Description
This pretrained pipeline maps RxNorm codes to UMLS codes without using any text data. You’ll just feed white space-delimited RxNorm 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( "rxnorm_umls_mapping","en","clinical/models")
pipeline.annotate("1161611 315677 343663")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline( "rxnorm_umls_mapping","en","clinical/models")
val result = pipeline.annotate("1161611 315677 343663")
import nlu
nlu.load("en.resolve.rxnorm.umls").predict("""1161611 315677 343663""")
Results
{'rxnorm': ['1161611', '315677', '343663'],
'umls': ['C3215948', 'C0984912', 'C1146501']}
Note:
| RxNorm | Details |
| ---------- | ------------------------:|
| 1161611 | metformin Pill |
| 315677 | cimetidine 100 mg |
| 343663 | insulin lispro 50 UNT/ML |
| UMLS | Details |
| ---------- | ------------------------:|
| C3215948 | metformin pill |
| C0984912 | cimetidine 100 mg |
| C1146501 | insulin lispro 50 unt/ml |
Model Information
| Model Name: | rxnorm_umls_mapping |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 3.1.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
Included Models
- DocumentAssembler
- TokenizerModel
- LemmatizerModel
- Finisher