Description
This pretrained pipeline is built on the top of icd10cm_snomed_mapper model.
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline= PretrainedPipeline("icd10cm_snomed_mapping", "en", "clinical/models")
result= pipeline.fullAnnotate('R079 N4289 M62830')
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline= new PretrainedPipeline("icd10cm_snomed_mapping", "en", "clinical/models")
val result= pipeline.fullAnnotate("R079 N4289 M62830")
import nlu
nlu.load("en.map_entity.icd10cm_to_snomed.pipe").predict("""R079 N4289 M62830""")
Results
| | icd10cm_code | snomed_code |
|---:|:----------------------|:-----------------------------------------|
| 0 | R079 | N4289 | M62830 | 161972006 | 22035000 | 16410651000119105 |
Model Information
| Model Name: | icd10cm_snomed_mapping |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 3.5.3+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.1 MB |
Included Models
- DocumentAssembler
- TokenizerModel
- ChunkMapperModel