Description
This pretrained pipeline maps ICD10CM codes to UMLS codes without using any text data. You’ll just feed white space-delimited ICD10CM codes and it will return the corresponding UMLS 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("icd10cm_umls_mapping", "en", "clinical/models")
result = pipeline.fullAnnotate(['M8950', 'R822', 'R0901'])
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("icd10cm_umls_mapping", "en", "clinical/models")
val result = pipeline.fullAnnotate(['M8950', 'R822', 'R0901'])
import nlu
nlu.load("en.icd10cm.umls.mapping").predict("""Put your text here.""")
Results
{'icd10cm': ['M89.50', 'R82.2', 'R09.01'],
'umls': ['C4721411', 'C0159076', 'C0004044']}
Model Information
| Model Name: | icd10cm_umls_mapping |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.4.4+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 956.6 KB |
Included Models
- DocumentAssembler
- TokenizerModel
- ChunkMapperModel