Description
This model extracts access to healthcare information related to Social Determinants of Health from various kinds of biomedical documents.
Predicted Entities
Insurance_Status, Healthcare_Institution, Access_To_Care
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_access_to_healthcare_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 = ["She has a pension and private health insurance, she reports feeling lonely and isolated.",
"He also reported food insecurityduring his childhood and lack of access to adequate healthcare.",
"She used to work as a unit clerk at XYZ Medical Center."]
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_access_to_healthcare_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("She has a pension and private health insurance, she reports feeling lonely and isolated.").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
Results
+-----------------------------+-----+---+----------------------+
|chunk |begin|end|ner_label |
+-----------------------------+-----+---+----------------------+
|private health insurance |22 |45 |Insurance_Status |
|access to adequate healthcare|65 |93 |Access_To_Care |
|XYZ Medical Center |36 |53 |Healthcare_Institution|
+-----------------------------+-----+---+----------------------+
Model Information
| Model Name: | ner_sdoh_access_to_healthcare_wip |
| Compatibility: | Healthcare NLP 4.3.1+ |
| 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
Healthcare_Institution 94.0 8.0 5.0 99.0 0.921569 0.949495 0.935323
Access_To_Care 561.0 23.0 38.0 599.0 0.960616 0.936561 0.948436
Insurance_Status 60.0 5.0 3.0 63.0 0.923077 0.952381 0.937500