Description
This model extracts income and social status information related to Social Determinants of Health from various kinds of biomedical documents.
Predicted Entities
Education, Marital_Status, Financial_Status, Population_Group, Employment
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_income_social_status_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 = ["Pt is described as divorced and pleasant when approached but keeps to himself. Pt is working as a plumber, but he gets financial diffuculties. He has a son student at college. His family is imigrant for 2 years."]
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_income_social_status_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("Pt is described as divorced and pleasant when approached but keeps to himself. Pt is working as a plumber, but he gets financial diffuculties. He has a son student at college. His family is imigrant for 2 years.").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
Results
+-----------+----------------+-----+---+----------------------+
|sentence_id|ner_label |begin|end|chunk |
+-----------+----------------+-----+---+----------------------+
|0 |Marital_Status |19 |26 |divorced |
|1 |Employment |98 |104|plumber |
|1 |Financial_Status|119 |140|financial diffuculties|
|2 |Education |156 |162|student |
|2 |Education |167 |173|college |
|3 |Population_Group|190 |197|imigrant |
+-----------+----------------+-----+---+----------------------+
Model Information
| Model Name: | ner_sdoh_income_social_status_wip |
| Compatibility: | Healthcare NLP 5.3.3+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 850.8 KB |
Benchmarking
label tp fp fn total precision recall f1
Education 95.0 20.0 18.0 113.0 0.826087 0.840708 0.833333
Population_Group 41.0 0.0 5.0 46.0 1.000000 0.891304 0.942529
Financial_Status 286.0 52.0 82.0 368.0 0.846154 0.777174 0.810198
Employment 3968.0 142.0 215.0 4183.0 0.965450 0.948601 0.956952
Marital_Status 167.0 1.0 7.0 174.0 0.994048 0.959770 0.976608