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.resolve.mesh.umls").predict("""C028491 D019326 C579867""")
Results
| | mesh_code | umls_code |
|---:|:----------------------------|:-------------------------------|
| 0 | C028491 | D019326 | C579867 | C0043904 | C0045010 | C3696376 |
Model Information
| Model Name: | mesh_umls_mapping |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 3.5.3+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 3.8 MB |
Included Models
- DocumentAssembler
- TokenizerModel
- ChunkMapperModel