Description
This model extracts terminology related to Social Determinants of Health from various kinds of biomedical documents.
Predicted Entities
Access_To_Care, Age, Alcohol, Chidhood_Event, Communicable_Disease, Community_Safety, Diet, Disability, Eating_Disorder, Education, Employment, Environmental_Condition, Exercise, Family_Member, Financial_Status, Food_Insecurity, Gender, Geographic_Entity, Healthcare_Institution, Housing, Hyperlipidemia, Hypertension, Income, Insurance_Status, Language, Legal_Issues, Marital_Status, Mental_Health, Obesity, Other_Disease, Other_SDoH_Keywords, Population_Group, Quality_Of_Life, Race_Ethnicity, Sexual_Activity, Sexual_Orientation, Smoking, Social_Exclusion, Social_Support, Spiritual_Beliefs, Substance_Duration, Substance_Frequency, Substance_Quantity, Substance_Use, Transportation, Violence_Or_Abuse
Live Demo Open in Colab Download Copy S3 URI
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_large", "en", "clinical/models")\
.setInputCols(["sentence", "token"])\
.setOutputCol("embeddings")
ner_model = MedicalNerModel.pretrained("ner_sdoh_emb_clinical_large_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 = [["Smith is a 55 years old, divorced Mexcian American woman with financial problems. She speaks spanish. She lives in an apartment. She has been struggling with diabetes for the past 10 years and has recently been experiencing frequent hospitalizations due to uncontrolled blood sugar levels. Smith works as a cleaning assistant and does not have access to health insurance or paid sick leave. She has a son student at college. Pt with likely long-standing depression. She is aware she needs rehab. Pt reprots having her catholic faith as a means of support as well. She has long history of etoh abuse, beginning in her teens. She reports she has been a daily drinker for 30 years, most recently drinking beer daily. She smokes a pack of cigarettes a day. She had DUI back in April and was due to be in court this week."]]
data = spark.createDataFrame(sample_texts).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_large", "en", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("embeddings")
val ner_model = MedicalNerModel.pretrained("ner_sdoh_emb_clinical_large_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("Smith is a 55 years old, divorced Mexcian American woman with financial problems. She speaks spanish. She lives in an apartment. She has been struggling with diabetes for the past 10 years and has recently been experiencing frequent hospitalizations due to uncontrolled blood sugar levels. Smith works as a cleaning assistant and does not have access to health insurance or paid sick leave. She has a son student at college. Pt with likely long-standing depression. She is aware she needs rehab. Pt reprots having her catholic faith as a means of support as well. She has long history of etoh abuse, beginning in her teens. She reports she has been a daily drinker for 30 years, most recently drinking beer daily. She smokes a pack of cigarettes a day. She had DUI back in April and was due to be in court this week.").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
Results
+------------------+-----+---+-------------------+
|chunk |begin|end|ner_label |
+------------------+-----+---+-------------------+
|55 years old |11 |22 |Age |
|divorced |25 |32 |Marital_Status |
|Mexcian |34 |40 |Race_Ethnicity |
|American |42 |49 |Race_Ethnicity |
|woman |51 |55 |Gender |
|financial problems|62 |79 |Financial_Status |
|She |82 |84 |Gender |
|spanish |93 |99 |Language |
|She |102 |104|Gender |
|apartment |118 |126|Housing |
|She |129 |131|Gender |
|diabetes |158 |165|Other_Disease |
|hospitalizations |233 |248|Other_SDoH_Keywords|
|cleaning assistant|307 |324|Employment |
|health insurance |354 |369|Insurance_Status |
|She |391 |393|Gender |
|son |401 |403|Family_Member |
|student |405 |411|Education |
|college |416 |422|Education |
|depression |454 |463|Mental_Health |
|She |466 |468|Gender |
|she |479 |481|Gender |
|rehab |489 |493|Access_To_Care |
|her |514 |516|Gender |
|catholic faith |518 |531|Spiritual_Beliefs |
|support |547 |553|Social_Support |
|She |565 |567|Gender |
|etoh abuse |589 |598|Alcohol |
|her |614 |616|Gender |
|teens |618 |622|Age |
|She |625 |627|Gender |
|she |637 |639|Gender |
|daily |652 |656|Substance_Quantity |
|drinker |658 |664|Alcohol |
|30 years |670 |677|Substance_Duration |
|drinking beer |694 |706|Alcohol |
|daily |708 |712|Substance_Frequency|
|She |715 |717|Gender |
|smokes |719 |724|Smoking |
|a pack |726 |731|Substance_Quantity |
|cigarettes |736 |745|Smoking |
|a day |747 |751|Substance_Frequency|
|She |754 |756|Gender |
|DUI |762 |764|Legal_Issues |
+------------------+-----+---+-------------------+
Model Information
| Model Name: | ner_sdoh_emb_clinical_large_wip |
| Compatibility: | Healthcare NLP 4.3.2+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 3.0 MB |
| Dependencies: | embeddings_clinical_large |
References
Internal SHOP Project
Benchmarking
label precision recall f1-score support
Employment 0.94 0.96 0.95 2075
Social_Support 0.91 0.90 0.90 658
Other_SDoH_Keywords 0.82 0.87 0.85 259
Healthcare_Institution 0.99 0.95 0.97 781
Alcohol 0.96 0.97 0.96 258
Gender 0.99 0.99 0.99 4957
Other_Disease 0.89 0.94 0.91 583
Access_To_Care 0.86 0.88 0.87 520
Mental_Health 0.89 0.81 0.85 494
Age 0.92 0.96 0.94 433
Marital_Status 1.00 1.00 1.00 92
Substance_Quantity 0.88 0.86 0.87 58
Substance_Use 0.91 0.97 0.94 192
Family_Member 0.97 0.99 0.98 2094
Financial_Status 0.86 0.65 0.74 124
Race_Ethnicity 0.93 0.93 0.93 27
Insurance_Status 0.93 0.87 0.90 85
Spiritual_Beliefs 0.86 0.81 0.83 52
Housing 0.88 0.85 0.87 400
Geographic_Entity 0.86 0.88 0.87 113
Disability 0.93 0.93 0.93 44
Quality_Of_Life 0.89 0.75 0.81 67
Income 0.89 0.77 0.83 31
Education 0.85 0.88 0.86 58
Transportation 0.86 0.89 0.88 57
Legal_Issues 0.72 0.91 0.80 47
Smoking 0.98 0.97 0.98 66
Substance_Frequency 0.93 0.75 0.83 57
Hypertension 1.00 1.00 1.00 21
Violence_Or_Abuse 0.83 0.62 0.71 63
Exercise 0.96 0.88 0.92 57
Diet 0.95 0.87 0.91 70
Sexual_Orientation 0.68 1.00 0.81 13
Language 0.89 0.73 0.80 22
Social_Exclusion 0.96 0.90 0.93 29
Substance_Duration 0.75 0.85 0.80 39
Communicable_Disease 1.00 0.84 0.91 31
Chidhood_Event 0.88 0.61 0.72 23
Community_Safety 0.95 0.93 0.94 44
Population_Group 0.89 0.62 0.73 13
Hyperlipidemia 0.78 1.00 0.88 7
Food_Insecurity 1.00 0.93 0.96 29
Eating_Disorder 0.67 0.92 0.77 13
Sexual_Activity 0.84 0.90 0.87 29
Environmental_Condition 1.00 1.00 1.00 20
Obesity 1.00 1.00 1.00 12
micro-avg 0.95 0.95 0.95 15217
macro-avg 0.90 0.88 0.88 15217
weighted-avg 0.95 0.95 0.95 15217