Description
This model extracts terminology related to Social Determinants of Health from various kinds of biomedical documents.
Predicted Entities
Housing, Smoking, Substance_Frequency, Childhood_Development, Age, Other_Disease, Employment, Marital_Status, Diet, Disability, Mental_Health, Alcohol, Substance_Quantity, Family_Member, Race_Ethnicity, Gender, Geographic_Entity, Sexual_Orientation, Substance_Use
Live Demo Open in Colab Copy S3 URI
How to use
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl","xx")\
.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_slim_wip", "en", "clinical/models")\
.setInputCols(["sentence", "token", "embeddings"])\
.setOutputCol("ner")
ner_converter = NerConverter()\
.setInputCols(["sentence","token","ner"])\
.setOutputCol("ner_chunk")
nlpPipeline = Pipeline(stages=[
documentAssembler,
sentenceDetector,
tokenizer,
clinical_embeddings,
ner_model,
ner_converter]
text = [""" Mother states that he does smoke, there is a family hx of alcohol on both maternal and paternal sides of the family, maternal grandfather who died of alcohol related complications and paternal grandmother with severe alcoholism. Pts own drinking began at age 16, living in LA, had a DUI at age 17 after totaling a new car that his mother bought for him, he was married. """]
data = spark.createDataFrame([text]).toDF("text")
result = nlpPipeline.fit(data).transform(data)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")
.setInputCols("document")
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols("sentence")
.setOutputCol("token")
val clinical_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
.setInputCols("sentence", "token")
.setOutputCol("embeddings")
val ner_model = MedicalNerModel.pretrained("ner_sdoh_slim_wip", "en", "clinical/models")
.setInputCols("sentence", "token", "embeddings")
val ner_converter = new NerConverter()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("ner_chunk")
val nlpPipeline = new PipelineModel().setStages(Array(document_assembler,
sentence_detector,
tokenizer,
clinical_embeddings,
ner_model,
ner_converter))
val data = Seq("""Mother states that there is a family hx of alcohol on both maternal and paternal sides of the family, maternal grandfather who died of alcohol related complications and paternal grandmother with severe alcoholism. Pts own drinking began at age 16, had a DUI at age 17 after totaling a new car that his mother bought for him, he was married.""").toDS.toDF("text")
val result = nlpPipeline.fit(data).transform(data)
import nlu
nlu.load("en.med_ner.sdoh_slim_wip").predict(""" Mother states that he does smoke, there is a family hx of alcohol on both maternal and paternal sides of the family, maternal grandfather who died of alcohol related complications and paternal grandmother with severe alcoholism. Pts own drinking began at age 16, living in LA, had a DUI at age 17 after totaling a new car that his mother bought for him, he was married. """)
Results
+-------------+-------------------+
| token| ner_label|
+-------------+-------------------+
| Mother| B-Family_Member|
| states| O|
| that| O|
| he| B-Gender|
| does| O|
| smoke| B-Smoking|
| ,| O|
| there| O|
| is| O|
| a| O|
| family| O|
| hx| O|
| of| O|
| alcohol| B-Alcohol|
| on| O|
| both| O|
| maternal| B-Family_Member|
| and| O|
| paternal| B-Family_Member|
| sides| O|
| of| O|
| the| O|
| family| O|
| ,| O|
| maternal| B-Family_Member|
| grandfather| B-Family_Member|
| who| O|
| died| O|
| of| O|
| alcohol| B-Alcohol|
| related| O|
|complications| O|
| and| O|
| paternal| B-Family_Member|
| grandmother| B-Family_Member|
| with| O|
| severe| B-Alcohol|
| alcoholism| I-Alcohol|
| .| O|
| Pts| O|
| own| O|
| drinking| B-Alcohol|
| began| O|
| at| O|
| age| B-Age|
| 16| I-Age|
| ,| O|
| living| O|
| in| O|
| LA|B-Geographic_Entity|
| ,| O|
| had| O|
| a| O|
| DUI| O|
| at| O|
| age| O|
| 17| O|
| after| O|
| totaling| O|
| a| O|
| new| O|
| car| O|
| that| O|
| his| B-Gender|
| mother| B-Family_Member|
| bought| O|
| for| O|
| him| B-Gender|
| ,| O|
| he| B-Gender|
| was| O|
| married| B-Marital_Status|
| .| O|
+-------------+-------------------+
Model Information
| Model Name: | ner_sdoh_slim_wip |
| Compatibility: | Healthcare NLP 4.2.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 2.8 MB |
References
Manuel annotations have been made over MTSamples and MIMIC datasets.
Benchmarking
label precision recall f1-score support
B-Age 0.93 0.90 0.91 277
B-Alcohol 0.90 0.88 0.89 410
B-Childhood_Development 1.00 1.00 1.00 1
B-Diet 1.00 1.00 1.00 6
B-Disability 0.96 0.95 0.96 57
B-Employment 0.91 0.79 0.85 1926
B-Family_Member 0.93 0.97 0.95 2412
B-Gender 0.97 0.99 0.98 6161
B-Geographic_Entity 0.81 0.79 0.80 82
B-Housing 0.82 0.73 0.77 183
B-Marital_Status 0.93 0.91 0.92 184
B-Mental_Health 0.85 0.72 0.78 487
B-Other_Disease 0.77 0.82 0.79 381
B-Race_Ethnicity 0.91 0.94 0.93 34
B-Sexual_Orientation 0.75 0.90 0.82 10
B-Smoking 0.96 0.96 0.96 209
B-Substance_Frequency 0.92 0.88 0.90 88
B-Substance_Quantity 0.83 0.79 0.81 72
B-Substance_Use 0.80 0.82 0.81 213
I-Age 0.91 0.95 0.93 589
I-Alcohol 0.80 0.77 0.79 159
I-Childhood_Development 1.00 1.00 1.00 3
I-Diet 1.00 0.89 0.94 9
I-Disability 1.00 0.53 0.70 15
I-Employment 0.77 0.62 0.69 369
I-Family_Member 0.79 0.84 0.81 138
I-Gender 0.57 0.88 0.69 231
I-Geographic_Entity 1.00 0.85 0.92 13
I-Housing 0.86 0.83 0.84 362
I-Marital_Status 1.00 0.18 0.31 11
I-Mental_Health 0.81 0.47 0.59 241
I-Other_Disease 0.75 0.74 0.75 256
I-Race_Ethnicity 1.00 1.00 1.00 15
I-Smoking 0.90 0.93 0.91 46
I-Substance_Frequency 0.85 0.73 0.79 75
I-Substance_Quantity 0.84 0.88 0.86 174
I-Substance_Use 0.86 0.84 0.85 182
O 0.99 0.99 0.99 148829
accuracy - - 0.98 164910
macro_avg 0.89 0.83 0.85 164910
weighted_avg 0.98 0.98 0.98 164910