Mapping Entities (Drug Substances) with Corresponding UMLS CUI Codes

Description

This pretrained model maps entities (Drug Substances) with their corresponding UMLS CUI codes.

Important Note: Mappers extract additional information such as extended descriptions and categories related to Concept codes (such as RxNorm, ICD10, CPT, MESH, NDC, UMLS, etc.). They generally take Concept Codes, which are the outputs of EntityResolvers, as input. When creating a pipeline that contains ‘Mapper’, it is necessary to use the ChunkMapperModel after an EntityResolverModel.

Predicted Entities

umls_code

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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")

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

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

ner_model_converter = NerConverterInternal()\
    .setInputCols(["sentence", "token", "clinical_ner"])\
    .setOutputCol("ner_chunk")

chunkerMapper = ChunkMapperModel.pretrained("umls_drug_substance_mapper", "en", "clinical/models")\
      .setInputCols(["ner_chunk"])\
      .setOutputCol("mappings")\
      .setRels(["umls_code"])\
      .setLowerCase(True)

mapper_pipeline = Pipeline().setStages([
        document_assembler,
        sentence_detector,
        tokenizer,
        word_embeddings,
        ner_model,
        ner_model_converter,
        chunkerMapper])

data = spark.createDataFrame([["""The patient was given  metformin, lenvatinib and gallopamil 50 MG Oral Tablet."""]]).toDF("text")

result = mapper_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_model = medical.NerModel.pretrained("ner_posology_greedy", "en", "clinical/models")\
    .setInputCols(["sentence", "token", "embeddings"])\
    .setOutputCol("clinical_ner")

ner_model_converter = medical.NerConverterInternal()\
    .setInputCols(["sentence", "token", "clinical_ner"])\
    .setOutputCol("ner_chunk")

chunkerMapper = medical.ChunkMapperModel.pretrained("umls_drug_substance_mapper", "en", "clinical/models")\
      .setInputCols(["ner_chunk"])\
      .setOutputCol("mappings")\
      .setRels(["umls_code"])\
      .setLowerCase(True)

mapper_pipeline = Pipeline().setStages([
        document_assembler,
        sentence_detector,
        tokenizer,
        word_embeddings,
        ner_model,
        ner_model_converter,
        chunkerMapper])

data = spark.createDataFrame([["""The patient was given  metformin, lenvatinib and gallopamil 50 MG Oral Tablet."""]]).toDF("text")

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

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

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

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

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

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

val ner_model_converter = new NerConverterInternal()
      .setInputCols(Array("sentence", "token", "clinical_ner"))
      .setOutputCol("ner_chunk")

val chunkerMapper = ChunkMapperModel
      .pretrained("umls_drug_substance_mapper", "en", "clinical/models")
      .setInputCols(Array("ner_chunk"))
      .setOutputCol("mappings")
      .setRels(Array("umls_code"))

val mapper_pipeline = new Pipeline().setStages(Array(
                                                  document_assembler,
                                                  sentence_detector,
                                                  tokenizer,
                                                  word_embeddings,
                                                  ner_model,
                                                  ner_model_converter,
                                                  chunkerMapper))

val data = Seq("""The patient was given  metformin, lenvatinib and gallopamil 50 MG Oral Tablet.""").toDF("text")

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

Results

+----------------------------+---------+
|ner_chunk                   |umls_code|
+----------------------------+---------+
|metformin                   |C0025598 |
|lenvatinib                  |C2986924 |
|gallopamil 50 MG Oral Tablet|C0787234 |
+----------------------------+---------+

Model Information

Model Name:	umls_drug_substance_mapper
Compatibility:	Healthcare NLP 5.5.1+
License:	Licensed
Edition:	Official
Input Labels:	[ner_chunk]
Output Labels:	[mappings]
Language:	en
Size:	43.8 MB

References

2024AB UMLS dataset’s Clinical Drug, Pharmacologic Substance, Antibiotic, Hazardous or Poisonous Substance categories. https://www.nlm.nih.gov/research/umls/index.html

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