Pipeline to Mapping SNOMED Codes with Their Corresponding UMLS Codes

Description

This pretrained pipeline is built on the top of snomed_umls_mapper model.

Predicted Entities

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Available as Private API Endpoint

Deploy

Deploy

Deploy

How to use

from sparknlp.pretrained import PretrainedPipeline

pipeline = PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models")
result = pipeline.fullAnnotate(["733187009", "449433008", "51264003"])

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val pipeline = new PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models")
val result = pipeline.fullAnnotate(["733187009", "449433008", "51264003"])

import nlu
nlu.load("en.snomed.umls.mapping").predict("""Put your text here.""")

Results

|   | snomed_code | umls_code |
|--:|------------:|----------:|
| 0 |   733187009 |  C4546029 |
| 1 |   449433008 |  C3164619 |
| 2 |    51264003 |  C0271267 |

Model Information

Included Models

• DocumentAssembler
• TokenizerModel
• ChunkMapperModel