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  • John Snow Labs
  • Dec 04, 2025

Mapping Entities with Corresponding RxNorm Codes

  • mapper
  • rxnorm
  • en
  • licensed
  • clinical
  • chunk_mapper

Description

This model maps drug entities to their corresponding RxNorm codes. It provides fast and accurate drug code mapping without requiring embeddings.

Predicted Entities

RxNorm Codes

Copy S3 URI

How to use


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

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

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

word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
    .setInputCols(["sentence", "token"])\
    .setOutputCol("embeddings")

ner_posology = MedicalNerModel.pretrained("ner_posology_greedy", "en", "clinical/models")\
    .setInputCols(["sentence", "token", "embeddings"])\
    .setOutputCol("posology_ner")

ner_posology_converter = NerConverterInternal()\
    .setInputCols("sentence", "token", "posology_ner")\
    .setOutputCol("ner_chunk")\
    .setWhiteList(["DRUG"])

rxnorm_mapper = ChunkMapperModel.pretrained("rxnorm_mapper", "en", "clinical/models")\
    .setInputCols(["ner_chunk"])\
    .setOutputCol("mappings")\
    .setRels(["rxnorm_code"])

pipeline = Pipeline().setStages([
    document_assembler,
    sentence_detector,
    tokenizer,
    word_embeddings,
    ner_posology,
    ner_posology_converter,
    rxnorm_mapper
])

text = """The patient reported persistent musculoskeletal discomfort, for which ibuprofen topical cream was initiated. Due to concurrent scalp irritation, selenium sulfide 25 mg/ml was also prescribed, and salicylamide 250 mg was added for additional symptomatic relief."""

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")

word_embeddings = nlp.WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
    .setInputCols(["sentence", "token"])\
    .setOutputCol("embeddings")

ner_posology = medical.NerModel.pretrained("ner_posology_greedy", "en", "clinical/models")\
    .setInputCols(["sentence", "token", "embeddings"])\
    .setOutputCol("posology_ner")

ner_posology_converter = medical.NerConverter()\
    .setInputCols("sentence", "token", "posology_ner")\
    .setOutputCol("ner_chunk")\
    .setWhiteList(["DRUG"])

rxnorm_mapper = medical.ChunkMapperModel.pretrained("rxnorm_mapper", "en", "clinical/models")\
    .setInputCols(["ner_chunk"])\
    .setOutputCol("mappings")\
    .setRels(["rxnorm_code"])

pipeline = nlp.Pipeline().setStages([
    document_assembler,
    sentence_detector,
    tokenizer,
    word_embeddings,
    ner_posology,
    ner_posology_converter,
    rxnorm_mapper
])

text = """The patient reported persistent musculoskeletal discomfort, for which ibuprofen topical cream was initiated. Due to concurrent scalp irritation, selenium sulfide 25 mg/ml was also prescribed, and salicylamide 250 mg was added for additional symptomatic relief."""

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 wordEmbeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
    .setInputCols(Array("sentence", "token"))
    .setOutputCol("embeddings")

val nerPosology = MedicalNerModel.pretrained("ner_posology_greedy", "en", "clinical/models")
    .setInputCols(Array("sentence", "token", "embeddings"))
    .setOutputCol("posology_ner")

val nerPosologyConverter = new NerConverterInternal()
    .setInputCols(Array("sentence", "token", "posology_ner"))
    .setOutputCol("ner_chunk")
    .setWhiteList(Array("DRUG"))

val rxnormMapper = ChunkMapperModel.pretrained("rxnorm_mapper", "en", "clinical/models")
    .setInputCols(Array("ner_chunk"))
    .setOutputCol("mappings")
    .setRels(Array("rxnorm_code"))

val pipeline = new Pipeline().setStages(Array(
    documentAssembler,
    sentenceDetector,
    tokenizer,
    wordEmbeddings,
    nerPosology,
    nerPosologyConverter,
    rxnormMapper
))

val data = Seq("""The patient reported persistent musculoskeletal discomfort, for which ibuprofen topical cream was initiated. Due to concurrent scalp irritation, selenium sulfide 25 mg/ml was also prescribed, and salicylamide 250 mg was added for additional symptomatic relief.""").toDF("text")

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

Results


|ner_chunk                |mapping_result|
|-------------------------|--------------|
|ibuprofen topical cream  |377732        |
|selenium sulfide 25 mg/ml|328880        |
|salicylamide 250 mg      |316651        |

Model Information

Model Name: rxnorm_mapper
Compatibility: Healthcare NLP 6.2.0+
License: Licensed
Edition: Official
Input Labels: [ner_chunk]
Output Labels: [mappings]
Language: en
Size: 10.4 MB