Description
SDOH NER model is designed to detect and label social determinants of health (SDOH) substance use related entities within text data. Social determinants of health are crucial factors that influence individuals’ health outcomes, encompassing various social, economic, and environmental elements. The model has been trained using advanced machine-learning techniques on a diverse range of text sources. The model’s accuracy and precision have been carefully validated against expert-labeled data to ensure reliable and consistent results. Here are the labels of the SDOH NER model with their description:
Alcohol: Mentions of an alcohol drinking habit.Smoking: mentions of smoking habit. “smoking, cigarette, tobacco, etc.”Substance_Duration: The duration associated with the health behaviors. “for 2 years, 3 months, etc”Substance_Frequency: The frequency associated with the health behaviors. “five days a week, daily, weekly, monthly, etc”Substance_Quantity: The quantity associated with the health behaviors. “2 packs, 40 ounces, ten to twelve, moderate, etc”Substance_Use: Mentions of illegal recreational drugs use. Include also substances that can create dependency including here caffeine and tea. “overdose, cocaine, illicit substance intoxication, coffee, etc.”
Predicted Entities
Alcohol, Smoking, Substance_Duration, Substance_Frequency, Substance_Quantity, Substance_Use
Live Demo Open in Colab Copy S3 URI
How to use
from pyspark.sql.types import StringType
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", "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.", "She smokes 1 ppd. Her partner is an alcoholic and a drug abuser for the last 5 years.He was using cocaine."]
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", "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(Array("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.", "She smokes 1 ppd. Her partner is an alcoholic and a drug abuser for the last 5 years.He was using cocaine.")).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 |
|smokes |4 |9 |Smoking |
|1 ppd |11 |15 |Substance_Quantity |
|alcoholic |36 |44 |Alcohol |
|drug abuser |52 |62 |Substance_Use |
|last 5 years |72 |83 |Substance_Duration |
|cocaine |99 |105|Substance_Use |
+----------------+-----+---+-------------------+
Model Information
| Model Name: | ner_sdoh_substance_usage |
| Compatibility: | Healthcare NLP 4.4.4+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 3.0 MB |
| Dependencies: | embeddings_clinical |
References
Internal SDOH Project
Benchmarking
label precision recall f1-score support
Alcohol 0.99 0.99 0.99 265
Smoking 0.97 1.00 0.99 71
Substance_Duration 0.93 0.81 0.87 48
Substance_Frequency 0.92 0.75 0.83 48
Substance_Quantity 0.94 0.89 0.92 55
Substance_Use 0.95 0.93 0.94 192
micro-avg 0.96 0.94 0.95 679
macro-avg 0.95 0.90 0.92 679
weighted-avg 0.96 0.94 0.95 679