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  • John Snow Labs
  • Dec 22, 2025

Specimen Contextual Parser Model

  • en
  • contextualparser
  • licensed
  • clinical
  • specimen

Description

This model, extracts specimen 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")

specimen_contextual_parser = ContextualParserModel.pretrained("specimen_parser","en","clinical/models")\
    .setInputCols(["sentence", "token"])\
    .setOutputCol("chunk_zip")

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

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

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

sample_text = """Specimen ID: AB123-456789 was collected from the patient. The laboratory processed Specimen Number CD987654 yesterday. Use Specimen Code: XYZ12-3456 for tracking purposes. Sample was labeled as Specimen#EF34-789. Specimen No. GH56-123456 was sent to the pathology department."""

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





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

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

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

specimen_contextual_parser = medical.ContextualParserModel.pretrained("specimen_parser","en","clinical/models")\
    .setInputCols(["sentence", "token"])\
    .setOutputCol("chunk_zip")

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

parserPipeline = nlp.Pipeline(stages=[
        document_assembler,
        sentence_detector,
        tokenizer,
        specimen_contextual_parser,
        chunk_converter
        ])

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

sample_text = """Specimen ID: AB123-456789 was collected from the patient. The laboratory processed Specimen Number CD987654 yesterday. Use Specimen Code: XYZ12-3456 for tracking purposes. Sample was labeled as Specimen#EF34-789. Specimen No. GH56-123456 was sent to the pathology department."""

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(Array("document"))
    .setOutputCol("sentence")

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

val specimen_contextual_parser = ContextualParserModel.pretrained("specimen_parser","en","clinical/models")
    .setInputCols(Array("sentence", "token"))
    .setOutputCol("chunk_zip")

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

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

val model = parserPipeline.fit(Seq(Array("")).toDS.toDF("text"))

val sample_text = """Specimen ID: AB123-456789 was collected from the patient. The laboratory processed Specimen Number CD987654 yesterday. Use Specimen Code: XYZ12-3456 for tracking purposes. Sample was labeled as Specimen#EF34-789. Specimen No. GH56-123456 was sent to the pathology department."""

val result = model.transform(Seq(Array(sample_text)).toDS.toDF("text"))

Results



| specimen_id   |   begin |   end | label    |
|:--------------|--------:|------:|:---------|
| AB123         |      13 |    18 | SPECIMEN |
| CD987654      |      99 |   107 | SPECIMEN |
| XYZ12-3456    |     138 |   148 | SPECIMEN |
| EF34-789      |     203 |   211 | SPECIMEN |
| GH56-123456   |     226 |   237 | SPECIMEN |

Model Information

Model Name: specimen_parser
Compatibility: Healthcare NLP 6.2.2+
License: Licensed
Edition: Official
Input Labels: [document, token_doc]
Output Labels: [entity_specimen]
Language: en
Size: 4.4 KB
Case sensitive: false