Description
This pipeline extracts clinical entities from text and maps them to SNOMED CT clinical finding concepts using sbiobert_base_cased_mli_onnx embeddings. Wraps the sbiobertresolve_snomed_findings_20260901 resolver, trained on SNOMED CT US Edition 20260901.
Live Demo Open in Colab Copy S3 URI
How to use
from sparknlp.pretrained import PretrainedPipeline
snomed_pipeline = PretrainedPipeline("sbiobertresolve_snomed_findings_pipeline_20260901", "en", "clinical/models")
data = spark.createDataFrame([["The patient presented with recurrent fevers. Clinically she appeared cachectic with hepatosplenomegaly. Laboratory results confirmed pancytopenia."]]).toDF("text")
result = snomed_pipeline.transform(data)
from johnsnowlabs import nlp, medical
snomed_pipeline = nlp.PretrainedPipeline("sbiobertresolve_snomed_findings_pipeline_20260901", "en", "clinical/models")
data = spark.createDataFrame([["The patient presented with recurrent fevers. Clinically she appeared cachectic with hepatosplenomegaly. Laboratory results confirmed pancytopenia."]]).toDF("text")
result = snomed_pipeline.transform(data)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val snomed_pipeline = PretrainedPipeline("sbiobertresolve_snomed_findings_pipeline_20260901", "en", "clinical/models")
val data = Seq("The patient presented with recurrent fevers. Clinically she appeared cachectic with hepatosplenomegaly. Laboratory results confirmed pancytopenia.").toDF("text")
val result = snomed_pipeline.transform(data)
Results
| chunk | label | snomed_code | resolution | all_codes | all_resolutions |
|:-------------------|:-----------|--------------:|:-------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| fevers | VS_Finding | 386661006 | fever | 386661006:::77957000:::271751000:::248435007:::271754008:::186694006:::704425001:::416113008:::271750004:::41348000:::271753002:::81472005:::240499000:::42136008:::1078287008:::111950007:::274640006:::248449003:::240453002:::103001002:::186774005 | fever:::intermittent fever:::sustained fever:::prolonged fever:::crisis of fever:::sweating fever:::chronic fever:::disorder characterized by fever:::gradual rise of fever:::piry fever:::irregular fever:::ossa fever:::sepik fever:::swinging fever:::recurrent fever:::artificial fever:::fever with rigors:::central fever:::oroya fever:::feels feverish:::boutonneuse fever |
| cachectic | Symptom | 238108007 | cachectic | 238108007:::422003001:::284529003:::788876001:::240128005:::288517002:::89476005:::298389007:::281583001:::231439009:::298744008:::280994000:::286933003:::284670008:::29740003:::441971000124107:::2492009:::716749005:::201139004:::441951000124102:::50805004:::84946008:::95868006 | cachectic:::cachexia associated with aids:::cardiac cachexia:::malignant cachexia:::muscle cachexia:::wasting disease:::pituitary cachexia:::wasting of neck:::nutritional wasting:::toxic confusional state:::wasting of arm:::chronic confusional state:::confusional state:::nutritionally compromised:::severe malnutrition:::chronic disease-related malnutrition:::malnutrition:::cancer-related fatigue:::cachectic alopecia:::starvation-related malnutrition:::tongue wasting:::extreme exhaustion:::heat exhaustion |
| hepatosplenomegaly | Symptom | 36760000 | hepatosplenomegaly | 36760000:::16294009:::19058002:::191382009:::80378000:::240630008:::190794006:::80515008:::36752001:::58639003:::240793000:::275598004:::94701003:::413808003:::27503000:::714254003:::127120007:::66789005:::51244008:::56338005:::123671009:::169149008 | hepatosplenomegaly:::splenomegaly:::congestive splenomegaly:::chronic congestive splenomegaly:::neonatal hepatosplenomegaly:::tropical splenomegaly syndrome:::gaucher splenomegaly:::hepatomegaly:::congenital splenomegaly:::neutropenic splenomegaly:::schistosomal splenomegaly:::hepatosplenomegalic lipoidosis:::mottled spleen:::ventriculomegaly:::constitutional hepatic dysfunction:::abdominal organomegaly:::hepatic lymphadenopathy:::hepatocellular jaundice:::splenic disorder:::fibrosis of spleen:::adrenal gland cytomegaly:::isotope scan spleen abnormal |
| pancytopenia | Symptom | 127034005 | pancytopenia | 127034005:::736024007:::5876000:::124961001:::417672002:::302215000:::38970002:::183005:::267524009:::267534000:::1396820003:::2897005:::51624005:::721119004:::48788004:::154826009:::415005004:::74576004:::768556005:::416902009:::191347008:::234487003:::371074009 | pancytopenia:::drug induced pancytopenia:::pancytopenia - acquired:::reticulocytopenia:::granulocytopenia:::thrombocytopenia:::splenic pancytopenia syndrome:::autoimmune pancytopenia:::pancytopenia with malformation:::primary thrombocytopenia:::intermittent thrombocytopenia:::immune thrombocytopenia:::dilutional thrombocytopenia:::pseudothrombocytopenia:::cyclic thrombocytopenia:::secondary thrombocytopenia:::panleukopenia:::acquired thrombocytopenia:::ataxia pancytopenia syndrome:::uraemic thrombocytopenia:::periodic neutropenia:::mediterranean thrombocytopenia:::radiation thrombocytopenia |
Model Information
| Model Name: | sbiobertresolve_snomed_findings_pipeline_20260901 |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 6.4.1+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 2.8 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel
- Chunk2Doc
- BertSentenceEmbeddings
- SentenceEntityResolverModel