Mapping MedDRA-PT (Preferred Term) Codes with Their Corresponding ICD-10 Codes

Description

This pretrained model maps MedDRA-PT (Preferred Term) codes to corresponding ICD10 codes. Some of the MedDRA PT codes map to more than ICD-10 codes. You can find all the mapped ICD-10 codes in the all_k_resolutions column in the metadata.

Predicted Entities

icd10 code

How to use

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

chunk_assembler = Doc2Chunk()\
      .setInputCols(["doc"])\
      .setOutputCol("ner_chunk")
 
mapperModel = ChunkMapperModel.load("meddra_pt_icd10_mapper")\
    .setInputCols(["ner_chunk"])\
    .setOutputCol("mappings")\
    .setRels(["icd10_code"])


mapper_pipeline = Pipeline(stages=[
    document_assembler,
    chunk_assembler,
    mapperModel
])

data = spark.createDataFrame([["10000153"], ["10000081"], ["10039085"]]).toDF("text")

mapper_model = mapper_pipeline.fit(data)
result = mapper_model.transform(data)
val document_assembler = DocumentAssembler()
      .setInputCol("text")
      .setOutputCol("doc")

val chunk_assembler = Doc2Chunk()
      .setInputCols(Array("doc"))
      .setOutputCol("ner_chunk")
 
val mapperModel = ChunkMapperModel.load("meddra_pt_icd10_mapper")
    .setInputCols(Array("ner_chunk"))
    .setOutputCol("mappings")
    .setRels(Array("icd10_code"))


val mapper_pipeline = new Pipeline().setStages(Array(
    document_assembler,
    chunk_assembler,
    mapperModel
))

val data = Seq("10000153", "10000081", "10039085").toDF("text")

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

Results

+-----------+-------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------+
|meddra_code|icd10_code                           |all_k_resolutions                                                                                                                         |
+-----------+-------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------+
|10000153.0 |O62:Abnormalities of forces of labour|O62:Abnormalities of forces of labour:::O62.8:Other abnormalities of forces of labour:::O62.9:Abnormality of forces of labour, unspecified|
|10000081.0 |R10:Abdominal and pelvic pain        |R10:Abdominal and pelvic pain:::R10.4:Other and unspecified abdominal pain                                                                |
|10039085.0 |J30:Vasomotor and allergic rhinitis  |J30:Vasomotor and allergic rhinitis:::J30.3:Other allergic rhinitis:::J30.4:Allergic rhinitis, unspecified                                |
+-----------+-------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------+

Model Information

Model Name: meddra_pt_icd10_mapper
Compatibility: Healthcare NLP 5.3.0+
License: Licensed
Edition: Official
Input Labels: [ner_chunk]
Output Labels: [mappings]
Language: en
Size: 190.3 KB

References

This model is trained with the January 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.