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  • John Snow Labs
  • Dec 16, 2024

Mapping Entities (Disease or Syndrome) with Corresponding UMLS CUI Codes

  • umls
  • chunk_mapper
  • clinical
  • licensed
  • en

Description

This pretrained model maps entities (Disease or Syndrome) with 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_clinical_large", "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_disease_syndrome_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([["""A 35-year-old male with a history of obesity and gestational diabetes mellitus and acyclovir allergy."""]]).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_clinical_large", "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_disease_syndrome_mapper", "en", "clinical/models")\
      .setInputCols(["ner_chunk"])\
      .setOutputCol("mappings")\
      .setRels(["umls_code"])\
      .setLowerCase(True)

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

data = spark.createDataFrame([["""A 35-year-old male with a history of obesity and gestational diabetes mellitus and acyclovir allergy."""]]).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_clinical_large", "en", "clinical/models")
       .setInputCols(Array("sentence", "token", "embeddings"))
       .setOutputCol("clinical_ner")

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

val chunkerMapper = ChunkMapperModel
       .pretrained("umls_disease_syndrome_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("""A 35-year-old male with a history of obesity and gestational diabetes mellitus and acyclovir allergy.""").toDF("text")

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

Results


+-----------------------------+---------+
|ner_chunk                    |umls_code|
+-----------------------------+---------+
|obesity                      |C0028754 |
|gestational diabetes mellitus|C0085207 |
|acyclovir allergy            |C0571297 |
+-----------------------------+---------+

Model Information

Model Name: umls_disease_syndrome_mapper
Compatibility: Healthcare NLP 5.5.1+
License: Licensed
Edition: Official
Input Labels: [ner_chunk]
Output Labels: [mappings]
Language: en
Size: 14.7 MB

References

Trained on concepts from disease syndrome for the 2024AB release of the Unified Medical Language System® (UMLS) Knowledge Sources: https://www.nlm.nih.gov/research/umls/index.html