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  • John Snow Labs
  • Sep 05, 2024

Mapping MedDRA LLT (Lowest Level Term) Codes with Their Corresponding SNOMED Codes

  • licensed
  • en
  • snomed
  • meddra
  • mapping

Description

This pretrained model maps MedDRA LLT (Lowest Level Term) codes to corresponding SNOMED 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)

meddra_resolver = SentenceEntityResolverModel.load("sbiobertresolve_meddra_lowest_level_term") \
    .setInputCols(["sbert_embeddings"]) \
    .setOutputCol("meddra_llt_code")\
    .setDistanceFunction("EUCLIDEAN")

resolver2chunk = Resolution2Chunk()\
    .setInputCols(["meddra_llt_code"])\
    .setOutputCol("meddra_llt_code2chunk")

chunkMapper = ChunkMapperModel.load('meddra_llt_snomed_mapper')\
    .setInputCols(["meddra_llt_code2chunk"])\
    .setOutputCol("mappings")\
    .setRels(["snomed_code"])

pipeline = Pipeline(stages = [
    documentAssembler,
    sbert_embedder,
    meddra_resolver,
    resolver2chunk,
    chunkMapper])

data = spark.createDataFrame([["Chronic renal insufficiency"], ["Gastritis"], ["Transient ischemic attack"]]).toDF("text")

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

val documentAssembler = new 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 snomed_resolver = new SentenceEntityResolverModel.load("sbiobertresolve_meddra_lowest_level_term")
    .setInputCols(Array("sbert_embeddings"))
    .setOutputCol("meddra_llt_code")
    .setDistanceFunction("EUCLIDEAN")
	
val resolver2chunk = new Resolution2Chunk()
    .setInputCols(Array("meddra_llt_code"))
    .setOutputCol("meddra_llt_code2chunk")
	
val chunkMapper = new ChunkMapperModel.load("meddra_llt_snomed_mapper")
    .setInputCols(Array("meddra_llt_code2chunk"))
    .setOutputCol("mappings")
    .setRels(Array("snomed_code"))
	
val newPipeline().setStages(Array(
     documentAssembler,
     sbert_embedder,
     meddra_resolver,
     resolver2chunk,
     chunkMapper))
	
val data = Seq("Chronic renal insufficiency","Gastritis","Transient ischemic attack") .toDF("text")
	
val result = pipeline.fit(data).transform(data)

Results

+---------------------------+-----------+------------------------------------------------+
|                      chunk|meddra_code|                                     snomed_code|
+---------------------------+-----------+------------------------------------------------+
|Chronic renal insufficiency|   10050441|723190009:Chronic renal insufficiency (disorder)|
|                  Gastritis|   10017853|                    4556007:Gastritis (disorder)|
|  Transient ischemic attack|   10072760|  266257000:Transient ischemic attack (disorder)|
+---------------------------+-----------+------------------------------------------------+

Model Information

Model Name: meddra_llt_snomed_mapper
Compatibility: Healthcare NLP 5.4.1+
License: Licensed
Edition: Official
Input Labels: [ner_chunk]
Output Labels: [mappings]
Language: en
Size: 173.9 KB

References

This model is trained with the April 2024 release of SNOMED 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.