Description
This pipeline extracts clinical entities from text and maps them to their corresponding SNOMED CT concept codes via a direct dictionary lookup.
Wraps the snomed_mapper_20260901 mapper, trained on the SNOMED CT US Edition 20260901 release.
Live Demo Open in Colab Copy S3 URI
How to use
from sparknlp.pretrained import PretrainedPipeline
snomed_pipeline = PretrainedPipeline("snomed_mapping_pipeline_20260901", "en", "clinical/models")
data = spark.createDataFrame([["The patient underwent an appendectomy for acute appendicitis and was diagnosed with type 2 diabetes mellitus. She reported hemolysis and chest pain on recent labs, was noted to have coronary artery disease, and was started on aspirin."]]).toDF("text")
result = snomed_pipeline.transform(data)
from johnsnowlabs import nlp, medical
snomed_pipeline = nlp.PretrainedPipeline("snomed_mapping_pipeline_20260901", "en", "clinical/models")
data = spark.createDataFrame([["The patient underwent an appendectomy for acute appendicitis and was diagnosed with type 2 diabetes mellitus. She reported hemolysis and chest pain on recent labs, was noted to have coronary artery disease, and was started on aspirin."]]).toDF("text")
result = snomed_pipeline.transform(data)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val snomed_pipeline = PretrainedPipeline("snomed_mapping_pipeline_20260901", "en", "clinical/models")
val data = Seq("The patient underwent an appendectomy for acute appendicitis and was diagnosed with type 2 diabetes mellitus. She reported hemolysis and chest pain on recent labs, was noted to have coronary artery disease, and was started on aspirin.").toDF("text")
val result = snomed_pipeline.transform(data)
Results
| ner_chunk | snomed_code | all_k_resolutions |
|:-------------------------|--------------:|:---------------------------------|
| appendectomy | 80146002 | 80146002::: |
| appendicitis | 74400008 | 74400008::: |
| type 2 diabetes mellitus | 44054006 | 44054006::: |
| hemolysis | 260882000 | 260882000:::404227002:::73320003 |
| chest pain | 29857009 | 29857009::: |
| coronary artery disease | 53741008 | 53741008::: |
| aspirin | 387458008 | 387458008::: |
Model Information
| Model Name: | snomed_mapping_pipeline_20260901 |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 6.4.1+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.7 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel
- ChunkMapperModel