Description
This pretrained pipeline maps RxNorm codes to MeSH codes without using any text data. You’ll just feed white space-delimited RxNorm codes and it will return the corresponding MeSH 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_mesh_mapping","en","clinical/models")
pipeline.annotate("1191 6809 47613")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models")
val result = pipeline.annotate("1191 6809 47613")
import nlu
nlu.load("en.resolve.rxnorm.mesh").predict("""1191 6809 47613""")
Results
{'rxnorm': ['1191', '6809', '47613'],
'mesh': ['D001241', 'D008687', 'D019355']}
Note:
| RxNorm | Details |
| ---------- | -------------------:|
| 1191 | aspirin |
| 6809 | metformin |
| 47613 | calcium citrate |
| MeSH | Details |
| ---------- | -------------------:|
| D001241 | Aspirin |
| D008687 | Metformin |
| D019355 | Calcium Citrate |
Model Information
| Model Name: | rxnorm_mesh_mapping |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 3.1.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
Included Models
- DocumentAssembler
- TokenizerModel
- LemmatizerModel
- Finisher