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.
Live Demo Open in Colab Copy S3 URI
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline( "icd10cm_umls_mapping","en","clinical/models")
pipeline.annotate(["M8950", "R822", "R0901"])
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("icd10cm_umls_mapping","en","clinical/models")
val result = pipeline.annotate("M8950", "R822", "R0901")
import nlu
nlu.load("en.resolve.icd10cm.umls").predict("""M8950 R822 R0901""")
Results
{'icd10cm': ['M89.50', 'R82.2', 'R09.01'],
'umls': ['C4721411', 'C0159076', 'C0004044']}
Model Information
| Model Name: | icd10cm_umls_mapping |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 3.1.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
Included Models
- DocumentAssembler
- TokenizerModel
- LemmatizerModel
- Finisher