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  • John Snow Labs
  • Sep 04, 2024

Mapping ICD10 Codes with Their Corresponding MedDRA PT (Preferred Term) Codes

  • licensed
  • en
  • icd_10
  • meddra
  • mapping

Description

This pretrained model maps ICD-10 codes to corresponding MedDRA PT (Preferred Term) codes.

Predicted Entities

How to use

documentAssembler = DocumentAssembler()\
    .setInputCol("text")\
    .setOutputCol("ner_chunk")

sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "en", "clinical/models")\
    .setInputCols(["ner_chunk"])\
    .setOutputCol("sbert_embeddings")\
    .setCaseSensitive(False)

icd10_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10cm_augmented", "en", "clinical/models")\
    .setInputCols(["sbert_embeddings"]) \
    .setOutputCol("icd10_code")\
    .setDistanceFunction("EUCLIDEAN")

resolver2chunk = Resolution2Chunk()\
    .setInputCols(["icd10_code"])\
    .setOutputCol("icd102chunk")

chunkerMapper = ChunkMapperModel.load("icd10_meddra_pt_mapper")\
    .setInputCols(["icd102chunk"])\
    .setOutputCol("mappings")

pipeline = Pipeline(stages = [
    documentAssembler,
    sbert_embedder,
    icd10_resolver,
    resolver2chunk,
    chunkerMapper])

data = spark.createDataFrame([["Type 2 diabetes mellitus"], ["Typhoid fever"], ["Malignant neoplasm of oesophagus"]]).toDF("text")

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

val documentAssembler = DocumentAssembler()
    .setInputCol("text")
    .setOutputCol("ner_chunk")

val sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "en", "clinical/models")
    .setInputCols(Array("ner_chunk"))
    .setOutputCol("sbert_embeddings")
    .setCaseSensitive(False)

val icd10_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10cm_augmented", "en", "clinical/models")
    .setInputCols(Array("sbert_embeddings"))
    .setOutputCol("icd10_code")
    .setDistanceFunction("EUCLIDEAN")

val resolver2chunk = Resolution2Chunk()
    .setInputCols(Array("icd10_code"))
    .setOutputCol("icd102chunk")

val chunkerMapper = ChunkMapperModel.load("icd10_meddra_pt_mapper")
    .setInputCols(Array("icd102chunk"))
    .setOutputCol("mappings")

val pipeline = new Pipeline().setStages(Array(
    documentAssembler,
    sbert_embedder,
    icd10_resolver,
    resolver2chunk,
    chunkerMapper))

val data = Seq(Array("Type 2 diabetes mellitus"), Array("Typhoid fever"), Array("Malignant neoplasm of oesophagus")).toDF("text")

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

Results

+--------------------------------+----------+-----------------------------------+
|chunk                           |icd10_code|meddra_code                        |
+--------------------------------+----------+-----------------------------------+
|Type 2 diabetes mellitus        |E11       |10067585.0:Type 2 diabetes mellitus|
|Typhoid fever                   |A01.0     |10045275.0:Typhoid fever           |
|Malignant neoplasm of oesophagus|C15.9     |10030155.0:Oesophageal carcinoma   |
+--------------------------------+----------+-----------------------------------+

Model Information

Model Name: icd10_meddra_pt_mapper
Compatibility: Healthcare NLP 5.4.1+
License: Licensed
Edition: Official
Input Labels: [ner_chunk]
Output Labels: [mappings]
Language: en
Size: 218.0 KB

References

This model is trained with the June 2024 release of ICD-10 to MedDRA Map dataset.

To utilize this model, possession of a valid MedDRA license is requisite. If you possess one and wish to use this model, kindly contact us at support@johnsnowlabs.com.