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  • John Snow Labs
  • Oct 18, 2024

Medical Record Contextual Parser Model

  • en
  • licensed
  • clinical
  • contextualparser
  • medical_record

Description

This model, extracts medical record entities from clinical texts.

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How to use


document_assembler = DocumentAssembler() \
    .setInputCol("text") \
    .setOutputCol("document")

sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")\
    .setInputCols(["document"])\
    .setOutputCol("sentence")

tokenizer = Tokenizer() \
    .setInputCols(["sentence"]) \
    .setOutputCol("token")

medical_record_contextual_parser = ContextualParserModel.pretrained("medical_record_parser","en","clinical/models") \
    .setInputCols(["sentence", "token"]) \
    .setOutputCol("chunk_medical_record")

chunk_converter = ChunkConverter() \
    .setInputCols(["chunk_medical_record"]) \
    .setOutputCol("ner_chunk")

parserPipeline = Pipeline(stages=[
        document_assembler,
        sentence_detector,
        tokenizer,
        medical_record_contextual_parser,
        chunk_converter
        ])

model = parserPipeline.fit(spark.createDataFrame([[""]]).toDF("text"))

sample_text = """Month DD, YYYY
XYZ
RE: ABC
MEDICAL RECORD#: 12332
MRN: 1233567
Dear Dr. XYZ:

I saw ABC back in Neuro-Oncology Clinic today."""

result = model.transform(spark.createDataFrame([[sample_text]]).toDF("text"))



val document_assembler = new DocumentAssembler()
    .setInputCol("text")
    .setOutputCol("document")

val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")
    .setInputCols("document")
    .setOutputCol("sentence")

val tokenizer = new Tokenizer()
    .setInputCols("sentence")
    .setOutputCol("token")

val medical_record_contextual_parser = ContextualParserModel.pretrained("medical_record_parser","en","clinical/models")
    .setInputCols(Array("sentence", "token"))
    .setOutputCol("chunk_medical_record")

val chunk_converter = new ChunkConverter()
    .setInputCols("chunk_medical_record")
    .setOutputCol("ner_chunk")

val parserPipeline = new Pipeline().setStages(Array(
        document_assembler,
        sentence_detector,
        tokenizer,
        medical_record_contextual_parser,
        chunk_converter
))


val sample_text = """Month DD, YYYY
XYZ
RE: ABC
MEDICAL RECORD#: 12332
MRN: 1233567
Dear Dr. XYZ:

I saw ABC back in Neuro-Oncology Clinic today."""

val data = Seq(sample_text).toDF("text")

val results = parserPipeline.fit(data).transform(data)

Results


+-------+-----+---+-------------+
|  chunk|begin|end|        label|
+-------+-----+---+-------------+
|  12332|   44| 48|MEDICALRECORD|
|1233567|   55| 61|MEDICALRECORD|
+-------+-----+---+-------------+

Model Information

Model Name: medical_record_parser
Compatibility: Healthcare NLP 5.5.0+
License: Licensed
Edition: Official
Input Labels: [sentence, token]
Output Labels: [med_code]
Language: en
Size: 9.3 KB
Case sensitive: false