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  • John Snow Labs
  • Dec 04, 2025

Mapping Entities with Corresponding ICD-10-CM Codes

  • mapper
  • icd10cm
  • en
  • licensed
  • clinical
  • chunk_mapper

Description

This model maps clinical entities to their corresponding ICD-10-CM codes. It provides fast and accurate clinical code mapping without requiring embeddings.

Predicted Entities

ICD-10-CM Codes

Copy S3 URI

How to use


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

sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare", "en", "clinical/models")\
    .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", "en", "clinical/models")\
    .setInputCols(["sentence", "token", "embeddings"])\
    .setOutputCol("ner")

ner_converter = NerConverterInternal()\
    .setInputCols("sentence", "token", "ner")\
    .setOutputCol("ner_chunk")\
    .setWhiteList(["PROBLEM"])

icd10cm_mapper = ChunkMapperModel.pretrained("icd10cm_mapper", "en", "clinical/models")\
    .setInputCols(["ner_chunk"])\
    .setOutputCol("mappings")\
    .setRels(["icd10cm_code"])

pipeline = Pipeline().setStages([
    document_assembler,
    sentence_detector,
    tokenizer,
    word_embeddings,
    ner_model,
    ner_converter,
    icd10cm_mapper
])

text = """A 58-year-old male presents with sciatica and myalgia affecting his lower extremities. He has a history of polymyositis, currently managed with medication. The patient also reports glossitis and beriberi due to nutritional deficiency. Recently, he developed spondylolysis and experiences motion sickness during travel."""

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

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



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

sentence_detector = nlp.SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare", "en", "clinical/models")\
    .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", "en", "clinical/models")\
    .setInputCols(["sentence", "token", "embeddings"])\
    .setOutputCol("ner")

ner_converter = medical.NerConverter()\
    .setInputCols("sentence", "token", "ner")\
    .setOutputCol("ner_chunk")\
    .setWhiteList(["PROBLEM"])

icd10cm_mapper = medical.ChunkMapperModel.pretrained("icd10cm_mapper", "en", "clinical/models")\
    .setInputCols(["ner_chunk"])\
    .setOutputCol("mappings")\
    .setRels(["icd10cm_code"])

pipeline = nlp.Pipeline().setStages([
    document_assembler,
    sentence_detector,
    tokenizer,
    word_embeddings,
    ner_model,
    ner_converter,
    icd10cm_mapper
])

text = """A 58-year-old male presents with sciatica and myalgia affecting his lower extremities. He has a history of polymyositis, currently managed with medication. The patient also reports glossitis and beriberi due to nutritional deficiency. Recently, he developed spondylolysis and experiences motion sickness during travel."""

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

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



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

val sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare", "en", "clinical/models")
    .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 nerModel = MedicalNerModel.pretrained("ner_clinical", "en", "clinical/models")
    .setInputCols(Array("sentence", "token", "embeddings"))
    .setOutputCol("ner")

val nerConverter = new NerConverterInternal()
    .setInputCols(Array("sentence", "token", "ner"))
    .setOutputCol("ner_chunk")
    .setWhiteList(Array("PROBLEM"))

val icd10cmMapper = ChunkMapperModel.pretrained("icd10cm_mapper", "en", "clinical/models")
    .setInputCols(Array("ner_chunk"))
    .setOutputCol("mappings")
    .setRels(Array("icd10cm_code"))

val pipeline = new Pipeline().setStages(Array(
    documentAssembler,
    sentenceDetector,
    tokenizer,
    wordEmbeddings,
    nerModel,
    nerConverter,
    icd10cmMapper
))

val data = Seq("""A 58-year-old male presents with sciatica and myalgia affecting his lower extremities. He has a history of polymyositis, currently managed with medication. The patient also reports glossitis and beriberi due to nutritional deficiency. Recently, he developed spondylolysis and experiences motion sickness during travel.""").toDF("text")

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

Results


|ner_chunk             |mapping_result|
|----------------------|--------------|
|sciatica              |M54.3         |
|myalgia               |M79.1         |
|polymyositis          |M33.2         |
|glossitis             |K14.0         |
|beriberi              |E51.1         |
|nutritional deficiency|E63.9         |
|spondylolysis         |M43.0         |
|motion sickness       |T75.3         |

Model Information

Model Name: icd10cm_mapper
Compatibility: Healthcare NLP 6.2.0+
License: Licensed
Edition: Official
Input Labels: [ner_chunk]
Output Labels: [mappings]
Language: en
Size: 24.3 MB