Description
This pretrained pipeline is built on the top of icd10cm_umls_mapper model.
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("icd10cm_umls_mapping", "en", "clinical/models")
result = pipeline.fullAnnotate(["M8950", "R822", "R0901"])
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("icd10cm_umls_mapping", "en", "clinical/models")
val result = pipeline.fullAnnotate(Array("M8950", "R822", "R0901"))
import nlu
nlu.load("en.resolve.icd10cm.umls").predict("""Put your text here.""")
Results
| | icd10cm_code | umls_code |
|---:|:---------------|:------------|
| 0 | M8950 | C4721411 |
| 1 | R822 | C0159076 |
| 2 | R0901 | C0004044 |
Model Information
| Model Name: | icd10cm_umls_mapping |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 3.5.3+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 952.4 KB |
Included Models
- DocumentAssembler
- TokenizerModel
- ChunkMapperModel