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  • John Snow Labs
  • Jul 14, 2026

Mapping Entities with Corresponding ICD-10-CM Codes

  • en
  • chunk_mapper
  • licensed
  • clinical
  • icd10cm

Description

This model maps clinical entities to their corresponding ICD-10-CM codes. It provides fast and accurate clinical code mapping without requiring embeddings. Built from the ICD-10-CM 20260401 (FY2026, effective April 1, 2026) code set.

Live Demo Open in Colab 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_clinical = 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(stages=[
    document_assembler, sentence_detector, tokenizer, word_embeddings,
    ner_clinical, ner_converter, icd10cm_mapper
])
data = spark.createDataFrame([["The patient has a history of type 2 diabetes mellitus and essential hypertension, presented with acute appendicitis and was noted to have chronic obstructive pulmonary disease on exam. Past psychiatric history notable for major depressive disorder."]]).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_clinical = medical.NerModel.pretrained("ner_clinical", "en", "clinical/models")\
    .setInputCols(["sentence", "token", "embeddings"])\
    .setOutputCol("ner")

ner_converter = medical.NerConverterInternal()\
    .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(stages=[
    document_assembler, sentence_detector, tokenizer, word_embeddings,
    ner_clinical, ner_converter, icd10cm_mapper
])
data = spark.createDataFrame([["The patient has a history of type 2 diabetes mellitus and essential hypertension, presented with acute appendicitis and was noted to have chronic obstructive pulmonary disease on exam. Past psychiatric history notable for major depressive disorder."]]).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("sentence")
    .setOutputCol("token")

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

val nerClinical = 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,
    nerClinical, nerConverter, icd10cmMapper
))

val data = Seq("The patient has a history of type 2 diabetes mellitus and essential hypertension, presented with acute appendicitis and was noted to have chronic obstructive pulmonary disease on exam. Past psychiatric history notable for major depressive disorder.").toDF("text")
val result = pipeline.fit(data).transform(data)

Results

| ner_chunk                             | icd10cm_code   |
|:--------------------------------------|:---------------|
| type 2 diabetes mellitus              | E11            |
| essential hypertension                | I10            |
| acute appendicitis                    | K35            |
| chronic obstructive pulmonary disease | J44.9          |
| major depressive disorder             | F32.9          |

Model Information

Model Name: icd10cm_mapper
Compatibility: Healthcare NLP 6.4.0+
License: Licensed
Edition: Official
Input Labels: [ner_chunk]
Output Labels: [mappings]
Language: en
Size: 13.3 MB