Description
This pretrained pipeline maps ICD-10-CM codes to their corresponding billable mappings, hcc codes, cause mappings, claim mappings, SNOMED codes, UMLS codes and ICD-9 codes without using any text data. You’ll just feed white space-delimited ICD-10-CM codes and get the result.
How to use
from sparknlp.pretrained import PretrainedPipeline
icd10cm_pipeline = PretrainedPipeline("icd10cm_multi_mapper_pipeline", "en", "clinical/models")
result = icd10cm_pipeline.fullAnnotate("""Z833 D66 G43.83""")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val icd10cm_pipeline = new PretrainedPipeline("icd10cm_multi_mapper_pipeline", "en", "clinical/models")
val result = icd10cm_pipeline.fullAnnotate("""Z833 D66 G43.83""")
Results
| | icd10cm_code | bill_mappings | hcc_mappings | cause_mappings | claim_mappings | snomed_mappings | umls_mappings | icd9_mappings |
|---:|:---------------|:----------------|:---------------|:-------------------------|:-----------------|:------------------|:----------------|:----------------|
| 0 | D66 | 1 | 46 | Nutritional deficiencies | NONE | 438599002 | C0019069 | 2860 |
| 1 | Z833 | NONE | NONE | NONE | NONE | 160402005 | C0260526 | V180 |
| 2 | G43.83 | 0 | 0 | Headache disorders | G43.83 | NONE | NONE | NONE |
Model Information
| Model Name: | icd10cm_multi_mapper_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 5.0.1+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 5.9 MB |
Included Models
- DocumentAssembler
- TokenizerModel
- DocMapperModel
- DocMapperModel
- ChunkMapperModel
- ChunkMapperModel
- ChunkMapperModel
- ChunkMapperModel
- ChunkMapperModel