Clinical Drugs to UMLS Code Mapping

Description

This pretrained pipeline maps entities (Clinical Drugs) with their corresponding UMLS CUI codes. You’ll just feed your text and it will return the corresponding UMLS codes.

Predicted Entities

DRUG

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How to use

from sparknlp.pretrained import PretrainedPipeline

resolver_pipeline = PretrainedPipeline("umls_drug_resolver_pipeline", "en", "clinical/models")

result = resolver_pipeline.annotate("""The patient was given Adapin 10 MG, coumadn 5 mg.""")

resolver_pipeline = nlp.PretrainedPipeline("umls_drug_resolver_pipeline", "en", "clinical/models")

result = resolver_pipeline.annotate("""The patient was given Adapin 10 MG, coumadn 5 mg.""")

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val resolver_pipeline = PretrainedPipeline("umls_drug_resolver_pipeline", "en", "clinical/models")

val result = resolver_pipeline.annotate("""The patient was given Adapin 10 MG, coumadn 5 mg.""")

Results

+------------+---------+---------+
|chunk       |ner_label|umls_code|
+------------+---------+---------+
|Adapin 10 MG|DRUG     |C1382178 |
|coumadn 5 mg|DRUG     |C1368171 |
+------------+---------+---------+

Model Information

Included Models

• DocumentAssembler
• SentenceDetector
• TokenizerModel
• WordEmbeddingsModel
• MedicalNerModel
• NerConverter
• ChunkMapperModel
• ChunkMapperFilterer
• Chunk2Doc
• BertSentenceEmbeddings
• SentenceEntityResolverModel
• ResolverMerger