2023-01-14-genericclassifier_sdoh_tobacco_usage_sbiobert_cased_mli_en

docs/_posts/Damla-Gurbaz/2023-01-14-genericclassifier_sdoh_alcohol_usage_binary_sbiobert_cased_mli_en.md

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+---
+layout: model
+title: SDOH Alcohol Usege For Binary Classification
+author: John Snow Labs
+name: genericclassifier_sdoh_alcohol_usage_binary_sbiobert_cased_mli
+date: 2023-01-14
+tags: [en, licensed, generic_classifier, sdoh, alcohol, clinical]
+task: Text Classification
+language: en
+edition: Healthcare NLP 4.2.4
+spark_version: 3.0
+supported: true
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This Generic Classifier model is intended for detecting alcohol use in clinical notes and trained by using GenericClassifierApproach annotator. `Present:` if the patient was a current consumer of alcohol or the patient was a consumer in the past and had quit. `Never:` if the patient had never consumed alcohol. `None: ` if there was no related text.
+
+## Predicted Entities
+
+`Present`, `Never`, `None`
+
+{:.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/genericclassifier_sdoh_alcohol_usage_binary_sbiobert_cased_mli_en_4.2.4_3.0_1673699002618.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+document_assembler = DocumentAssembler()\
+    .setInputCol("text")\
+    .setOutputCol("document")
+
+sentence_embeddings = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", 'en','clinical/models')\
+    .setInputCols(["document"])\
+    .setOutputCol("sentence_embeddings")
+
+features_asm = FeaturesAssembler()\
+    .setInputCols(["sentence_embeddings"])\
+    .setOutputCol("features")
+
+generic_classifier = GenericClassifierModel.pretrained("genericclassifier_sdoh_alcohol_usage_binary_sbiobert_cased_mli", 'en', 'clinical/models')\
+    .setInputCols(["features"])\
+    .setOutputCol("class")
+
+pipeline = Pipeline(stages=[
+    document_assembler,
+    sentence_embeddings,
+    features_asm,
+    generic_classifier    
+])
+
+text_list = ["Retired schoolteacher, now substitutes. Lives with wife in location 1439. Has a 27 yo son and a 25 yo daughter. He uses alcohol and cigarettes",
+             "Employee in neuro departmentin at the Center Hospital 18. Widower since 2001. Current smoker since 20 years. No EtOH or illicits.",
+             "Patient smoked 4 ppd x 37 years, quitting 22 years ago. He is widowed, lives alone, has three children."]
+
+df = spark.createDataFrame(text_list, StringType()).toDF("text")
+
+result = pipeline.fit(df).transform(df)
+
+result.select("text", "class.result").show(truncate=100)
+```
+```scala
+val document_assembler = new DocumentAssembler()
+    .setInputCol("text")
+    .setOutputCol("document")
+
+val sentence_embeddings = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "en", "clinical/models")
+    .setInputCols("document")
+    .setOutputCol("sentence_embeddings")
+
+val features_asm = new FeaturesAssembler()
+    .setInputCols("sentence_embeddings")
+    .setOutputCol("features")
+
+val generic_classifier = GenericClassifierModel.pretrained("genericclassifier_sdoh_alcohol_usage_binary_sbiobert_cased_mli", "en", "clinical/models")
+    .setInputCols("features")
+    .setOutputCol("class")
+
+val pipeline = new PipelineModel().setStages(Array(
+    document_assembler,
+    sentence_embeddings,
+    features_asm,
+    generic_classifier))
+
+val data = Seq("Retired schoolteacher, now substitutes. Lives with wife in location 1439. Has a 27 yo son and a 25 yo daughter. He uses alcohol and cigarettes.").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+</div>
+
+## Results
+
+```bash
++----------------------------------------------------------------------------------------------------+---------+
+|                                                                                                text|   result|
++----------------------------------------------------------------------------------------------------+---------+
+|Retired schoolteacher, now substitutes. Lives with wife in location 1439. Has a 27 yo son and a 2...|[Present]|
+|Employee in neuro departmentin at the Center Hospital 18. Widower since 2001. Current smoker sinc...|  [Never]|
+|Patient smoked 4 ppd x 37 years, quitting 22 years ago. He is widowed, lives alone, has three chi...|   [None]|
++----------------------------------------------------------------------------------------------------+---------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|genericclassifier_sdoh_alcohol_usage_binary_sbiobert_cased_mli|
+|Compatibility:|Healthcare NLP 4.2.4+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[features]|
+|Output Labels:|[prediction]|
+|Language:|en|
+|Size:|3.4 MB|
+
+## Benchmarking
+
+```bash
+       label  precision    recall  f1-score   support
+       Never       0.85      0.86      0.85       523
+        None       0.81      0.82      0.81       341
+     Present       0.88      0.86      0.87       516
+    accuracy        -         -        0.85      1380
+   macro-avg       0.85      0.85      0.85      1380
+weighted-avg       0.85      0.85      0.85      1380
+```

docs/_posts/Damla-Gurbaz/2023-01-14-genericclassifier_sdoh_alcohol_usage_sbiobert_cased_mli_en.md

@@ -0,0 +1,143 @@
+---
+layout: model
+title: SDOH Alcohol Usege For Classification
+author: John Snow Labs
+name: genericclassifier_sdoh_alcohol_usage_sbiobert_cased_mli
+date: 2023-01-14
+tags: [en, licensed, generic_classifier, sdoh, alcohol, clinical]
+task: Text Classification
+language: en
+edition: Healthcare NLP 4.2.4
+spark_version: 3.0
+supported: true
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This Generic Classifier model is intended for detecting alcohol use in clinical notes and trained by using GenericClassifierApproach annotator. `Present:` if the patient was a current consumer of alcohol. `Past:` the patient was a consumer in the past and had quit. `Never:` if the patient had never consumed alcohol. `None: ` if there was no related text.
+
+## Predicted Entities
+
+`Present`, `Past`, `Never`, `None`
+
+{:.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/genericclassifier_sdoh_alcohol_usage_sbiobert_cased_mli_en_4.2.4_3.0_1673698550774.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+document_assembler = DocumentAssembler()\
+    .setInputCol("text")\
+    .setOutputCol("document")
+
+sentence_embeddings = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", 'en','clinical/models')\
+    .setInputCols(["document"])\
+    .setOutputCol("sentence_embeddings")
+
+features_asm = FeaturesAssembler()\
+    .setInputCols(["sentence_embeddings"])\
+    .setOutputCol("features")
+
+generic_classifier = GenericClassifierModel.pretrained("genericclassifier_sdoh_alcohol_usage_sbiobert_cased_mli", 'en', 'clinical/models')\
+    .setInputCols(["features"])\
+    .setOutputCol("class")
+
+pipeline = Pipeline(stages=[
+    document_assembler,
+    sentence_embeddings,
+    features_asm,
+    generic_classifier    
+])
+
+text_list = ["Retired schoolteacher, now substitutes. Lives with wife in location 1439. Has a 27 yo son and a 25 yo daughter. He uses alcohol and cigarettes",
+             "The patient quit smoking approximately two years ago with an approximately a 40 pack year history, mostly cigar use. He also reports 'heavy alcohol use', quit 15 months ago.",
+             "Employee in neuro departmentin at the Center Hospital 18. Widower since 2001. Current smoker since 20 years. No EtOH or illicits.",
+             "Patient smoked 4 ppd x 37 years, quitting 22 years ago. He is widowed, lives alone, has three children."]         
+
+df = spark.createDataFrame(text_list, StringType()).toDF("text")
+
+result = pipeline.fit(df).transform(df)
+
+result.select("text", "class.result").show(truncate=100)
+```
+```scala
+val document_assembler = new DocumentAssembler()
+    .setInputCol("text")
+    .setOutputCol("document")
+
+val sentence_embeddings = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "en", "clinical/models")
+    .setInputCols("document")
+    .setOutputCol("sentence_embeddings")
+
+val features_asm = new FeaturesAssembler()
+    .setInputCols("sentence_embeddings")
+    .setOutputCol("features")
+
+val generic_classifier = GenericClassifierModel.pretrained("genericclassifier_sdoh_alcohol_usage_sbiobert_cased_mli", "en", "clinical/models")
+    .setInputCols("features")
+    .setOutputCol("class")
+
+val pipeline = new PipelineModel().setStages(Array(
+    document_assembler,
+    sentence_embeddings,
+    features_asm,
+    generic_classifier))
+
+val data = Seq("Retired schoolteacher, now substitutes. Lives with wife in location 1439. Has a 27 yo son and a 25 yo daughter. He uses alcohol and cigarettes.").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+</div>
+
+## Results
+
+```bash
+
++----------------------------------------------------------------------------------------------------+---------+
+|                                                                                                text|   result|
++----------------------------------------------------------------------------------------------------+---------+
+|Retired schoolteacher, now substitutes. Lives with wife in location 1439. Has a 27 yo son and a 2...|[Present]|
+|The patient quit smoking approximately two years ago with an approximately a 40 pack year history...|   [Past]|
+|Employee in neuro departmentin at the Center Hospital 18. Widower since 2001. Current smoker sinc...|  [Never]|
+|Patient smoked 4 ppd x 37 years, quitting 22 years ago. He is widowed, lives alone, has three chi...|   [None]|
++----------------------------------------------------------------------------------------------------+---------+
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|genericclassifier_sdoh_alcohol_usage_sbiobert_cased_mli|
+|Compatibility:|Healthcare NLP 4.2.4+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[features]|
+|Output Labels:|[prediction]|
+|Language:|en|
+|Size:|3.5 MB|
+
+## Benchmarking
+
+```bash
+
+       label  precision    recall  f1-score   support
+       Never       0.84      0.87      0.85       523
+        None       0.83      0.74      0.81       341
+        Past       0.51      0.35      0.50        98
+     Present       0.74      0.83      0.79       418
+    accuracy        -         -        0.79      1380
+   macro-avg       0.73      0.70      0.71      1380
+weighted-avg       0.78      0.79      0.78      1380
+
+```