Description
This pipeline maps ICDO codes to their corresponding SNOMED codes via a direct dictionary lookup.
Wraps the icdo_snomed_mapper_20260901 mapper, trained on SNOMED CT US Edition 20260901 crosswalk data.
Live Demo Open in Colab Copy S3 URI
How to use
from sparknlp.pretrained import PretrainedPipeline
snomed_pipeline = PretrainedPipeline("icdo_snomed_mapping_pipeline_20260901", "en", "clinical/models")
data = spark.createDataFrame([["8000/0"]]).toDF("text")
result = snomed_pipeline.transform(data)
from johnsnowlabs import nlp, medical
snomed_pipeline = nlp.PretrainedPipeline("icdo_snomed_mapping_pipeline_20260901", "en", "clinical/models")
data = spark.createDataFrame([["8000/0"]]).toDF("text")
result = snomed_pipeline.transform(data)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val snomed_pipeline = PretrainedPipeline("icdo_snomed_mapping_pipeline_20260901", "en", "clinical/models")
val data = Seq("8000/0").toDF("text")
val result = snomed_pipeline.transform(data)
Results
| icdo_code | snomed_code | all_k_resolutions |
|:------------|--------------:|:---------------------------------------------|
| 8000/0 | 3898006 | 3898006::: |
| 8000/1 | 400095002 | 400095002:::414389009:::783219005:::86251006 |
| 8170/3 | 25370001 | 25370001::: |
Model Information
| Model Name: | icdo_snomed_mapping_pipeline_20260901 |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 6.4.1+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 119.6 KB |
Included Models
- DocumentAssembler
- Doc2Chunk
- ChunkMapperModel