Description
This pretrained pipeline is built on the top of mesh_umls_mapper model.
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models")
result = pipeline.fullAnnotate(C028491 D019326 C579867)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models")
val result = pipeline.fullAnnotate(C028491 D019326 C579867)
import nlu
nlu.load("en.mesh.umls.mapping").predict("""Put your text here.""")
Results
| | mesh_code | umls_code |
|---:|:----------------------------|:-------------------------------|
| 0 | C028491 | D019326 | C579867 | C0043904 | C0045010 | C3696376 |
Model Information
| Model Name: | mesh_umls_mapping |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.3.2+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 3.8 MB |
Included Models
- DocumentAssembler
- TokenizerModel
- ChunkMapperModel