Diseases and Syndromes to UMLS Code Pipeline

Description

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

Predicted Entities

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Available as Private API Endpoint

Deploy

Deploy

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How to use

from sparknlp.pretrained import PretrainedPipeline

pipeline= PretrainedPipeline("umls_disease_syndrome_resolver_pipeline", "en", "clinical/models")
text = "A 34-year-old female with a history of poor appetite, gestational diabetes mellitus, acyclovir allergy and polyuria."
result = pipeline.annotate(text)

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val pipeline = PretrainedPipeline("umls_disease_syndrome_resolver_pipeline", "en", "clinical/models")
val result = pipeline.annotate("A 34-year-old female with a history of poor appetite, gestational diabetes mellitus, acyclovir allergy and polyuria")

import nlu
nlu.load("en.map_entity.umls_disease_syndrome_resolver").predict("""A 34-year-old female with a history of poor appetite, gestational diabetes mellitus, acyclovir allergy and polyuria""")

Results

+-----------------------------+---------+---------+
|chunk                        |ner_label|umls_code|
+-----------------------------+---------+---------+
|poor appetite                |PROBLEM  |C0003123 |
|gestational diabetes mellitus|PROBLEM  |C0085207 |
|acyclovir allergy            |PROBLEM  |C0571297 |
|polyuria                     |PROBLEM  |C0018965 |
+-----------------------------+---------+---------+

Model Information

Included Models

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