Description
This pretrained pipeline is built on the top of snomed_icdo_mapper model.
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("snomed_icdo_mapping", "en", "clinical/models")
result= pipeline.fullAnnotate(["10376009", "2026006", "26638004"])
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("snomed_icdo_mapping", "en", "clinical/models")
val result= pipeline.fullAnnotate(["10376009", "2026006", "26638004"])
import nlu
nlu.load("en.map_entity.snomed_to_icdo.pipe").predict("""Put your text here.""")
Results
| | snomed_code | icdo_code |
|--:|------------:|----------:|
| 0 | 10376009 | 8050/2 |
| 1 | 2026006 | 9014/0 |
| 2 | 26638004 | 8322/0 |
Model Information
| Model Name: | snomed_icdo_mapping |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 3.5.3+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 208.7 KB |
Included Models
- DocumentAssembler
- TokenizerModel
- ChunkMapperModel