Description
SDOH NER model is designed to detect and label social determinants of health (SDOH) entities within text data. Social determinants of health are crucial factors that influence individuals’ health outcomes, encompassing various social, economic, and environmental element. The model has been trained using advanced machine learning techniques on a diverse range of text sources. The model can accurately recognize and classify a wide range of SDOH entities, including but not limited to factors such as socioeconomic status, education level, housing conditions, access to healthcare services, employment status, cultural and ethnic background, neighborhood characteristics, and environmental factors. The model’s accuracy and precision have been carefully validated against expert-labeled data to ensure reliable and consistent results. This model is augmented version of ner_sdoh
Predicted Entities
Access_To_Care, Age, Alcohol, Childhood_Event, 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 Copy S3 URI
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentence_detector = SentenceDetector()\
.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_langtest", "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 55 years old, living in New York, a divorced Mexcian American woman with financial problems. She speaks Spanish and Portuguese. 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 cannot access health insurance or paid sick leave. She has a son, a student at college. Pt with likely long-standing depression. She is aware she needs rehab. Pt reports having her catholic faith as a means of support as well. She has a 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 in April and was due to 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 = SentenceDetector()
.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_langtest", "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 55 years old, living in New York, a divorced Mexcian American woman with financial problems. She speaks Spanish and Portuguese. 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 cannot access health insurance or paid sick leave. She has a son, a student at college. Pt with likely long-standing depression. She is aware she needs rehab. Pt reports having her catholic faith as a means of support as well. She has a 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 in April and was due to court this week.""").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
Results
+--------------------+-----+---+-------------------+
| chunk|begin|end| ner_label|
+--------------------+-----+---+-------------------+
| 55 years old| 9| 20| Age|
| New York| 33| 40| Geographic_Entity|
| divorced| 45| 52| Marital_Status|
| Mexcian American| 54| 69| Race_Ethnicity|
| woman| 71| 75| Gender|
| financial problems| 82| 99| Financial_Status|
| She| 102|104| Gender|
| Spanish| 113|119| Language|
| Portuguese| 125|134| Language|
| She| 137|139| Gender|
| apartment| 153|161| Housing|
| She| 164|166| Gender|
| diabetes| 193|200| Other_Disease|
| hospitalizations| 268|283|Other_SDoH_Keywords|
| cleaning assistant| 342|359| Employment|
|access health ins...| 372|394| Insurance_Status|
| She| 416|418| Gender|
| son| 426|428| Family_Member|
| student| 433|439| Education|
| college| 444|450| Education|
| depression| 482|491| Mental_Health|
| She| 494|496| Gender|
| she| 507|509| Gender|
| rehab| 517|521| Access_To_Care|
| her| 542|544| Gender|
| catholic faith| 546|559| Spiritual_Beliefs|
| support| 575|581| Social_Support|
| She| 593|595| Gender|
| etoh abuse| 619|628| Alcohol|
| her| 644|646| Gender|
| teens| 648|652| Age|
| She| 655|657| Gender|
| she| 667|669| Gender|
| daily| 682|686|Substance_Frequency|
| drinker| 688|694| Alcohol|
| 30 years| 700|707| Substance_Duration|
| drinking| 724|731| Alcohol|
| beer| 733|736| Alcohol|
| daily| 738|742|Substance_Frequency|
| She| 745|747| Gender|
| smokes| 749|754| Smoking|
| a pack| 756|761| Substance_Quantity|
| cigarettes| 766|775| Smoking|
| a day| 777|781|Substance_Frequency|
| She| 784|786| Gender|
| DUI| 792|794| Legal_Issues|
+--------------------+-----+---+-------------------+
Model Information
| Model Name: | ner_sdoh_langtest |
| Compatibility: | Healthcare NLP 5.0.0+ |
| 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
Other_SDoH_Keywords 359.0 123.0 99.0 458.0 0.7448 0.7838 0.7638
Education 96.0 24.0 19.0 115.0 0.8 0.8348 0.817
Population_Group 14.0 1.0 14.0 28.0 0.9333 0.5 0.6512
Quality_Of_Life 78.0 25.0 20.0 98.0 0.7573 0.7959 0.7761
Food_Insecurity 29.0 5.0 3.0 32.0 0.8529 0.9063 0.8788
Housing 321.0 57.0 67.0 388.0 0.8492 0.8273 0.8381
Smoking 134.0 7.0 5.0 139.0 0.9504 0.964 0.9571
Substance_Frequency 104.0 14.0 23.0 127.0 0.8814 0.8189 0.849
Eating_Disorder 53.0 2.0 0.0 53.0 0.9636 1.0 0.9815
Environmental_Con... 34.0 3.0 5.0 39.0 0.9189 0.8718 0.8947
Obesity 13.0 2.0 2.0 15.0 0.8667 0.8667 0.8667
Healthcare_Instit... 1350.0 23.0 43.0 1393.0 0.9832 0.9691 0.9761
Financial_Status 94.0 26.0 35.0 129.0 0.7833 0.7287 0.755
Age 509.0 65.0 53.0 562.0 0.8868 0.9057 0.8961
Exercise 87.0 10.0 27.0 114.0 0.8969 0.7632 0.8246
Communicable_Disease 73.0 6.0 9.0 82.0 0.9241 0.8902 0.9068
Hypertension 56.0 1.0 5.0 61.0 0.9825 0.918 0.9492
Other_Disease 644.0 91.0 103.0 747.0 0.8762 0.8621 0.8691
Violence_Or_Abuse 86.0 35.0 40.0 126.0 0.7107 0.6825 0.6964
Spiritual_Beliefs 71.0 10.0 7.0 78.0 0.8765 0.9103 0.8931
Employment 3424.0 210.0 233.0 3657.0 0.9422 0.9363 0.9392
Social_Exclusion 33.0 5.0 3.0 36.0 0.8684 0.9167 0.8919
Access_To_Care 464.0 112.0 87.0 551.0 0.8056 0.8421 0.8234
Marital_Status 189.0 8.0 2.0 191.0 0.9594 0.9895 0.9742
Income 55.0 7.0 10.0 65.0 0.8871 0.8462 0.8661
Diet 52.0 15.0 16.0 68.0 0.7761 0.7647 0.7704
Social_Support 866.0 161.0 115.0 981.0 0.8432 0.8828 0.8625
Community_Safety 39.0 12.0 5.0 44.0 0.7647 0.8864 0.8211
Disability 94.0 2.0 3.0 97.0 0.9792 0.9691 0.9741
Mental_Health 740.0 107.0 100.0 840.0 0.8737 0.881 0.8773
Alcohol 508.0 39.0 33.0 541.0 0.9287 0.939 0.9338
Insurance_Status 99.0 31.0 19.0 118.0 0.7615 0.839 0.7984
Substance_Quantity 84.0 9.0 22.0 106.0 0.9032 0.7925 0.8442
Hyperlipidemia 11.0 0.0 2.0 13.0 1.0 0.8462 0.9167
Family_Member 4118.0 103.0 63.0 4181.0 0.9756 0.9849 0.9802
Legal_Issues 58.0 15.0 17.0 75.0 0.7945 0.7733 0.7838
Race_Ethnicity 70.0 7.0 3.0 73.0 0.9091 0.9589 0.9333
Gender 10175.0 233.0 197.0 10372.0 0.9776 0.981 0.9793
Geographic_Entity 170.0 17.0 17.0 187.0 0.9091 0.9091 0.9091
Childhood_Event 19.0 0.0 5.0 24.0 1.0 0.7917 0.8837
Sexual_Orientation 35.0 9.0 17.0 52.0 0.7955 0.6731 0.7292
Transportation 72.0 7.0 14.0 86.0 0.9114 0.8372 0.8727
Substance_Duration 34.0 17.0 16.0 50.0 0.6667 0.68 0.6733
Sexual_Activity 38.0 29.0 8.0 46.0 0.5672 0.8261 0.6726
Language 29.0 3.0 6.0 35.0 0.9063 0.8286 0.8657
Substance_Use 312.0 44.0 20.0 332.0 0.8764 0.9398 0.907
macro - - - - - - 0.8592
micro - - - - - - 0.9396