Description
This pretrained pipeline is built on the top of snomed_umls_mapper model.
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models")
result= pipeline.fullAnnotate("733187009 449433008 51264003")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models")
val result= pipeline.fullAnnotate("733187009 449433008 51264003")
import nlu
nlu.load("en.resolve.snomed.umls").predict("""733187009 449433008 51264003""")
Results
| | snomed_code | umls_code |
|---:|:---------------------------------|:-------------------------------|
| 0 | 733187009 | 449433008 | 51264003 | C4546029 | C3164619 | C0271267 |
Model Information
| Model Name: | snomed_umls_mapping |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 3.5.3+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 5.1 MB |
Included Models
- DocumentAssembler
- TokenizerModel
- ChunkMapperModel