Description
This pipeline maps SNOMED codes to their corresponding ICD10-CM codes via a direct dictionary lookup.
Wraps the snomed_icd10cm_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("snomed_icd10cm_mapping_pipeline_20260901", "en", "clinical/models")
data = spark.createDataFrame([["1001000119102"]]).toDF("text")
result = snomed_pipeline.transform(data)
from johnsnowlabs import nlp, medical
snomed_pipeline = nlp.PretrainedPipeline("snomed_icd10cm_mapping_pipeline_20260901", "en", "clinical/models")
data = spark.createDataFrame([["1001000119102"]]).toDF("text")
result = snomed_pipeline.transform(data)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val snomed_pipeline = PretrainedPipeline("snomed_icd10cm_mapping_pipeline_20260901", "en", "clinical/models")
val data = Seq("1001000119102").toDF("text")
val result = snomed_pipeline.transform(data)
Results
| snomed_code | icd10cm_code | all_k_resolutions |
|--------------:|:---------------|:--------------------|
| 1001000119102 | I26.99 | I26.99::: |
| 10001005 | A41.9 | A41.9:::P36.9 |
| 10000006 | R07.9 | R07.9::: |
Model Information
| Model Name: | snomed_icd10cm_mapping_pipeline_20260901 |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 6.4.1+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 2.3 MB |
Included Models
- DocumentAssembler
- Doc2Chunk
- ChunkMapperModel