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  • John Snow Labs
  • Nov 30, 2025

Drug Route Text Matcher

  • matcher
  • drug_route
  • en
  • licensed
  • clinical
  • text_matcher

Description

This is a TextMatcher model that identifies drug administration route entities in clinical text. It recognizes routes including oral, IV, IM, subcutaneous, topical, transdermal, inhalation, sublingual, rectal, and more.

Predicted Entities

DRUG_ROUTE

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How to use


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

sentence_detector = SentenceDetector()\
    .setInputCols(["document"])\
    .setOutputCol("sentence")

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

drug_route_matcher = TextMatcherInternalModel.pretrained("drug_route_matcher", "en", "clinical/models")\
    .setInputCols(["sentence", "token"])\
    .setOutputCol("matched_drug_route")

pipeline = Pipeline().setStages([
    document_assembler,
    sentence_detector,
    tokenizer,
    drug_route_matcher
])

text = """Metformin 500mg orally twice daily. Insulin glargine subcutaneously at bedtime. Morphine 4mg IV for pain. Albuterol via inhalation every 4 hours PRN."""

data = spark.createDataFrame([[text]]).toDF("text")

result = pipeline.fit(data).transform(data)



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

sentence_detector = nlp.SentenceDetector()\
    .setInputCols(["document"])\
    .setOutputCol("sentence")

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

drug_route_matcher = medical.TextMatcherModel.pretrained("drug_route_matcher", "en", "clinical/models")\
    .setInputCols(["sentence", "token"])\
    .setOutputCol("matched_drug_route")

pipeline = nlp.Pipeline().setStages([
    document_assembler,
    sentence_detector,
    tokenizer,
    drug_route_matcher
])

text = """Metformin 500mg orally twice daily. Insulin glargine subcutaneously at bedtime. Morphine 4mg IV for pain. Albuterol via inhalation every 4 hours PRN."""

data = spark.createDataFrame([[text]]).toDF("text")

result = pipeline.fit(data).transform(data)



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

val sentenceDetector = new SentenceDetector()
    .setInputCols(Array("document"))
    .setOutputCol("sentence")

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

val drugRouteMatcher = TextMatcherInternalModel.pretrained("drug_route_matcher", "en", "clinical/models")
    .setInputCols(Array("sentence", "token"))
    .setOutputCol("matched_drug_route")

val pipeline = new Pipeline().setStages(Array(
    documentAssembler,
    sentenceDetector,
    tokenizer,
    drugRouteMatcher
))

val data = Seq("""Metformin 500mg orally twice daily. Insulin glargine subcutaneously at bedtime. Morphine 4mg IV for pain. Albuterol via inhalation every 4 hours PRN.""").toDF("text")

val result = pipeline.fit(data).transform(data)

Results


+--------------+-----+---+----------+
|chunk         |begin|end|label     |
+--------------+-----+---+----------+
|orally        |18   |23 |DRUG_ROUTE|
|subcutaneously|52   |65 |DRUG_ROUTE|
|IV            |93   |94 |DRUG_ROUTE|
|inhalation    |114  |123|DRUG_ROUTE|
+--------------+-----+---+----------+

Model Information

Model Name: drug_route_matcher
Compatibility: Healthcare NLP 6.2.0+
License: Licensed
Edition: Official
Input Labels: [sentence, token]
Output Labels: [matched_drug_route]
Language: en
Size: 939.6 KB
Case sensitive: false