Extract Substance Usage Entities from Social Determinants of Health Texts

Description

This model extracts substance usage information related to Social Determinants of Health from various kinds of biomedical documents.

Predicted Entities

Smoking, Substance_Duration, Substance_Use, Substance_Quantity, Substance_Frequency, Alcohol

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How to use

document_assembler = DocumentAssembler()\
    .setInputCol("text")\
    .setOutputCol("document")

sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "en")\
    .setInputCols(["document"])\
    .setOutputCol("sentence")

tokenizer = Tokenizer()\
    .setInputCols(["sentence"])\
    .setOutputCol("token")

clinical_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
    .setInputCols(["sentence", "token"])\
    .setOutputCol("embeddings")

ner_model = MedicalNerModel.pretrained("ner_sdoh_substance_usage_wip", "en", "clinical/models")\
    .setInputCols(["sentence", "token", "embeddings"])\
    .setOutputCol("ner")

ner_converter = NerConverterInternal()\
    .setInputCols(["sentence", "token", "ner"])\
    .setOutputCol("ner_chunk")

pipeline = Pipeline(stages=[
    document_assembler, 
    sentence_detector,
    tokenizer,
    clinical_embeddings,
    ner_model,
    ner_converter   
    ])

sample_texts = ["He does drink occasional alcohol approximately 5 to 6 alcoholic drinks per month.",
             "He continues to smoke one pack of cigarettes daily, as he has for the past 28 years."]


data = spark.createDataFrame(sample_texts, StringType()).toDF("text")

result = pipeline.fit(data).transform(data)
val document_assembler = new DocumentAssembler()
    .setInputCol("text")
    .setOutputCol("document")

val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "en")
    .setInputCols("document")
    .setOutputCol("sentence")

val tokenizer = new Tokenizer()
    .setInputCols("sentence")
    .setOutputCol("token")

val clinical_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
    .setInputCols(Array("sentence", "token"))
    .setOutputCol("embeddings")

val ner_model = MedicalNerModel.pretrained("ner_sdoh_substance_usage_wip", "en", "clinical/models")
    .setInputCols(Array("sentence", "token", "embeddings"))
    .setOutputCol("ner")

val ner_converter = new NerConverterInternal()
    .setInputCols(Array("sentence", "token", "ner"))
    .setOutputCol("ner_chunk")

val pipeline = new Pipeline().setStages(Array(
    document_assembler, 
    sentence_detector,
    tokenizer,
    clinical_embeddings,
    ner_model,
    ner_converter   
))

val data = Seq("He does drink occasional alcohol approximately 5 to 6 alcoholic drinks per month.").toDS.toDF("text")

val result = pipeline.fit(data).transform(data)

Results

+----------------+-----+---+-------------------+
|chunk           |begin|end|ner_label          |
+----------------+-----+---+-------------------+
|drink           |8    |12 |Alcohol            |
|occasional      |14   |23 |Substance_Frequency|
|alcohol         |25   |31 |Alcohol            |
|5 to 6          |47   |52 |Substance_Quantity |
|alcoholic drinks|54   |69 |Alcohol            |
|per month       |71   |79 |Substance_Frequency|
|smoke           |16   |20 |Smoking            |
|one pack        |22   |29 |Substance_Quantity |
|cigarettes      |34   |43 |Smoking            |
|daily           |45   |49 |Substance_Frequency|
|past 28 years   |70   |82 |Substance_Duration |
+----------------+-----+---+-------------------+

Model Information

Model Name: ner_sdoh_substance_usage_wip
Compatibility: Healthcare NLP 5.3.3+
License: Licensed
Edition: Official
Input Labels: [sentence, token, embeddings]
Output Labels: [ner]
Language: en
Size: 3.0 MB

Benchmarking

              label	   tp	 fp	  fn	total	 precision	  recall	      f1
Substance_Frequency	 52.0	2.0	12.0	 64.0	  0.962963	0.812500	0.881356
            Smoking	 77.0	4.0	 2.0	 79.0	  0.950617	0.974684	0.962500
            Alcohol	327.0	8.0	15.0	342.0	  0.976119	0.956140	0.966027
 Substance_Quantity	 74.0	7.0	12.0	 86.0	  0.913580	0.860465	0.886228
 Substance_Duration	 27.0	7.0	14.0	 41.0	  0.794118	0.658537	0.720000
      Substance_Use	204.0	8.0	 6.0	210.0	  0.962264	0.971429	0.966825