Mapping MedDRA-PT (Preferred Term) Codes With Their Corresponding MedDRA-LLT (Lowest Level Term) Codes

Description

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

Predicted Entities

How to use

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

chunk_assembler = Doc2Chunk()\
      .setInputCols(['doc'])\
      .setOutputCol('ner_chunk')
 
mapperModel = ChunkMapperModel.load('meddra_pt_llt_mapper')\
    .setInputCols(["ner_chunk"])\
    .setOutputCol("mappings")\
    .setRels(["llt_code"])


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

data = spark.createDataFrame([["10008684"], ["10014472"], ["10019785"]]).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_llt_mapper")
    .setInputCols(Array("ner_chunk"))
    .setOutputCol("mappings")
    .setRels(Array("llt_code"))


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

val data = Seq("10008684", "10014472", "10019785").toDF("text")

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

Results

+--------+---------------------------+--------------------------------------------------------------+
|pt_code |llt_code                   |all_k_resolutions                                             |
+--------+---------------------------+--------------------------------------------------------------+
|10008684|10004711:Biliuria          |10004711:Biliuria:::10008684:Choluria:::10046617:Urine bilious|
|10014472|10014472:Elephantiasis     |10014472:Elephantiasis:::10014473:Elephantiasis of eyelid     |
|10019785|10019785:Hepatitis neonatal|10019785:Hepatitis neonatal:::                                |
+--------+---------------------------+--------------------------------------------------------------+

Model Information

Model Name: meddra_pt_llt_mapper
Compatibility: Healthcare NLP 5.4.1+
License: Licensed
Edition: Official
Input Labels: [ner_chunk]
Output Labels: [mappings]
Language: en
Size: 1.3 MB

References

This model is trained with the September 2024 (v27.1) release of MedDRA 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.