Description
This pretrained model maps MedDRA PT (Preferred Term) to corresponding MedDRA HLT (High Level Term) codes. Some of the MedDRA PT codes map to more than one MedDRA HLT codes. You can find all the mapped MedDRA HLT 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('pt_code')
mapperModel = ChunkMapperModel.load('meddra_pt_hlt_mapper')\
.setInputCols(["pt_code"])\
.setOutputCol("hlt_mapping")\
.setRels(["hlt_code"])
pipeline = Pipeline(stages=[
document_assembler,
chunk_assembler,
mapperModel
])
data = spark.createDataFrame([["10014468"], ["10017677"], ["10014490"]]).toDF("text")
mapper_model = pipeline.fit(data)
result = mapper_model.transform(data)
val document_assembler = DocumentAssembler()
.setInputCol("text")
.setOutputCol("doc")
val chunk_assembler = Doc2Chunk()
.setInputCols(Array("doc"))
.setOutputCol("pt_code")
val mapperModel = ChunkMapperModel.load("meddra_pt_hlt_mapper")
.setInputCols(Array("pt_code"))
.setOutputCol("hlt_mapping")
.setRels(["hlt_code"])
val pipeline = new Pipeline().setStages(Array(
document_assembler,
chunk_assembler,
mapperModel))
val data = Seq("10014468", "10017677", "10014490").toDF("text")
val mapper_model = pipeline.fit(data)
val result = mapper_model.transform(data)
Results
+--------+------------------------------------------------+------------------------------------------------------------------------------------------------------+
|pt_code |hlt_mapping |all_k_resolutions |
+--------+------------------------------------------------+------------------------------------------------------------------------------------------------------+
|10014468|10036998:Protein analyses NEC |10036998:Protein analyses NEC::: |
|10017677|10006304:Breast radiotherapies |10006304:Breast radiotherapies::: |
|10014490|10018848:Haematological disorders congenital NEC|10018848:Haematological disorders congenital NEC:::10038185:Red cell membrane and enzyme abnormalities|
+--------+------------------------------------------------+------------------------------------------------------------------------------------------------------+
Model Information
| Model Name: | meddra_pt_hlt_mapper |
| Compatibility: | Healthcare NLP 5.3.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [ner_chunk] |
| Output Labels: | [mappings] |
| Language: | en |
| Size: | 680.8 KB |
References
This model is trained with the v27 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.