Models hub internal to Main (#838)

* 2023-12-21-bert_sequence_classifier_clinical_sections_en (#834) * added missing files --------- Co-authored-by: jsl-models <[email protected]>
JohnSnowLabs · Dec 25, 2023 · b31f678 · b31f678
1 parent cba80d0
commit b31f678
Show file tree

Hide file tree

Showing 5 changed files with 848 additions and 0 deletions.
diff --git a/...ts/akrztrk/2023-12-20-bert_sequence_classifier_sdoh_frailty_vulnerability_en.md b/...ts/akrztrk/2023-12-20-bert_sequence_classifier_sdoh_frailty_vulnerability_en.md
@@ -0,0 +1,151 @@
+---
+layout: model
+title: Social Determinants of Healthcare for Frailty and Vulnerability Classifier
+author: John Snow Labs
+name: bert_sequence_classifier_sdoh_frailty_vulnerability
+date: 2023-12-20
+tags: [sdoh, en, clinical, social_determinants_of_heathcare, public_health, frailty, vulnerability, licensed, tensorflow]
+task: Text Classification
+language: en
+edition: Healthcare NLP 5.1.4
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: MedicalBertForSequenceClassification
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The Fraitly classifier employs [MedicalBertForSequenceClassification embeddings](https://sparknlp.org/2022/07/18/biobert_pubmed_base_cased_v1.2_en_3_0.html) within a robust classifier architecture. Trained on a diverse dataset, this model provides accurate label assignments and confidence scores for its predictions. The primary goal of this model is to categorize text into two key labels: `Frailty_Vulnerability` and `No_Or_Unknown`.
+
+- `Frailty_Vulnerability`: This category includes statements that highlight concerns, signs or symptoms associated with frailty and/or vulnerability conditions.
+
+- `No_Or_Unknown`: This category encompasses statements that either do not present any identifiable concerns related to frailty/vulnerability or where the presence or extent of frailty/ vulnerability is indeterminate.
+
+## Predicted Entities
+
+`Frailty_Vulnerability`, `No_Or_Unknown`
+
+{:.btn-box}
+[Live Demo](https://demo.johnsnowlabs.com/healthcare/SOCIAL_DETERMINANT_SEQUENCE_CLASSIFICATION/){:.button.button-orange}
+[Open in Colab](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/streamlit_notebooks/healthcare/SOCIAL_DETERMINANT_CLASSIFICATION.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/bert_sequence_classifier_sdoh_frailty_vulnerability_en_5.1.4_3.0_1703084816116.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/bert_sequence_classifier_sdoh_frailty_vulnerability_en_5.1.4_3.0_1703084816116.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
+document_assembler = DocumentAssembler()\
+    .setInputCol("text")\
+    .setOutputCol("document")
+
+tokenizer = Tokenizer()\
+    .setInputCols(["document"])\
+    .setOutputCol("token")
+
+sequenceClassifier = MedicalBertForSequenceClassification.pretrained("bert_sequence_classifier_sdoh_frailty_vulnerability", "en", "clinical/models")\
+    .setInputCols(["document","token"])\
+    .setOutputCol("prediction")
+
+pipeline = Pipeline(
+        stages=[
+            document_assembler,
+            tokenizer,
+            sequenceClassifier
+                ])
+
+sample_texts = [
+               ["Patient B is a 40-year-old female who was diagnosed with breast cancer. She has received a treatment plan that includes surgery, chemotherapy, and radiation therapy."],
+               ["Post-chemotherapy, the patient was under regular surveillance for osteosarcoma. Recent imaging showed no signs of local recurrence or distant metastasis. Whereas the recovery was challenging, current evaluation confirms patient is in remission."],
+               ["The patient was diagnosed with stage II colon cancer and will be undergoing a treatment regimen that includes both chemotherapy and radiation therapy."],
+               ["Thyroid nodules detected during routine examination; fine-needle aspiration was conducted. Cytology results indicated no malignancy, consistent with a benign thyroid adenoma. However, patient is advised for a follow-up ultrasound in 12 months to monitor nodule size."],
+               ["The patient's persistent lymphadenopathy led to further tests, which confirmed a diagnosis of AIDS."],
+               ["Female patient presented with pelvic discomfort. Ovarian cysts were found during ultrasound; however, CA-125 levels are within normal range, and repeat imaging has shown consistent cyst size. No features of ovarian cancer were present, and a follow-up is scheduled in six months."]
+               ]
+
+sample_data = spark.createDataFrame(sample_texts).toDF("text")
+
+result = pipeline.fit(sample_data).transform(sample_data)
+
+result.select("text", "prediction.result").show(truncate=100)
+```
+```scala
+val documenter = new DocumentAssembler() 
+    .setInputCol("text") 
+    .setOutputCol("document")
+
+val tokenizer = new Tokenizer()
+    .setInputCols("document")
+    .setOutputCol("token")
+
+val sequenceClassifier = MedicalBertForSequenceClassification.pretrained("bert_sequence_classifier_sdoh_frailty_vulnerability", "en", "clinical/models")
+    .setInputCols(Array("document","token"))
+    .setOutputCol("prediction")
+
+val pipeline = new Pipeline().setStages(Array(documenter, tokenizer, sequenceClassifier))
+
+val data = Seq(Array("Patient B is a 40-year-old female who was diagnosed with breast cancer. She has received a treatment plan that includes surgery, chemotherapy, and radiation therapy.",
+                     "Post-chemotherapy, the patient was under regular surveillance for osteosarcoma. Recent imaging showed no signs of local recurrence or distant metastasis. Whereas the recovery was challenging, current evaluation confirms patient is in remission.",
+                     "The patient was diagnosed with stage II colon cancer and will be undergoing a treatment regimen that includes both chemotherapy and radiation therapy.",
+                     "Thyroid nodules detected during routine examination; fine-needle aspiration was conducted. Cytology results indicated no malignancy, consistent with a benign thyroid adenoma. However, patient is advised for a follow-up ultrasound in 12 months to monitor nodule size.",
+                     "The patient's persistent lymphadenopathy led to further tests, which confirmed a diagnosis of AIDS.",
+                     "Female patient presented with pelvic discomfort. Ovarian cysts were found during ultrasound; however, CA-125 levels are within normal range, and repeat imaging has shown consistent cyst size. No features of ovarian cancer were present, and a follow-up is scheduled in six months."
+                    )).toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+</div>
+
+## Results
+
+```bash
++----------------------------------------------------------------------------------------------------+-----------------------+
+|                                                                                                text|                 result|
++----------------------------------------------------------------------------------------------------+-----------------------+
+|Patient B is a 40-year-old female who was diagnosed with breast cancer. She has received a treatm...|[Frailty_Vulnerability]|
+|Post-chemotherapy, the patient was under regular surveillance for osteosarcoma. Recent imaging sh...|        [No_Or_Unknown]|
+|The patient was diagnosed with stage II colon cancer and will be undergoing a treatment regimen t...|[Frailty_Vulnerability]|
+|Thyroid nodules detected during routine examination; fine-needle aspiration was conducted. Cytolo...|        [No_Or_Unknown]|
+| The patient's persistent lymphadenopathy led to further tests, which confirmed a diagnosis of AIDS.|[Frailty_Vulnerability]|
+|Female patient presented with pelvic discomfort. Ovarian cysts were found during ultrasound; howe...|        [No_Or_Unknown]|
++----------------------------------------------------------------------------------------------------+-----------------------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bert_sequence_classifier_sdoh_frailty_vulnerability|
+|Compatibility:|Healthcare NLP 5.1.4+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[document, token]|
+|Output Labels:|[prediction]|
+|Language:|en|
+|Size:|406.4 MB|
+|Case sensitive:|false|
+|Max sentence length:|512|
+
+## References
+
+Trained with the in-house dataset
+
+## Benchmarking
+
+```bash
+                label  precision    recall  f1-score   support
+Frailty_Vulnerability   0.982014  0.971530  0.976744       281
+        No_Or_Unknown   0.960976  0.975248  0.968059       202
+             accuracy          -         -  0.973085       483
+            macro-avg   0.971495  0.973389  0.972402       483
+         weighted-avg   0.973216  0.973085  0.973112       483
+```
diff --git a/docs/_posts/akrztrk/2023-12-20-bert_sequence_classifier_sdoh_mental_health_en.md b/docs/_posts/akrztrk/2023-12-20-bert_sequence_classifier_sdoh_mental_health_en.md
@@ -0,0 +1,151 @@
+---
+layout: model
+title: Social Determinants of Healthcare for Mental Health Classifier
+author: John Snow Labs
+name: bert_sequence_classifier_sdoh_mental_health
+date: 2023-12-20
+tags: [sdoh, en, clinical, social_determinants_of_heathcare, public_health, mental_health, licensed, tensorflow]
+task: Text Classification
+language: en
+edition: Healthcare NLP 5.1.4
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: MedicalBertForSequenceClassification
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The Mental Health classifier employs [MedicalBertForSequenceClassification embeddings](https://sparknlp.org/2022/07/18/biobert_pubmed_base_cased_v1.2_en_3_0.html) within a robust classifier architecture. Trained on a diverse dataset, this model provides accurate label assignments and confidence scores for its predictions. The primary goal of this model is to categorize text into two key labels: `Mental_Disorder` and `No_Or_Not_Mentioned`.
+
+- `Mental_Disorder`: It encompasses a wide range of mental health conditions that affect a person's mood, thinking, behavior, and overall psychological well-being.
+
+- `No_Or_Not_Mentioned`: The patient doesn’t have mental health problems or it is not mentioned in the clinical notes.
+
+## Predicted Entities
+
+`Mental_Disorder`, `No_Or_Not_Mentioned`
+
+{:.btn-box}
+[Live Demo](https://demo.johnsnowlabs.com/healthcare/SOCIAL_DETERMINANT_SEQUENCE_CLASSIFICATION/){:.button.button-orange}
+[Open in Colab](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/streamlit_notebooks/healthcare/SOCIAL_DETERMINANT_CLASSIFICATION.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/bert_sequence_classifier_sdoh_mental_health_en_5.1.4_3.0_1703076463310.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/bert_sequence_classifier_sdoh_mental_health_en_5.1.4_3.0_1703076463310.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
+document_assembler = DocumentAssembler()\
+    .setInputCol("text")\
+    .setOutputCol("document")
+
+tokenizer = Tokenizer()\
+    .setInputCols(["document"])\
+    .setOutputCol("token")
+
+sequenceClassifier = MedicalBertForSequenceClassification.pretrained("bert_sequence_classifier_sdoh_mental_health", "en", "clinical/models")\
+    .setInputCols(["document", "token"])\
+    .setOutputCol("prediction")
+
+pipeline = Pipeline(
+    stages=[
+        document_assembler,
+        tokenizer,
+        sequenceClassifier
+        ])
+
+sample_texts= [
+  ["John, a 45-year-old man, was diagnosed with bipolar disorder, a mental disorder characterized by alternating periods of elevated mood (mania) and depression. His treatment plan involved a combination of mood stabilizing medication and regular therapy sessions. With proper management and support, John learned to better understand and cope with his condition, leading to improved stability and overall well-being."],
+  ["Lisa, a 28-year-old woman, was diagnosed with generalized anxiety disorder (GAD), a mental disorder characterized by excessive worry and persistent anxiety."],
+  ["Mark, a 35-year-old man, sought medical help for symptoms of attention-deficit/hyperactivity disorder (ADHD), a neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity. After a comprehensive evaluation, Mark was diagnosed with ADHD, and his healthcare provider recommended a multimodal treatment approach. "],
+  ["Patient B is a 40-year-old female who was diagnosed with breast cancer. She has received a treatment plan that includes surgery, chemotherapy, and radiation therapy."],
+  ["She reported occasional respiratory symptoms, such as wheezing and shortness of breath, but had no signs of a mental disorder. Her healthcare provider assessed her lung function, reviewed her medication regimen, and provided personalized asthma education. "],
+  ["During the appointment, her healthcare provider assessed her joint function, reviewed her medication regimen, and discussed the importance of adherence. They also discussed the benefits of regular exercise, maintaining a healthy weight, and using assistive devices when needed to support Anna's joint health. "],       
+]
+
+sample_data = spark.createDataFrame(sample_texts).toDF("text")
+
+result = pipeline.fit(sample_data).transform(sample_data)
+
+result.select("text", "prediction.result").show(truncate=100)
+```
+```scala
+val documenter = new DocumentAssembler() 
+    .setInputCol("text") 
+    .setOutputCol("document")
+
+val tokenizer = new Tokenizer()
+    .setInputCols("document")
+    .setOutputCol("token")
+
+val sequenceClassifier = MedicalBertForSequenceClassification.pretrained("bert_sequence_classifier_sdoh_mental_health", "en", "clinical/models")
+    .setInputCols(Array("document", "token"))
+    .setOutputCol("prediction")
+
+val pipeline = new Pipeline().setStages(Array(documenter, tokenizer, sequenceClassifier))
+
+val data = Seq(Array("John, a 45-year-old man, was diagnosed with bipolar disorder, a mental disorder characterized by alternating periods of elevated mood (mania) and depression. His treatment plan involved a combination of mood stabilizing medication and regular therapy sessions. With proper management and support, John learned to better understand and cope with his condition, leading to improved stability and overall well-being.",
+                     "Lisa, a 28-year-old woman, was diagnosed with generalized anxiety disorder (GAD), a mental disorder characterized by excessive worry and persistent anxiety.",
+                     "Mark, a 35-year-old man, sought medical help for symptoms of attention-deficit/hyperactivity disorder (ADHD), a neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity. After a comprehensive evaluation, Mark was diagnosed with ADHD, and his healthcare provider recommended a multimodal treatment approach. ",
+                     "Patient B is a 40-year-old female who was diagnosed with breast cancer. She has received a treatment plan that includes surgery, chemotherapy, and radiation therapy.",
+                     "She reported occasional respiratory symptoms, such as wheezing and shortness of breath, but had no signs of a mental disorder. Her healthcare provider assessed her lung function, reviewed her medication regimen, and provided personalized asthma education. ",
+                     "During the appointment, her healthcare provider assessed her joint function, reviewed her medication regimen, and discussed the importance of adherence. They also discussed the benefits of regular exercise, maintaining a healthy weight, and using assistive devices when needed to support Anna's joint health. ",
+)).toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+</div>
+
+## Results
+
+```bash
++----------------------------------------------------------------------------------------------------+---------------------+
+|                                                                                                text|               result|
++----------------------------------------------------------------------------------------------------+---------------------+
+|John, a 45-year-old man, was diagnosed with bipolar disorder, a mental disorder characterized by ...|    [Mental_Disorder]|
+|Lisa, a 28-year-old woman, was diagnosed with generalized anxiety disorder (GAD), a mental disord...|    [Mental_Disorder]|
+|Mark, a 35-year-old man, sought medical help for symptoms of attention-deficit/hyperactivity diso...|    [Mental_Disorder]|
+|Patient B is a 40-year-old female who was diagnosed with breast cancer. She has received a treatm...|[No_Or_Not_Mentioned]|
+|She reported occasional respiratory symptoms, such as wheezing and shortness of breath, but had n...|[No_Or_Not_Mentioned]|
+|During the appointment, her healthcare provider assessed her joint function, reviewed her medicat...|[No_Or_Not_Mentioned]|
++----------------------------------------------------------------------------------------------------+---------------------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bert_sequence_classifier_sdoh_mental_health|
+|Compatibility:|Healthcare NLP 5.1.4+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[document, token]|
+|Output Labels:|[prediction]|
+|Language:|en|
+|Size:|406.4 MB|
+|Case sensitive:|false|
+|Max sentence length:|512|
+
+## References
+
+Trained with the in-house dataset
+
+## Benchmarking
+
+```bash
+              label  precision    recall  f1-score   support
+    Mental_Disorder   0.903226  0.845921  0.873635       331
+No_Or_Not_Mentioned   0.923653  0.953632  0.938403       647
+           accuracy          -         -  0.917178       978
+          macro-avg   0.913439  0.899777  0.906019       978
+       weighted-avg   0.916739  0.917178  0.916483       978
+```