Description
This pretrained pipeline maps ICD10CM codes to SNOMED codes without using any text data. You’ll just feed a comma or white space delimited ICD10CM codes and it will return the corresponding SNOMED codes as a list. For the time being, it supports 132K Snomed codes and will be augmented & enriched in the next releases.
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline( "icd10cm_snomed_mapping","en","clinical/models")
pipeline.annotate('M89.50 I288 H16269')
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("icd10cm_snomed_mapping","en","clinical/models")
val result = pipeline.annotate('M89.50 I288 H16269')
Results
{'icd10cm': ['M89.50', 'I288', 'H16269'],
'snomed': ['733187009', '449433008', '51264003']}
Model Information
| Model Name: | icd10cm_snomed_mapping |
| Type: | pipeline |
| Compatibility: | Spark NLP 2.7.5+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
Included Models
- DocumentAssembler
- TokenizerModel
- LemmatizerModel
- Finisher