Snomed to UMLS Code Mapping

Description

This pretrained pipeline maps SNOMED codes to UMLS codes without using any text data. You’ll just feed white space-delimited SNOMED codes and it will return the corresponding UMLS codes as a list. If there is no mapping, the original code is returned with no mapping.

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How to use

from sparknlp.pretrained import PretrainedPipeline 
pipeline = PretrainedPipeline( 'snomed_umls_mapping','en','clinical/models')
pipeline.annotate('733187009 449433008 51264003')
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new  PretrainedPipeline( 'snomed_umls_mapping','en','clinical/models')
val result = pipeline.annotate('733187009 449433008 51264003')

Results

{'snomed': ['733187009', '449433008', '51264003'],
 'umls': ['C4546029', 'C3164619', 'C0271267']}


Note:

|SNOMED      | Details                                                    | 
| ---------- | ----------------------------------------------------------:|
| 733187009  | osteolysis following surgical procedure on skeletal system |
| 449433008  | Diffuse stenosis of left pulmonary artery                  |
| 51264003   | Limbal AND/OR corneal involvement in vernal conjunctivitis |

| UMLS       | Details                                                    |
| ---------- | ----------------------------------------------------------:|
| C4546029   | osteolysis following surgical procedure on skeletal system |
| C3164619   | diffuse stenosis of left pulmonary artery                  |
| C0271267   | limbal and/or corneal involvement in vernal conjunctivitis |

Model Information

Model Name: snomed_umls_mapping
Type: pipeline
Compatibility: Spark NLP for Healthcare 3.0.2+
License: Licensed
Edition: Official
Language: en

Included Models

  • DocumentAssembler
  • TokenizerModel
  • LemmatizerModel
  • Finisher