Description
This model extracts terminology related to Social Determinants of Health from various kinds of biomedical documents.
Predicted Entities
Other_SDoH_Keywords, Education, Population_Group, Quality_Of_Life, Housing, Substance_Frequency, Smoking, Eating_Disorder, Obesity, Healthcare_Institution, Financial_Status, Age, Chidhood_Event, Exercise, Communicable_Disease, Hypertension, Other_Disease, Violence_Or_Abuse, Spiritual_Beliefs, Employment, Social_Exclusion, Access_To_Care, Marital_Status, Diet, Social_Support, Disability, Mental_Health, Alcohol, Insurance_Status, Substance_Quantity, Hyperlipidemia, Family_Member, Legal_Issues, Race_Ethnicity, Gender, Geographic_Entity, Sexual_Orientation, Transportation, Sexual_Activity, Language, Substance_Use
Live Demo Open in Colab 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", "en", "clinical/models")\
.setInputCols(["sentence", "token"])\
.setOutputCol("embeddings")
ner_model = MedicalNerModel.pretrained("ner_sdoh_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", "en", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("embeddings")
val ner_model = MedicalNerModel.pretrained("ner_sdoh_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 continues to smoke one pack of cigarettes daily, as he has for the past 28 years.").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 American |34 |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 |
|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 |
|drinker |658 |664|Alcohol |
|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_wip |
| Compatibility: | Healthcare NLP 4.2.8+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 1.5 MB |
Benchmarking
label tp fp fn total precision recall f1
Other_SDoH_Keywords 317.0 84.0 112.0 429.0 0.790524 0.738928 0.763855
Education 116.0 38.0 31.0 147.0 0.753247 0.789116 0.770764
Population_Group 27.0 4.0 11.0 38.0 0.870968 0.710526 0.782609
Quality_Of_Life 146.0 37.0 57.0 203.0 0.797814 0.719212 0.756477
Housing 809.0 91.0 118.0 927.0 0.898889 0.872708 0.885605
Substance_Frequency 74.0 19.0 44.0 118.0 0.795699 0.627119 0.701422
Smoking 136.0 4.0 2.0 138.0 0.971429 0.985507 0.978417
Eating_Disorder 40.0 2.0 0.0 40.0 0.952381 1.000000 0.975610
Obesity 16.0 1.0 5.0 21.0 0.941176 0.761905 0.842105
Healthcare_Institution 117.0 36.0 57.0 174.0 0.764706 0.672414 0.715596
Financial_Status 222.0 47.0 128.0 350.0 0.825279 0.634286 0.717286
Age 1328.0 109.0 48.0 1376.0 0.924148 0.965116 0.944188
Chidhood_Event 30.0 0.0 24.0 54.0 1.000000 0.555556 0.714286
Exercise 52.0 17.0 31.0 83.0 0.753623 0.626506 0.684211
Communicable_Disease 61.0 5.0 10.0 71.0 0.924242 0.859155 0.890511
Hypertension 45.0 1.0 12.0 57.0 0.978261 0.789474 0.873786
Other_Disease 1065.0 229.0 119.0 1184.0 0.823029 0.899493 0.859564
Violence_Or_Abuse 98.0 26.0 53.0 151.0 0.790323 0.649007 0.712727
Spiritual_Beliefs 94.0 9.0 21.0 115.0 0.912621 0.817391 0.862385
Employment 3797.0 272.0 288.0 4085.0 0.933153 0.929498 0.931322
Social_Exclusion 38.0 6.0 14.0 52.0 0.863636 0.730769 0.791667
Access_To_Care 810.0 95.0 160.0 970.0 0.895028 0.835052 0.864000
Marital_Status 177.0 4.0 9.0 186.0 0.977901 0.951613 0.964578
Diet 110.0 34.0 30.0 140.0 0.763889 0.785714 0.774648
Social_Support 1243.0 197.0 99.0 1342.0 0.863194 0.926230 0.893602
Disability 94.0 4.0 9.0 103.0 0.959184 0.912621 0.935323
Mental_Health 817.0 99.0 216.0 1033.0 0.891921 0.790900 0.838379
Alcohol 592.0 32.0 28.0 620.0 0.948718 0.954839 0.951768
Insurance_Status 145.0 23.0 32.0 177.0 0.863095 0.819209 0.840580
Substance_Quantity 107.0 42.0 39.0 146.0 0.718121 0.732877 0.725424
Hyperlipidemia 14.0 1.0 2.0 16.0 0.933333 0.875000 0.903226
Family_Member 4255.0 110.0 73.0 4328.0 0.974800 0.983133 0.978949
Legal_Issues 71.0 13.0 20.0 91.0 0.845238 0.780220 0.811429
Race_Ethnicity 81.0 9.0 7.0 88.0 0.900000 0.920455 0.910112
Gender 9698.0 183.0 193.0 9891.0 0.981480 0.980487 0.980983
Geographic_Entity 189.0 18.0 22.0 211.0 0.913043 0.895735 0.904306
Sexual_Orientation 21.0 0.0 3.0 24.0 1.000000 0.875000 0.933333
Transportation 27.0 2.0 27.0 54.0 0.931034 0.500000 0.650602
Sexual_Activity 56.0 4.0 24.0 80.0 0.933333 0.700000 0.800000
Language 35.0 6.0 2.0 37.0 0.853659 0.945946 0.897436
Substance_Use 400.0 40.0 24.0 424.0 0.909091 0.943396 0.925926