Description
This pipeline extracts clinical entities from text and maps them to SNOMED CT auxiliary/descriptive concepts using sbiobert_base_cased_mli_onnx embeddings. Wraps the sbiobertresolve_snomed_auxConcepts_20260901 resolver, trained on SNOMED CT US Edition 20260901.
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How to use
from sparknlp.pretrained import PretrainedPipeline
snomed_pipeline = PretrainedPipeline("sbiobertresolve_snomed_auxConcepts_pipeline_20260901", "en", "clinical/models")
data = spark.createDataFrame([["She underwent an appendectomy for appendicitis. Post-operatively, she was started on penicillin. An echocardiogram showed normal left ventricle function."]]).toDF("text")
result = snomed_pipeline.transform(data)
from johnsnowlabs import nlp, medical
snomed_pipeline = nlp.PretrainedPipeline("sbiobertresolve_snomed_auxConcepts_pipeline_20260901", "en", "clinical/models")
data = spark.createDataFrame([["She underwent an appendectomy for appendicitis. Post-operatively, she was started on penicillin. An echocardiogram showed normal left ventricle function."]]).toDF("text")
result = snomed_pipeline.transform(data)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val snomed_pipeline = PretrainedPipeline("sbiobertresolve_snomed_auxConcepts_pipeline_20260901", "en", "clinical/models")
val data = Seq("She underwent an appendectomy for appendicitis. Post-operatively, she was started on penicillin. An echocardiogram showed normal left ventricle function.").toDF("text")
val result = snomed_pipeline.transform(data)
Results
| chunk | label | snomed_code | resolution | all_codes | all_resolutions |
|:---------------|:----------------|--------------:|:---------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| appendectomy | Procedure | 80146002 | appendectomy | 80146002:::17041004:::82730006:::174045003:::6025007:::235314005:::51113007:::1299000:::49586007:::42332004:::49438003:::6801000:::39126001:::22324003:::54357003:::55588008:::174036004:::307583008:::119954001 | appendectomy:::appendicotomy:::secondary appendectomy:::interval appendectomy:::endoscopic appendectomy:::inversion appendectomy:::appendicolysis:::excision of appendiceal stump:::appendicocaecostomy:::appendicostomy:::appendectomy and drainage:::mesenterectomy:::angiectomy:::jejunectomy:::enterectomy:::abdominal arteriectomy:::emergency appendectomy:::cecectomy:::adenoidectomy |
| penicillin | Drug_Ingredient | 764146007 | penicillin | 764146007:::372725003:::323389000:::79744009:::9330003:::890458001:::387545005:::52627000:::387084009:::391818008:::373298001:::373291007:::86848007:::373218000:::42993004:::372836004:::387246005:::96072001:::300041008:::39359008:::78507004:::373284009 | penicillin:::penicillin v:::penicillin g:::penicillinase:::penicillin measurement:::penicillin-containing product:::pivampicillin:::penicillinase measurement:::phenethicillin:::benethamine penicillin:::aminopenicillin:::natural penicillin:::penicillin amidase:::antipseudomonal penicillin:::penicillium:::piperacillin:::procaine penicillin g:::pivampicillin-containing product:::penicillin prophylaxis:::penicillin v-containing product:::penicillin g-containing product:::extended spectrum penicillin |
| echocardiogram | Test | 40701008 | echocardiogram | 40701008:::1354543009:::433232009:::433236007:::16310003:::105376000:::439238004:::401000009:::1297086000:::252420009:::390791001:::468366005:::425789000:::830078000:::433231002:::830079008:::86599005:::61518007 | echocardiogram:::ventricular echocardiography:::epicardial echocardiography:::transthoracic echocardiography:::echography:::transoesophageal echocardiogram:::echocardiography test interpretation:::echocardiogram requested:::echoendoscope:::intravascular echocardiography:::referral for echocardiography:::echocardiographic recording paper:::transluminal intracardiac echocardiography:::velocity vector echocardiography:::contrast echocardiography:::speckle tracking echocardiography:::echoplacentogram:::echography, a-mode |
Model Information
| Model Name: | sbiobertresolve_snomed_auxConcepts_pipeline_20260901 |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 6.4.1+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 4.0 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel
- Chunk2Doc
- BertSentenceEmbeddings
- SentenceEntityResolverModel