2024-12-19-medication_resolver_transform_pipeline_en

docs/_posts/akrztrk/2024-12-19-medication_resolver_pipeline_en.md

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+---
+layout: model
+title: Pipeline to Resolve Medication Codes
+author: John Snow Labs
+name: medication_resolver_pipeline
+date: 2024-12-19
+tags: [licensed, en, resolver, snomed, umls, rxnorm, ndc, ade, pipeline]
+task: [Pipeline Healthcare, Named Entity Recognition]
+language: en
+edition: Healthcare NLP 5.5.1
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+A pretrained resolver pipeline to extract medications and resolve their adverse reactions (ADE), RxNorm, UMLS, NDC, SNOMED CT codes, and action/treatments in clinical text.
+
+Action/treatments are available for branded medication, and SNOMED codes are available for non-branded medication.
+
+This pipeline can be used as Lightpipeline (with `annotate/fullAnnotate`). You can use `medication_resolver_transform_pipeline` for Spark transform.
+
+## Predicted Entities
+
+`DRUG`
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/medication_resolver_pipeline_en_5.5.1_3.0_1734639447518.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/medication_resolver_pipeline_en_5.5.1_3.0_1734639447518.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+
+```python
+
+from sparknlp.pretrained import PretrainedPipeline
+
+ner_pipeline = PretrainedPipeline("medication_resolver_pipeline", "en", "clinical/models")
+
+result = ner_pipeline.annotate("""The patient was prescribed Amlodopine Vallarta 10-320mg, Eviplera.
+The other patient is given Lescol 40 MG and Everolimus 1.5 mg tablet.
+""")
+
+```
+
+{:.jsl-block}
+```python
+
+ner_pipeline = nlp.PretrainedPipeline("medication_resolver_pipeline", "en", "clinical/models")
+
+result = ner_pipeline.annotate("""The patient was prescribed Amlodopine Vallarta 10-320mg, Eviplera.
+The other patient is given Lescol 40 MG and Everolimus 1.5 mg tablet.
+""")
+
+```
+```scala
+
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val ner_pipeline = PretrainedPipeline("medication_resolver_pipeline", "en", "clinical/models")
+
+val result = ner_pipeline.annotate("""The patient was prescribed Amlodopine Vallarta 10-320mg, Eviplera.
+The other patient is given Lescol 40 MG and Everolimus 1.5 mg tablet.
+""")
+
+```
+</div>
+
+## Results
+
+```bash
+
+
++----------------------------+---------+---------------------------+-------+--------------------------+------------------------------------------+--------+----------------+-----------+-------------+
+|chunk                       |ner_label|ADE                        |RxNorm |Action                    |Treatment                                 |UMLS    |SNOMED_CT       |NDC_Product|NDC_Package  |
++----------------------------+---------+---------------------------+-------+--------------------------+------------------------------------------+--------+----------------+-----------+-------------+
+|Amlodopine Vallarta 10-320mg|DRUG     |Gynaecomastia              |722131 |NONE                      |NONE                                      |C1949334|1153435009      |00093-7693 |00093-7693-56|
+|Eviplera                    |DRUG     |Anxiety                    |217010 |Inhibitory Bone Resorption|Osteoporosis                              |C0720318|NONE            |NONE       |NONE         |
+|Lescol 40 MG                |DRUG     |NONE                       |103919 |Hypocholesterolemic       |Heterozygous Familial Hypercholesterolemia|C0353573|NONE            |00078-0234 |00078-0234-05|
+|Everolimus 1.5 mg tablet    |DRUG     |Acute myocardial infarction|2056895|NONE                      |NONE                                      |C4723581|1029521000202102|00054-0604 |00054-0604-21|
++----------------------------+---------+---------------------------+-------+--------------------------+------------------------------------------+--------+----------------+-----------+-------------+
+
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|medication_resolver_pipeline|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 5.5.1+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+|Size:|3.3 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetectorDLModel
+- TokenizerModel
+- WordEmbeddingsModel
+- MedicalNerModel
+- NerConverterInternalModel
+- TextMatcherInternalModel
+- ChunkMergeModel
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperFilterer
+- Chunk2Doc
+- BertSentenceEmbeddings
+- SentenceEntityResolverModel
+- ResolverMerger
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperModel
+- Finisher

docs/_posts/akrztrk/2024-12-19-medication_resolver_transform_pipeline_en.md

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+---
+layout: model
+title: Pipeline to Resolve Medication Codes(Transform)
+author: John Snow Labs
+name: medication_resolver_transform_pipeline
+date: 2024-12-19
+tags: [licensed, en, resolver, snomed, umls, rxnorm, ndc, ade, pipeline]
+task: [Pipeline Healthcare, Named Entity Recognition]
+language: en
+edition: Healthcare NLP 5.5.1
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+A pretrained resolver pipeline to extract medications and resolve their adverse reactions (ADE), RxNorm, UMLS, NDC, SNOMED CT codes, and action/treatments in clinical text.
+
+Action/treatments are available for branded medication, and SNOMED codes are available for non-branded medication.
+
+This pipeline can be used with Spark transform. You can use `medication_resolver_pipeline` as Lightpipeline (with `annotate/fullAnnotate`).
+
+## Predicted Entities
+
+`DRUG`
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/medication_resolver_transform_pipeline_en_5.5.1_3.0_1734635642246.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/medication_resolver_transform_pipeline_en_5.5.1_3.0_1734635642246.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+
+```python
+
+from sparknlp.pretrained import PretrainedPipeline
+
+medication_resolver_pipeline = PretrainedPipeline("medication_resolver_transform_pipeline", "en", "clinical/models")
+
+text = """The patient was prescribed Amlodopine Vallarta 10-320mg, Eviplera.
+The other patient is given Lescol 40 MG and Everolimus 1.5 mg tablet."""
+
+data = spark.createDataFrame([[text]]).toDF("text")
+
+result = medication_resolver_pipeline.transform(data)
+
+```
+
+{:.jsl-block}
+```python
+
+medication_resolver_pipeline = nlp.PretrainedPipeline("medication_resolver_transform_pipeline", "en", "clinical/models")
+
+text = """The patient was prescribed Amlodopine Vallarta 10-320mg, Eviplera.
+The other patient is given Lescol 40 MG and Everolimus 1.5 mg tablet."""
+
+data = spark.createDataFrame([[text]]).toDF("text")
+
+result = medication_resolver_pipeline.transform(data)
+
+
+```
+```scala
+
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val medication_resolver_pipeline = new PretrainedPipeline("medication_resolver_transform_pipeline", "en", "clinical/models")
+
+val data = Seq("""The patient was prescribed Amlodopine Vallarta 10-320mg, Eviplera.
+The other patient is given Lescol 40 MG and Everolimus 1.5 mg tablet.""").toDS.toDF("text")
+
+val result = medication_resolver_pipeline.fit(data).transform(data)
+
+```
+</div>
+
+## Results
+
+```bash
+
+
++----------------------------+---------+---------------------------+-------+--------------------------+------------------------------------------+--------+----------------+-----------+-------------+
+|chunk                       |ner_label|ADE                        |RxNorm |Action                    |Treatment                                 |UMLS    |SNOMED_CT       |NDC_Product|NDC_Package  |
++----------------------------+---------+---------------------------+-------+--------------------------+------------------------------------------+--------+----------------+-----------+-------------+
+|Amlodopine Vallarta 10-320mg|DRUG     |Gynaecomastia              |722131 |NONE                      |NONE                                      |C1949334|1153435009      |00093-7693 |00093-7693-56|
+|Eviplera                    |DRUG     |Anxiety                    |217010 |Inhibitory Bone Resorption|Osteoporosis                              |C0720318|NONE            |NONE       |NONE         |
+|Lescol 40 MG                |DRUG     |NONE                       |103919 |Hypocholesterolemic       |Heterozygous Familial Hypercholesterolemia|C0353573|NONE            |00078-0234 |00078-0234-05|
+|Everolimus 1.5 mg tablet    |DRUG     |Acute myocardial infarction|2056895|NONE                      |NONE                                      |C4723581|1029521000202102|00054-0604 |00054-0604-21|
++----------------------------+---------+---------------------------+-------+--------------------------+------------------------------------------+--------+----------------+-----------+-------------+
+
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|medication_resolver_transform_pipeline|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 5.5.1+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+|Size:|3.3 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetectorDLModel
+- TokenizerModel
+- WordEmbeddingsModel
+- MedicalNerModel
+- NerConverterInternalModel
+- TextMatcherInternalModel
+- ChunkMergeModel
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperFilterer
+- Chunk2Doc
+- BertSentenceEmbeddings
+- SentenceEntityResolverModel
+- ResolverMerger
+- Doc2Chunk
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperModel
+- Doc2Chunk
+- ChunkMapperModel
+- Finisher

docs/_posts/akrztrk/2024-12-23-snomed_multi_mapper_pipeline_en.md

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+---
+layout: model
+title: SNOMED Code Mapping Pipeline
+author: John Snow Labs
+name: snomed_multi_mapper_pipeline
+date: 2024-12-23
+tags: [licensed, en, snomed, pipeline]
+task: Pipeline Healthcare
+language: en
+edition: Healthcare NLP 5.5.1
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline maps SNOMED codes to their corresponding ICD-10, ICD-O, and UMLS codes.
+
+## Predicted Entities
+
+`snomed_code`
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/snomed_multi_mapper_pipeline_en_5.5.1_3.0_1734953947952.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/snomed_multi_mapper_pipeline_en_5.5.1_3.0_1734953947952.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+
+```python
+
+from sparknlp.pretrained import PretrainedPipeline
+
+mapper_pipeline = PretrainedPipeline("snomed_multi_mapper_pipeline", "en", "clinical/models")
+
+result = mapper_pipeline.fullAnnotate(["10000006", "128501000"])
+
+```
+
+{:.jsl-block}
+```python
+
+mapper_pipeline = nlp.PretrainedPipeline("snomed_multi_mapper_pipeline", "en", "clinical/models")
+
+result = mapper_pipeline.fullAnnotate(["10000006", "128501000"])
+
+```
+```scala
+
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val mapper_pipeline = PretrainedPipeline("snomed_multi_mapper_pipeline", "en", "clinical/models")
+
+val result = mapper_pipeline.fullAnnotate(["10000006", "128501000"])
+
+```
+</div>
+
+## Results
+
+```bash
+
++-----------+------------+---------+---------+
+|snomed_code|icd10cm_code|icdo_code|umls_code|
++-----------+------------+---------+---------+
+|   10000006|       R07.9|     NONE| C0232289|
+|  128501000|        NONE|    C49.5| C0448606|
++-----------+------------+---------+---------+
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|snomed_multi_mapper_pipeline|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 5.5.1+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+|Size:|9.4 MB|
+
+## Included Models
+
+- DocumentAssembler
+- DocMapperModel
+- DocMapperModel
+- DocMapperModel