherModel RegexMatcherModel RegexMatcherModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ChunkMergeModel ChunkMergeModel ChunkMergeModel ChunkMergeModel ChunkMergeModel ChunkFilterer LightDeIdentification LightDeIdentification</summary></entry><entry><title type="html">Clinical Deidentification Pipeline (Document Wise)</title><link href="/2024/11/29/clinical_deidentification_docwise_wip_en.html" rel="alternate" type="text/html" title="Clinical Deidentification Pipeline (Document Wise)" /><published>2024-11-29T00:00:00+00:00</published><updated>2024-11-29T00:00:00+00:00</updated><id>/2024/11/29/clinical_deidentification_docwise_wip_en</id><content type="html" xml:base="/2024/11/29/clinical_deidentification_docwise_wip_en.html">## Description
+
+This pipeline can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text.
+The pipeline can mask and obfuscate `LOCATION`, `CONTACT`, `PROFESSION`, `NAME`, `DATE`, `AGE`, `MEDICALRECORD`, `ORGANIZATION`, `HEALTHPLAN`, `DOCTOR`, `USERNAME`,
+`LOCATION-OTHER`, `URL`, `DEVICE`, `CITY`, `ZIP`, `STATE`, `PATIENT`, `COUNTRY`, `STREET`, `PHONE`, `HOSPITAL`, `EMAIL`, `IDNUM`, `BIOID`, `FAX`, `LOCATION_OTHER`, `DLN`,
+`SSN`, `ACCOUNT`, `PLATE`, `VIN`, `LICENSE`, `IP` entities.
+
+## Predicted Entities
+
+`ACCOUNT`, `AGE`, `CITY`, `CONTACT`, `COUNTRY`, `DATE`, `DLN`, `DOCTOR`, `EMAIL`, `EXCLUDED`, `HOSPITAL`, `ID`, `LICENSE`, `LOCATION`, `MEDICALRECORD`, `NAME`, `ORGANIZATION`, `PATIENT`, `PHONE`, `PLATE`, `PROFESSION`, `SSN`, `STATE`, `STREET`, `URL`, `USERNAME`, `VIN`, `ZIP`
+
+
+
+{:.btn-box}
+&lt;button class=&quot;button button-orange&quot; disabled&gt;Live Demo&lt;/button&gt;
+&lt;button class=&quot;button button-orange&quot; disabled&gt;Open in Colab&lt;/button&gt;
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_wip_en_5.5.0_3.0_1732868771089.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_wip_en_5.5.0_3.0_1732868771089.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+&lt;div class=&quot;tabs-box&quot; markdown=&quot;1&quot;&gt;
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+  
+```python
+from sparknlp.pretrained import PretrainedPipeline
+
+deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;)
+
+text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
 The patient’s medical record number is 56467890.
-The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;&lt;/span&gt;
+The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;
+
+deid_result = deid_pipeline.fullAnnotate(text)
 
-&lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;fullAnnotate&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
+print(''.join([i.result for i in deid_result['mask_entity']]))
+print(''.join([i.result for i in deid_result['obfuscated']]))
 
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'mask_entity'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'obfuscated'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
 
+```
 
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;  &lt;/div&gt;
+{:.jsl-block}
+```python
 
-  &lt;div class=&quot;language-python jsl-block highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-&lt;span class=&quot;kn&quot;&gt;from&lt;/span&gt; &lt;span class=&quot;nn&quot;&gt;sparknlp.pretrained&lt;/span&gt; &lt;span class=&quot;kn&quot;&gt;import&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;
+from sparknlp.pretrained import PretrainedPipeline
 
-&lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;nlp&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;clinical_deidentification_docwise_wip&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;en&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;clinical/models&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
+deid_pipeline = nlp.PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;)
 
-&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
+text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
 The patient’s medical record number is 56467890.
-The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;&lt;/span&gt;
+The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;
 
-&lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;fullAnnotate&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
+deid_result = deid_pipeline.fullAnnotate(text)
 
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'mask_entity'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'obfuscated'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
+print(''.join([i.result for i in deid_result['mask_entity']]))
+print(''.join([i.result for i in deid_result['obfuscated']]))
 
 
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;  &lt;/div&gt;
-  &lt;div class=&quot;language-scala highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-&lt;span class=&quot;k&quot;&gt;import&lt;/span&gt; &lt;span class=&quot;nn&quot;&gt;com.johnsnowlabs.nlp.pretrained.PretrainedPipeline&lt;/span&gt;
+```
+```scala
 
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_pipeline&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;nc&quot;&gt;PretrainedPipeline&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;clinical_deidentification_docwise_wip&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;en&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;clinical/models&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;text&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
+val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;)
+
+val text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
 The patient’s medical record number is 56467890.
-The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;&lt;/span&gt;
+The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;
 
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_pipeline&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;fullAnnotate&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
+val deid_result = deid_pipeline.fullAnnotate(text)
 
-&lt;span class=&quot;nf&quot;&gt;println&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;mask_entity&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;map&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;_&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;result&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;toString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;mkString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;))&lt;/span&gt;
-&lt;span class=&quot;nf&quot;&gt;println&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;obfuscated&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;map&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;_&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;result&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;toString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;mkString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;))&lt;/span&gt;
+println(deid_result(&quot;mask_entity&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;))
+println(deid_result(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;))
 
 
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;  &lt;/div&gt;
+```
 &lt;/div&gt;
 
-&lt;h2 id=&quot;results&quot;&gt;Results&lt;/h2&gt;
+## Results
+
+```bash
 
-&lt;div class=&quot;language-bash highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
 Masked with entity labels
-&lt;span class=&quot;nt&quot;&gt;------------------------------&lt;/span&gt;
-Dr. &amp;lt;DOCTOR&amp;gt;, from &amp;lt;HOSPITAL&amp;gt; &lt;span class=&quot;k&quot;&gt;in&lt;/span&gt; &amp;lt;CITY&amp;gt;,  attended to the patient on &amp;lt;DATE&amp;gt;.
-The patient’s medical record number is &amp;lt;MEDICALRECORD&amp;gt;
-patient, &amp;lt;PATIENT&amp;gt;, is &amp;lt;AGE&amp;gt; years old,  her Contact number: &amp;lt;PHONE&amp;gt; &lt;span class=&quot;nb&quot;&gt;.&lt;/span&gt;
+------------------------------
+Dr. &lt;DOCTOR&gt;, from &lt;HOSPITAL&gt; in &lt;CITY&gt;,  attended to the patient on &lt;DATE&gt;.
+The patient’s medical record number is &lt;MEDICALRECORD&gt;
+patient, &lt;PATIENT&gt;, is &lt;AGE&gt; years old,  her Contact number: &lt;PHONE&gt; .
 
 Obfuscated
-&lt;span class=&quot;nt&quot;&gt;------------------------------&lt;/span&gt;
-Dr. Edwardo Graft, from MCBRIDE ORTHOPEDIC HOSPITAL &lt;span class=&quot;k&quot;&gt;in &lt;/span&gt;CLAMART,  attended to the patient on 14/06/2024.
+------------------------------
+Dr. Edwardo Graft, from MCBRIDE ORTHOPEDIC HOSPITAL in CLAMART,  attended to the patient on 14/06/2024.
 The patient’s medical record number is 78295621.
-The patient, Nathaneil Bakes, is 43 years old,  her Contact number: 308-657-8469 &lt;span class=&quot;nb&quot;&gt;.&lt;/span&gt;
-
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;/div&gt;
-
-&lt;h2 class=&quot;model-param&quot; id=&quot;model-information&quot;&gt;Model Information&lt;/h2&gt;
-
-&lt;table class=&quot;table-model&quot;&gt;
-  &lt;tbody&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Model Name:&lt;/td&gt;
-      &lt;td&gt;clinical_deidentification_docwise_wip&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Type:&lt;/td&gt;
-      &lt;td&gt;pipeline&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Compatibility:&lt;/td&gt;
-      &lt;td&gt;Healthcare NLP 5.5.0+&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;License:&lt;/td&gt;
-      &lt;td&gt;Licensed&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Edition:&lt;/td&gt;
-      &lt;td&gt;Official&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Language:&lt;/td&gt;
-      &lt;td&gt;en&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Size:&lt;/td&gt;
-      &lt;td&gt;1.8 GB&lt;/td&gt;
-    &lt;/tr&gt;
-  &lt;/tbody&gt;
-&lt;/table&gt;
-
-&lt;h2 id=&quot;included-models&quot;&gt;Included Models&lt;/h2&gt;
-
-&lt;ul&gt;
-  &lt;li&gt;DocumentAssembler&lt;/li&gt;
-  &lt;li&gt;InternalDocumentSplitter&lt;/li&gt;
-  &lt;li&gt;TokenizerModel&lt;/li&gt;
-  &lt;li&gt;WordEmbeddingsModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;LightDeIdentification&lt;/li&gt;
-  &lt;li&gt;LightDeIdentification&lt;/li&gt;
-&lt;/ul&gt;</content><author><name>John Snow Labs</name></author><category term="deidentification" /><category term="deid" /><category term="en" /><category term="licensed" /><category term="clinical" /><category term="pipeline" /><category term="docwise" /><summary type="html">Description This pipeline can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate LOCATION, CONTACT, PROFESSION, NAME, DATE, AGE, MEDICALRECORD, ORGANIZATION, HEALTHPLAN, DOCTOR, USERNAME, LOCATION-OTHER, URL, DEVICE, CITY, ZIP, STATE, PATIENT, COUNTRY, STREET, PHONE, HOSPITAL, EMAIL, IDNUM, BIOID, FAX, LOCATION_OTHER, DLN, SSN, ACCOUNT, PLATE, VIN, LICENSE, IP entities. Predicted Entities ACCOUNT, AGE, CITY, CONTACT, COUNTRY, DATE, DLN, DOCTOR, EMAIL, EXCLUDED, HOSPITAL, ID, LICENSE, LOCATION, MEDICALRECORD, NAME, ORGANIZATION, PATIENT, PHONE, PLATE, PROFESSION, SSN, STATE, STREET, URL, USERNAME, VIN, ZIP Live Demo Open in Colab Download Copy S3 URI How to use PythonHealthcare NLPPythonJohnSnowLabsScalaNLU from sparknlp.pretrained import PretrainedPipeline deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.result for i in deid_result['mask_entity']])) print(''.join([i.result for i in deid_result['obfuscated']])) from sparknlp.pretrained import PretrainedPipeline deid_pipeline = nlp.PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.result for i in deid_result['mask_entity']])) print(''.join([i.result for i in deid_result['obfuscated']])) import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;) val text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; val deid_result = deid_pipeline.fullAnnotate(text) println(deid_result(&quot;mask_entity&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) println(deid_result(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) Results Masked with entity labels ------------------------------ Dr. &amp;lt;DOCTOR&amp;gt;, from &amp;lt;HOSPITAL&amp;gt; in &amp;lt;CITY&amp;gt;, attended to the patient on &amp;lt;DATE&amp;gt;. The patient’s medical record number is &amp;lt;MEDICALRECORD&amp;gt; patient, &amp;lt;PATIENT&amp;gt;, is &amp;lt;AGE&amp;gt; years old, her Contact number: &amp;lt;PHONE&amp;gt; . Obfuscated ------------------------------ Dr. Edwardo Graft, from MCBRIDE ORTHOPEDIC HOSPITAL in CLAMART, attended to the patient on 14/06/2024. The patient’s medical record number is 78295621. The patient, Nathaneil Bakes, is 43 years old, her Contact number: 308-657-8469 . Model Information Model Name: clinical_deidentification_docwise_wip Type: pipeline Compatibility: Healthcare NLP 5.5.0+ License: Licensed Edition: Official Language: en Size: 1.8 GB Included Models DocumentAssembler InternalDocumentSplitter TokenizerModel WordEmbeddingsModel MedicalNerModel NerConverterInternalModel MedicalNerModel MedicalNerModel MedicalNerModel NerConverterInternalModel NerConverterInternalModel NerConverterInternalModel ChunkMergeModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel TextMatcherInternalModel TextMatcherInternalModel TextMatcherInternalModel ContextualParserModel RegexMatcherInternalModel ContextualParserModel ContextualParserModel ContextualParserModel RegexMatcherInternalModel RegexMatcherInternalModel RegexMatcherInternalModel TextMatcherInternalModel ChunkMergeModel ChunkMergeModel LightDeIdentification LightDeIdentification</summary></entry><entry><title type="html">Clinical Deidentification Pipeline (Document Wise)</title><link href="/2024/11/29/clinical_deidentification_docwise_wip_v2_en.html" rel="alternate" type="text/html" title="Clinical Deidentification Pipeline (Document Wise)" /><published>2024-11-29T00:00:00+00:00</published><updated>2024-11-29T00:00:00+00:00</updated><id>/2024/11/29/clinical_deidentification_docwise_wip_v2_en</id><content type="html" xml:base="/2024/11/29/clinical_deidentification_docwise_wip_v2_en.html">&lt;h2 id=&quot;description&quot;&gt;Description&lt;/h2&gt;
-
-&lt;p&gt;This pipeline can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text.
-The pipeline can mask and obfuscate &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LOCATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CONTACT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PROFESSION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;NAME&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;AGE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;MEDICALRECORD&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ORGANIZATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;HEALTHPLAN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DOCTOR&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;USERNAME&lt;/code&gt;,
-&lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LOCATION-OTHER&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;URL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DEVICE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CITY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ZIP&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PATIENT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;COUNTRY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STREET&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PHONE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;HOSPITAL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;EMAIL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;IDNUM&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;BIOID&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;FAX&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LOCATION_OTHER&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DLN&lt;/code&gt;,
-&lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;SSN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ACCOUNT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PLATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;VIN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LICENSE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;IP&lt;/code&gt; entities.&lt;/p&gt;
-
-&lt;h2 id=&quot;predicted-entities&quot;&gt;Predicted Entities&lt;/h2&gt;
-
-&lt;p&gt;&lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;NAME&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;IDNUM&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ZIP&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;SSN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ACCOUNT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LICENSE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;AGE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PHONE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;COUNTRY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CITY&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PLATE&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;VIN&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;MEDICALRECORD&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;EMAIL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;URL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;LOCATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PROFESSION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;CONTACT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;PATIENT&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;HOSPITAL&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;ORGANIZATION&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;STREET&lt;/code&gt;, &lt;code class=&quot;language-plaintext highlighter-rouge&quot;&gt;DOCTOR&lt;/code&gt;&lt;/p&gt;
-
-&lt;p class=&quot;btn-box&quot;&gt;&lt;button class=&quot;button button-orange&quot; disabled=&quot;&quot;&gt;Live Demo&lt;/button&gt;
-&lt;button class=&quot;button button-orange&quot; disabled=&quot;&quot;&gt;Open in Colab&lt;/button&gt;
-&lt;a href=&quot;https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_wip_v2_en_5.5.1_3.0_1732874596442.zip&quot; class=&quot;button button-orange button-orange-trans arr button-icon hidden&quot;&gt;Download&lt;/a&gt;
-&lt;a href=&quot;s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_wip_v2_en_5.5.1_3.0_1732874596442.zip&quot; class=&quot;button button-orange button-orange-trans button-icon button-copy-s3&quot;&gt;Copy S3 URI&lt;/a&gt;&lt;/p&gt;
-
-&lt;h2 id=&quot;how-to-use&quot;&gt;How to use&lt;/h2&gt;
-
-&lt;div class=&quot;tabs-box&quot;&gt;
-  &lt;div class=&quot;tabs-model-aproach-head&quot;&gt;&lt;button class=&quot;tab-li-model-aproach tabheader_active&quot;&gt;Python&lt;strong&gt;Healthcare NLP&lt;/strong&gt;&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach tab-li-jsl&quot;&gt;Python&lt;strong&gt;JohnSnowLabs&lt;/strong&gt;&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach&quot;&gt;Scala&lt;/button&gt;&lt;button class=&quot;tab-li-model-aproach tab-li-nlu&quot;&gt;NLU&lt;/button&gt;&lt;/div&gt;
-
-  &lt;div class=&quot;language-python highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-&lt;span class=&quot;kn&quot;&gt;from&lt;/span&gt; &lt;span class=&quot;nn&quot;&gt;sparknlp.pretrained&lt;/span&gt; &lt;span class=&quot;kn&quot;&gt;import&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;clinical_deidentification_docwise_wip_v2&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;en&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;clinical/models&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
-
-&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
+The patient, Nathaneil Bakes, is 43 years old,  her Contact number: 308-657-8469 .
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|clinical_deidentification_docwise_wip|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 5.5.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+|Size:|1.8 GB|
+
+## Included Models
+
+- DocumentAssembler
+- InternalDocumentSplitter
+- TokenizerModel
+- WordEmbeddingsModel
+- MedicalNerModel
+- NerConverterInternalModel
+- MedicalNerModel
+- MedicalNerModel
+- MedicalNerModel
+- NerConverterInternalModel
+- NerConverterInternalModel
+- NerConverterInternalModel
+- ChunkMergeModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- TextMatcherInternalModel
+- TextMatcherInternalModel
+- TextMatcherInternalModel
+- ContextualParserModel
+- RegexMatcherInternalModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- RegexMatcherInternalModel
+- RegexMatcherInternalModel
+- RegexMatcherInternalModel
+- TextMatcherInternalModel
+- ChunkMergeModel
+- ChunkMergeModel
+- LightDeIdentification
+- LightDeIdentification</content><author><name>John Snow Labs</name></author><category term="deidentification" /><category term="deid" /><category term="en" /><category term="licensed" /><category term="clinical" /><category term="pipeline" /><category term="docwise" /><summary type="html">Description This pipeline can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate LOCATION, CONTACT, PROFESSION, NAME, DATE, AGE, MEDICALRECORD, ORGANIZATION, HEALTHPLAN, DOCTOR, USERNAME, LOCATION-OTHER, URL, DEVICE, CITY, ZIP, STATE, PATIENT, COUNTRY, STREET, PHONE, HOSPITAL, EMAIL, IDNUM, BIOID, FAX, LOCATION_OTHER, DLN, SSN, ACCOUNT, PLATE, VIN, LICENSE, IP entities. Predicted Entities ACCOUNT, AGE, CITY, CONTACT, COUNTRY, DATE, DLN, DOCTOR, EMAIL, EXCLUDED, HOSPITAL, ID, LICENSE, LOCATION, MEDICALRECORD, NAME, ORGANIZATION, PATIENT, PHONE, PLATE, PROFESSION, SSN, STATE, STREET, URL, USERNAME, VIN, ZIP Live Demo Open in Colab Download Copy S3 URI How to use PythonHealthcare NLPPythonJohnSnowLabsScalaNLU from sparknlp.pretrained import PretrainedPipeline deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.result for i in deid_result['mask_entity']])) print(''.join([i.result for i in deid_result['obfuscated']])) from sparknlp.pretrained import PretrainedPipeline deid_pipeline = nlp.PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.result for i in deid_result['mask_entity']])) print(''.join([i.result for i in deid_result['obfuscated']])) import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip&quot;, &quot;en&quot;, &quot;clinical/models&quot;) val text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; val deid_result = deid_pipeline.fullAnnotate(text) println(deid_result(&quot;mask_entity&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) println(deid_result(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) Results Masked with entity labels ------------------------------ Dr. &amp;lt;DOCTOR&amp;gt;, from &amp;lt;HOSPITAL&amp;gt; in &amp;lt;CITY&amp;gt;, attended to the patient on &amp;lt;DATE&amp;gt;. The patient’s medical record number is &amp;lt;MEDICALRECORD&amp;gt; patient, &amp;lt;PATIENT&amp;gt;, is &amp;lt;AGE&amp;gt; years old, her Contact number: &amp;lt;PHONE&amp;gt; . Obfuscated ------------------------------ Dr. Edwardo Graft, from MCBRIDE ORTHOPEDIC HOSPITAL in CLAMART, attended to the patient on 14/06/2024. The patient’s medical record number is 78295621. The patient, Nathaneil Bakes, is 43 years old, her Contact number: 308-657-8469 . Model Information Model Name: clinical_deidentification_docwise_wip Type: pipeline Compatibility: Healthcare NLP 5.5.0+ License: Licensed Edition: Official Language: en Size: 1.8 GB Included Models DocumentAssembler InternalDocumentSplitter TokenizerModel WordEmbeddingsModel MedicalNerModel NerConverterInternalModel MedicalNerModel MedicalNerModel MedicalNerModel NerConverterInternalModel NerConverterInternalModel NerConverterInternalModel ChunkMergeModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel TextMatcherInternalModel TextMatcherInternalModel TextMatcherInternalModel ContextualParserModel RegexMatcherInternalModel ContextualParserModel ContextualParserModel ContextualParserModel RegexMatcherInternalModel RegexMatcherInternalModel RegexMatcherInternalModel TextMatcherInternalModel ChunkMergeModel ChunkMergeModel LightDeIdentification LightDeIdentification</summary></entry><entry><title type="html">Clinical Deidentification Pipeline (Document Wise)</title><link href="/2024/11/29/clinical_deidentification_docwise_wip_v2_en.html" rel="alternate" type="text/html" title="Clinical Deidentification Pipeline (Document Wise)" /><published>2024-11-29T00:00:00+00:00</published><updated>2024-11-29T00:00:00+00:00</updated><id>/2024/11/29/clinical_deidentification_docwise_wip_v2_en</id><content type="html" xml:base="/2024/11/29/clinical_deidentification_docwise_wip_v2_en.html">## Description
+
+This pipeline can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text.
+The pipeline can mask and obfuscate `LOCATION`, `CONTACT`, `PROFESSION`, `NAME`, `DATE`, `AGE`, `MEDICALRECORD`, `ORGANIZATION`, `HEALTHPLAN`, `DOCTOR`, `USERNAME`,
+`LOCATION-OTHER`, `URL`, `DEVICE`, `CITY`, `ZIP`, `STATE`, `PATIENT`, `COUNTRY`, `STREET`, `PHONE`, `HOSPITAL`, `EMAIL`, `IDNUM`, `BIOID`, `FAX`, `LOCATION_OTHER`, `DLN`,
+`SSN`, `ACCOUNT`, `PLATE`, `VIN`, `LICENSE`, `IP` entities.
+
+## Predicted Entities
+
+`NAME`, `DATE`, `IDNUM`, `ZIP`, `SSN`, `ACCOUNT`, `LICENSE`, `AGE`, `PHONE`, `COUNTRY`, `STATE`, `CITY`, `PLATE`, `VIN`, `MEDICALRECORD`, `EMAIL`, `URL`, `LOCATION`, `PROFESSION`, `CONTACT`, `PATIENT`, `HOSPITAL`, `ORGANIZATION`, `STREET`, `DOCTOR`
+
+{:.btn-box}
+&lt;button class=&quot;button button-orange&quot; disabled&gt;Live Demo&lt;/button&gt;
+&lt;button class=&quot;button button-orange&quot; disabled&gt;Open in Colab&lt;/button&gt;
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_wip_v2_en_5.5.1_3.0_1732874596442.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_docwise_wip_v2_en_5.5.1_3.0_1732874596442.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+&lt;div class=&quot;tabs-box&quot; markdown=&quot;1&quot;&gt;
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+  
+```python
+
+from sparknlp.pretrained import PretrainedPipeline
+
+deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip_v2&quot;, &quot;en&quot;, &quot;clinical/models&quot;)
+
+text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
 The patient’s medical record number is 56467890.
-The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;&lt;/span&gt;
+The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;
 
-&lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;fullAnnotate&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
+deid_result = deid_pipeline.fullAnnotate(text)
 
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'mask_entity'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'obfuscated'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
+print(''.join([i.result for i in deid_result['mask_entity']]))
+print(''.join([i.result for i in deid_result['obfuscated']]))
 
 
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;  &lt;/div&gt;
+```
 
-  &lt;div class=&quot;language-python jsl-block highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-&lt;span class=&quot;kn&quot;&gt;from&lt;/span&gt; &lt;span class=&quot;nn&quot;&gt;sparknlp.pretrained&lt;/span&gt; &lt;span class=&quot;kn&quot;&gt;import&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;
+{:.jsl-block}
+```python
 
-&lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;nlp&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;PretrainedPipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;clinical_deidentification_docwise_wip_v2&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;en&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;clinical/models&quot;&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
+from sparknlp.pretrained import PretrainedPipeline
 
-&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
+deid_pipeline = nlp.PretrainedPipeline(&quot;clinical_deidentification_docwise_wip_v2&quot;, &quot;en&quot;, &quot;clinical/models&quot;)
+
+text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
 The patient’s medical record number is 56467890.
-The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;&lt;/span&gt;
+The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;
+
+deid_result = deid_pipeline.fullAnnotate(text)
 
-&lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt; &lt;span class=&quot;o&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_pipeline&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;fullAnnotate&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;)&lt;/span&gt;
+print(''.join([i.result for i in deid_result['mask_entity']]))
+print(''.join([i.result for i in deid_result['obfuscated']]))
 
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'mask_entity'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
-&lt;span class=&quot;k&quot;&gt;print&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;''&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;join&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;([&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;i&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;for&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;i&lt;/span&gt; &lt;span class=&quot;ow&quot;&gt;in&lt;/span&gt; &lt;span class=&quot;n&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;[&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;'obfuscated'&lt;/span&gt;&lt;span class=&quot;p&quot;&gt;]]))&lt;/span&gt;
 
+```
+```scala
 
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;  &lt;/div&gt;
-  &lt;div class=&quot;language-scala highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
-&lt;span class=&quot;k&quot;&gt;import&lt;/span&gt; &lt;span class=&quot;nn&quot;&gt;com.johnsnowlabs.nlp.pretrained.PretrainedPipeline&lt;/span&gt;
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_pipeline&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;nc&quot;&gt;PretrainedPipeline&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;clinical_deidentification_docwise_wip_v2&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;en&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;,&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;clinical/models&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
+val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip_v2&quot;, &quot;en&quot;, &quot;clinical/models&quot;)
 
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;text&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;s&quot;&gt;&quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
+val text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago,  attended to the patient on 11/05/2024.
 The patient’s medical record number is 56467890.
-The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;&lt;/span&gt;
+The patient, Emma Wilson, is 50 years old,  her Contact number: 444-456-7890 .&quot;&quot;&quot;
 
-&lt;span class=&quot;k&quot;&gt;val&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_result&lt;/span&gt; &lt;span class=&quot;k&quot;&gt;=&lt;/span&gt; &lt;span class=&quot;nv&quot;&gt;deid_pipeline&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;.&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;fullAnnotate&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;n&quot;&gt;text&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;)&lt;/span&gt;
+val deid_result = deid_pipeline.fullAnnotate(text)
 
-&lt;span class=&quot;nf&quot;&gt;println&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;mask_entity&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;map&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;_&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;result&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;toString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;mkString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;))&lt;/span&gt;
-&lt;span class=&quot;nf&quot;&gt;println&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;deid_result&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;obfuscated&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;map&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;nf&quot;&gt;_&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;result&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;toString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;).&lt;/span&gt;&lt;span class=&quot;py&quot;&gt;mkString&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;(&lt;/span&gt;&lt;span class=&quot;s&quot;&gt;&quot;&quot;&lt;/span&gt;&lt;span class=&quot;o&quot;&gt;))&lt;/span&gt;
+println(deid_result(&quot;mask_entity&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;))
+println(deid_result(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;))
 
 
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;  &lt;/div&gt;
+```
 &lt;/div&gt;
 
-&lt;h2 id=&quot;results&quot;&gt;Results&lt;/h2&gt;
+## Results
+
+```bash
 
-&lt;div class=&quot;language-bash highlighter-rouge&quot;&gt;&lt;div class=&quot;highlight&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;
 Masked with entity labels
-&lt;span class=&quot;nt&quot;&gt;------------------------------&lt;/span&gt;
-Dr. &amp;lt;DOCTOR&amp;gt;, from &amp;lt;HOSPITAL&amp;gt; &lt;span class=&quot;k&quot;&gt;in&lt;/span&gt; &amp;lt;CITY&amp;gt;,  attended to the patient on &amp;lt;DATE&amp;gt;.
-The patient’s medical record number is &amp;lt;MEDICALRECORD&amp;gt;
-patient, &amp;lt;PATIENT&amp;gt;, is &amp;lt;AGE&amp;gt; years old,  her Contact number: &amp;lt;PHONE&amp;gt; &lt;span class=&quot;nb&quot;&gt;.&lt;/span&gt;
+------------------------------
+Dr. &lt;DOCTOR&gt;, from &lt;HOSPITAL&gt; in &lt;CITY&gt;,  attended to the patient on &lt;DATE&gt;.
+The patient’s medical record number is &lt;MEDICALRECORD&gt;
+patient, &lt;PATIENT&gt;, is &lt;AGE&gt; years old,  her Contact number: &lt;PHONE&gt; .
 
 Obfuscated
-&lt;span class=&quot;nt&quot;&gt;------------------------------&lt;/span&gt;
-Dr. Edwardo Graft, from MCBRIDE ORTHOPEDIC HOSPITAL &lt;span class=&quot;k&quot;&gt;in &lt;/span&gt;CLAMART,  attended to the patient on 14/06/2024.
+------------------------------
+Dr. Edwardo Graft, from MCBRIDE ORTHOPEDIC HOSPITAL in CLAMART,  attended to the patient on 14/06/2024.
 The patient’s medical record number is 78295621.
-The patient, Nathaneil Bakes, is 43 years old,  her Contact number: 308-657-8469 &lt;span class=&quot;nb&quot;&gt;.&lt;/span&gt;
-
-&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;/div&gt;
-
-&lt;h2 class=&quot;model-param&quot; id=&quot;model-information&quot;&gt;Model Information&lt;/h2&gt;
-
-&lt;table class=&quot;table-model&quot;&gt;
-  &lt;tbody&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Model Name:&lt;/td&gt;
-      &lt;td&gt;clinical_deidentification_docwise_wip_v2&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Type:&lt;/td&gt;
-      &lt;td&gt;pipeline&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Compatibility:&lt;/td&gt;
-      &lt;td&gt;Healthcare NLP 5.5.1+&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;License:&lt;/td&gt;
-      &lt;td&gt;Licensed&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Edition:&lt;/td&gt;
-      &lt;td&gt;Official&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Language:&lt;/td&gt;
-      &lt;td&gt;en&lt;/td&gt;
-    &lt;/tr&gt;
-    &lt;tr&gt;
-      &lt;td&gt;Size:&lt;/td&gt;
-      &lt;td&gt;2.5 GB&lt;/td&gt;
-    &lt;/tr&gt;
-  &lt;/tbody&gt;
-&lt;/table&gt;
-
-&lt;h2 id=&quot;included-models&quot;&gt;Included Models&lt;/h2&gt;
-
-&lt;ul&gt;
-  &lt;li&gt;DocumentAssembler&lt;/li&gt;
-  &lt;li&gt;InternalDocumentSplitter&lt;/li&gt;
-  &lt;li&gt;TokenizerModel&lt;/li&gt;
-  &lt;li&gt;WordEmbeddingsModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;MedicalNerModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;PretrainedZeroShotNER&lt;/li&gt;
-  &lt;li&gt;NerConverterInternalModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;ContextualParserModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;RegexMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;TextMatcherInternalModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;ChunkMergeModel&lt;/li&gt;
-  &lt;li&gt;LightDeIdentification&lt;/li&gt;
-  &lt;li&gt;LightDeIdentification&lt;/li&gt;
-&lt;/ul&gt;</content><author><name>John Snow Labs</name></author><category term="deidentification" /><category term="deid" /><category term="en" /><category term="licensed" /><category term="clinical" /><category term="pipeline" /><category term="docwise" /><summary type="html">Description This pipeline can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate LOCATION, CONTACT, PROFESSION, NAME, DATE, AGE, MEDICALRECORD, ORGANIZATION, HEALTHPLAN, DOCTOR, USERNAME, LOCATION-OTHER, URL, DEVICE, CITY, ZIP, STATE, PATIENT, COUNTRY, STREET, PHONE, HOSPITAL, EMAIL, IDNUM, BIOID, FAX, LOCATION_OTHER, DLN, SSN, ACCOUNT, PLATE, VIN, LICENSE, IP entities. Predicted Entities NAME, DATE, IDNUM, ZIP, SSN, ACCOUNT, LICENSE, AGE, PHONE, COUNTRY, STATE, CITY, PLATE, VIN, MEDICALRECORD, EMAIL, URL, LOCATION, PROFESSION, CONTACT, PATIENT, HOSPITAL, ORGANIZATION, STREET, DOCTOR Live Demo Open in Colab Download Copy S3 URI How to use PythonHealthcare NLPPythonJohnSnowLabsScalaNLU from sparknlp.pretrained import PretrainedPipeline deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip_v2&quot;, &quot;en&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.result for i in deid_result['mask_entity']])) print(''.join([i.result for i in deid_result['obfuscated']])) from sparknlp.pretrained import PretrainedPipeline deid_pipeline = nlp.PretrainedPipeline(&quot;clinical_deidentification_docwise_wip_v2&quot;, &quot;en&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.result for i in deid_result['mask_entity']])) print(''.join([i.result for i in deid_result['obfuscated']])) import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip_v2&quot;, &quot;en&quot;, &quot;clinical/models&quot;) val text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; val deid_result = deid_pipeline.fullAnnotate(text) println(deid_result(&quot;mask_entity&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) println(deid_result(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) Results Masked with entity labels ------------------------------ Dr. &amp;lt;DOCTOR&amp;gt;, from &amp;lt;HOSPITAL&amp;gt; in &amp;lt;CITY&amp;gt;, attended to the patient on &amp;lt;DATE&amp;gt;. The patient’s medical record number is &amp;lt;MEDICALRECORD&amp;gt; patient, &amp;lt;PATIENT&amp;gt;, is &amp;lt;AGE&amp;gt; years old, her Contact number: &amp;lt;PHONE&amp;gt; . Obfuscated ------------------------------ Dr. Edwardo Graft, from MCBRIDE ORTHOPEDIC HOSPITAL in CLAMART, attended to the patient on 14/06/2024. The patient’s medical record number is 78295621. The patient, Nathaneil Bakes, is 43 years old, her Contact number: 308-657-8469 . Model Information Model Name: clinical_deidentification_docwise_wip_v2 Type: pipeline Compatibility: Healthcare NLP 5.5.1+ License: Licensed Edition: Official Language: en Size: 2.5 GB Included Models DocumentAssembler InternalDocumentSplitter TokenizerModel WordEmbeddingsModel MedicalNerModel NerConverterInternalModel MedicalNerModel MedicalNerModel MedicalNerModel NerConverterInternalModel NerConverterInternalModel NerConverterInternalModel PretrainedZeroShotNER NerConverterInternalModel ChunkMergeModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel TextMatcherInternalModel TextMatcherInternalModel TextMatcherInternalModel ContextualParserModel RegexMatcherInternalModel ContextualParserModel ContextualParserModel ContextualParserModel RegexMatcherInternalModel RegexMatcherInternalModel RegexMatcherInternalModel TextMatcherInternalModel ChunkMergeModel ChunkMergeModel LightDeIdentification LightDeIdentification</summary></entry></feed>
\ No newline at end of file
+The patient, Nathaneil Bakes, is 43 years old,  her Contact number: 308-657-8469 .
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|clinical_deidentification_docwise_wip_v2|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 5.5.1+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+|Size:|2.5 GB|
+
+## Included Models
+
+- DocumentAssembler
+- InternalDocumentSplitter
+- TokenizerModel
+- WordEmbeddingsModel
+- MedicalNerModel
+- NerConverterInternalModel
+- MedicalNerModel
+- MedicalNerModel
+- MedicalNerModel
+- NerConverterInternalModel
+- NerConverterInternalModel
+- NerConverterInternalModel
+- PretrainedZeroShotNER
+- NerConverterInternalModel
+- ChunkMergeModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- TextMatcherInternalModel
+- TextMatcherInternalModel
+- TextMatcherInternalModel
+- ContextualParserModel
+- RegexMatcherInternalModel
+- ContextualParserModel
+- ContextualParserModel
+- ContextualParserModel
+- RegexMatcherInternalModel
+- RegexMatcherInternalModel
+- RegexMatcherInternalModel
+- TextMatcherInternalModel
+- ChunkMergeModel
+- ChunkMergeModel
+- LightDeIdentification
+- LightDeIdentification</content><author><name>John Snow Labs</name></author><category term="deidentification" /><category term="deid" /><category term="en" /><category term="licensed" /><category term="clinical" /><category term="pipeline" /><category term="docwise" /><summary type="html">Description This pipeline can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate LOCATION, CONTACT, PROFESSION, NAME, DATE, AGE, MEDICALRECORD, ORGANIZATION, HEALTHPLAN, DOCTOR, USERNAME, LOCATION-OTHER, URL, DEVICE, CITY, ZIP, STATE, PATIENT, COUNTRY, STREET, PHONE, HOSPITAL, EMAIL, IDNUM, BIOID, FAX, LOCATION_OTHER, DLN, SSN, ACCOUNT, PLATE, VIN, LICENSE, IP entities. Predicted Entities NAME, DATE, IDNUM, ZIP, SSN, ACCOUNT, LICENSE, AGE, PHONE, COUNTRY, STATE, CITY, PLATE, VIN, MEDICALRECORD, EMAIL, URL, LOCATION, PROFESSION, CONTACT, PATIENT, HOSPITAL, ORGANIZATION, STREET, DOCTOR Live Demo Open in Colab Download Copy S3 URI How to use PythonHealthcare NLPPythonJohnSnowLabsScalaNLU from sparknlp.pretrained import PretrainedPipeline deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip_v2&quot;, &quot;en&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.result for i in deid_result['mask_entity']])) print(''.join([i.result for i in deid_result['obfuscated']])) from sparknlp.pretrained import PretrainedPipeline deid_pipeline = nlp.PretrainedPipeline(&quot;clinical_deidentification_docwise_wip_v2&quot;, &quot;en&quot;, &quot;clinical/models&quot;) text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; deid_result = deid_pipeline.fullAnnotate(text) print(''.join([i.result for i in deid_result['mask_entity']])) print(''.join([i.result for i in deid_result['obfuscated']])) import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline val deid_pipeline = PretrainedPipeline(&quot;clinical_deidentification_docwise_wip_v2&quot;, &quot;en&quot;, &quot;clinical/models&quot;) val text = &quot;&quot;&quot;Dr. John Lee, from Royal Medical Clinic in Chicago, attended to the patient on 11/05/2024. The patient’s medical record number is 56467890. The patient, Emma Wilson, is 50 years old, her Contact number: 444-456-7890 .&quot;&quot;&quot; val deid_result = deid_pipeline.fullAnnotate(text) println(deid_result(&quot;mask_entity&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) println(deid_result(&quot;obfuscated&quot;).map(_(&quot;result&quot;).toString).mkString(&quot;&quot;)) Results Masked with entity labels ------------------------------ Dr. &amp;lt;DOCTOR&amp;gt;, from &amp;lt;HOSPITAL&amp;gt; in &amp;lt;CITY&amp;gt;, attended to the patient on &amp;lt;DATE&amp;gt;. The patient’s medical record number is &amp;lt;MEDICALRECORD&amp;gt; patient, &amp;lt;PATIENT&amp;gt;, is &amp;lt;AGE&amp;gt; years old, her Contact number: &amp;lt;PHONE&amp;gt; . Obfuscated ------------------------------ Dr. Edwardo Graft, from MCBRIDE ORTHOPEDIC HOSPITAL in CLAMART, attended to the patient on 14/06/2024. The patient’s medical record number is 78295621. The patient, Nathaneil Bakes, is 43 years old, her Contact number: 308-657-8469 . Model Information Model Name: clinical_deidentification_docwise_wip_v2 Type: pipeline Compatibility: Healthcare NLP 5.5.1+ License: Licensed Edition: Official Language: en Size: 2.5 GB Included Models DocumentAssembler InternalDocumentSplitter TokenizerModel WordEmbeddingsModel MedicalNerModel NerConverterInternalModel MedicalNerModel MedicalNerModel MedicalNerModel NerConverterInternalModel NerConverterInternalModel NerConverterInternalModel PretrainedZeroShotNER NerConverterInternalModel ChunkMergeModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel ContextualParserModel TextMatcherInternalModel TextMatcherInternalModel TextMatcherInternalModel ContextualParserModel RegexMatcherInternalModel ContextualParserModel ContextualParserModel ContextualParserModel RegexMatcherInternalModel RegexMatcherInternalModel RegexMatcherInternalModel TextMatcherInternalModel ChunkMergeModel ChunkMergeModel LightDeIdentification LightDeIdentification</summary></entry></feed>
\ No newline at end of file
diff --git a/sitemap.xml b/sitemap.xml
index c4671fca44..fd8f106bf2 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -18993,4666 +18993,4666 @@
 </url>
 <url>
 <loc>/assets/plugins/elegant_font/css/</loc>
-<lastmod>2024-12-10T16:44:33+00:00</lastmod>
+<lastmod>2024-12-10T20:58:50+00:00</lastmod>
 </url>
 <url>
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