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  • John Snow Labs
  • Jul 26, 2026

Mapping National Drug Codes (NDC) Codes with Corresponding Drug Brand Names

  • en
  • chunk_mapper
  • licensed
  • clinical
  • ndc
  • drug_brandname

Description

This pretrained model maps National Drug Codes (NDC) codes with their corresponding drug brand names. Trained on the openFDA NDC Directory (release 2026-07-22).

Open in Colab Copy S3 URI

How to use


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

doc2chunk = Doc2Chunk()\
    .setInputCols(["document"])\
    .setOutputCol("chunk")

chunkerMapper = ChunkMapperModel.pretrained("ndc_drug_brandname_mapper", "en", "clinical/models")\
    .setInputCols(["chunk"])\
    .setOutputCol("mappings")\
    .setRels(["drug_brand_name"])

pipeline = Pipeline(stages=[
    document_assembler,
    doc2chunk,
    chunkerMapper
])

data = spark.createDataFrame([["57894-150"], ["0363-0221"]]).toDF("text")
result = pipeline.fit(data).transform(data)



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

doc2chunk = nlp.Doc2Chunk()\
    .setInputCols(["document"])\
    .setOutputCol("chunk")

chunkerMapper = medical.ChunkMapperModel.pretrained("ndc_drug_brandname_mapper", "en", "clinical/models")\
    .setInputCols(["chunk"])\
    .setOutputCol("mappings")\
    .setRels(["drug_brand_name"])

pipeline = nlp.Pipeline(stages=[
    document_assembler,
    doc2chunk,
    chunkerMapper
])

data = spark.createDataFrame([["57894-150"], ["0363-0221"]]).toDF("text")
result = pipeline.fit(data).transform(data)



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

val doc2chunk = new Doc2Chunk()
    .setInputCols("document")
    .setOutputCol("chunk")

val chunkerMapper = ChunkMapperModel
    .pretrained("ndc_drug_brandname_mapper", "en", "clinical/models")
    .setInputCols(Array("chunk"))
    .setOutputCol("mappings")
    .setRels(Array("drug_brand_name"))

val pipeline = new Pipeline().setStages(Array(
    documentAssembler,
    doc2chunk,
    chunkerMapper
))

val data = Seq("57894-150", "0363-0221").toDF("text")
val result = pipeline.fit(data).transform(data)

Results

| NDC Code   | drug_brand_name   |
|:-----------|:------------------|
| 57894-150  | Zytiga            |
| 0363-0221  | Ibuprofen         |

Model Information

Model Name: ndc_drug_brandname_mapper
Compatibility: Healthcare NLP 6.4.0+
License: Licensed
Edition: Official
Input Labels: [chunk]
Output Labels: [brandname]
Language: en
Size: 2.7 MB