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.
Predicted Entities
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models")
result = 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 | mesh_code |
|--:|-------:|----------:|
| 0 | 1191 | D001241 |
| 1 | 6809 | D008687 |
| 2 | 47613 | 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 4.4.4+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 103.6 KB |
Included Models
- DocumentAssembler
- TokenizerModel
- LemmatizerModel
- Finisher