Description
This pretrained pipeline is built on the top of snomed_icd10cm_mapper model.
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline= PretrainedPipeline("snomed_icd10cm_mapping", "en", "clinical/models")
result= pipeline.fullAnnotate("128041000119107 292278006 293072005")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline= new PretrainedPipeline("snomed_icd10cm_mapping", "en", "clinical/models")
val result= pipeline.fullAnnotate("128041000119107 292278006 293072005")
import nlu
nlu.load("en.map_entity.snomed_to_icd10cm.pipe").predict("""128041000119107 292278006 293072005""")
Results
| | snomed_code | icd10cm_code |
|---:|:----------------------------------------|:---------------------------|
| 0 | 128041000119107 | 292278006 | 293072005 | K22.70 | T43.595 | T37.1X5 |
Model Information
| Model Name: | snomed_icd10cm_mapping |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 3.5.3+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.5 MB |
Included Models
- DocumentAssembler
- TokenizerModel
- ChunkMapperModel