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.
Live Demo Open in Colab Copy S3 URI
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')
import nlu
nlu.load("en.resolve.snomed.umls").predict("""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: | Healthcare NLP 3.1.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
Included Models
- DocumentAssembler
- TokenizerModel
- LemmatizerModel
- Finisher