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  • John Snow Labs
  • Mar 16, 2026

Sentence Entity Resolver for SNOMED (sbiobertresolve_snomed_findings_aux_concepts)

  • en
  • snomed
  • resolver
  • licensed
  • clinical
  • findings
  • auxconcepts

Description

This model maps clinical conditions to their corresponding SNOMED codes using sbiobert_base_cased_mli Sentence Bert Embeddings.

Open in Colab Copy S3 URI

How to use



document_assembler = DocumentAssembler()\
      .setInputCol("text")\
      .setOutputCol("document")

sentenceDetectorDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare", "en", "clinical/models")\
      .setInputCols(["document"])\
      .setOutputCol("sentence")

tokenizer = Tokenizer()\
      .setInputCols(["sentence"])\
      .setOutputCol("token")

word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
      .setInputCols(["sentence", "token"])\
      .setOutputCol("embeddings")

ner_jsl  = MedicalNerModel.pretrained("ner_jsl", "en", "clinical/models")\
      .setInputCols(["sentence", "token", "embeddings"])\
      .setOutputCol("ner_jsl")


ner_jsl_converter  = NerConverterInternal()\
      .setInputCols(["sentence", "token", "ner_jsl"])\
      .setOutputCol("ner_jsl_chunk")\
      .setWhiteList(["Procedure","Kidney_Disease","Cerebrovascular_Disease","Heart_Disease",
                     "Disease_Syndrome_Disorder", "ImagingFindings", "Symptom", "VS_Finding",
                     "EKG_Findings", "Communicable_Disease","Substance","Drug_Ingredient",
                     "Internal_organ_or_component","External_body_part_or_region","Modifier",
                     "Triglycerides","Alcohol","Smoking","Pregnancy","Hypertension","Obesity",
                     "Injury_or_Poisoning","Test","Hyperlipidemia","BMI","Oncological","Psychological_Condition","LDL","Diabetes"])

chunk2doc = Chunk2Doc()\
    .setInputCols("ner_jsl_chunk")\
    .setOutputCol("ner_chunk_doc")

sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "en", "clinical/models")\
    .setInputCols(["ner_chunk_doc"])\
    .setOutputCol("sbert_embeddings")\
    .setCaseSensitive(False)

snomed_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_snomed_findings_aux_concepts", "en", "clinical/models") \
    .setInputCols(["sbert_embeddings"]) \
    .setOutputCol("snomed_code")

snomed_pipeline = Pipeline(stages = [
    document_assembler,
    sentence_detector,
    tokenizer,
    word_embeddings,
    ner_jsl,
    ner_jsl_converter,
    chunk2doc,
    sbert_embedder,
    snomed_resolver
])


sample_text = """This is an 82-year-old male with a history of prior tobacco use, hypertension, chronic renal insufficiency, COPD, gastritis, and TIA. He initially presented to Braintree with a nonspecific ST-T abnormality and was transferred to St. Margaret’s Center. He underwent cardiac catheterization because of occlusion of the mid left anterior descending coronary artery lesion, which was complicated by hypotension and bradycardia. He required atropine, IV fluids, and dopamine, possibly secondary to a vagal reaction. He was subsequently transferred to the CCU for close monitoring. He was hemodynamically stable at the time of admission to the CCU."""

df= spark.createDataFrame([[sample_text]]).toDF("text")

result= nlpPipeline.fit(df).transform(df)




documentAssembler = nlp.DocumentAssembler()\
      .setInputCol("text")\
      .setOutputCol("document")

sentenceDetector = nlp.SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")\
      .setInputCols(["document"])\
      .setOutputCol("sentence")

tokenizer = nlp.Tokenizer() \
      .setInputCols(["sentence"]) \
      .setOutputCol("token")

word_embeddings = nlp.WordEmbeddingsModel.pretrained("embeddings_clinical","en", "clinical/models")\
      .setInputCols(["sentence", "token"])\
      .setOutputCol("embeddings")

ner_jsl = medical.NerModel.pretrained("ner_jsl", "en", "clinical/models") \
      .setInputCols(["sentence", "token", "embeddings"]) \
      .setOutputCol("ner_jsl")

ner_jsl_converter   = medical.NerConverterInternal()\
      .setInputCols(["sentence", "token", "ner_jsl"])\
      .setOutputCol("ner_jsl_chunk")\
      .setWhiteList(["Procedure","Kidney_Disease","Cerebrovascular_Disease","Heart_Disease",
                     "Disease_Syndrome_Disorder", "ImagingFindings", "Symptom", "VS_Finding",
                     "EKG_Findings", "Communicable_Disease","Substance","Drug_Ingredient",
                     "Internal_organ_or_component","External_body_part_or_region","Modifier",
                     "Triglycerides","Alcohol","Smoking","Pregnancy","Hypertension","Obesity",
                     "Injury_or_Poisoning","Test","Hyperlipidemia","BMI","Oncological","Psychological_Condition","LDL","Diabetes"])

chunk2doc = nlp.Chunk2Doc() \
      .setInputCols("ner_jsl_chunk") \
      .setOutputCol("ner_chunk_doc")

sbert_embedder = nlp.BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli","en","clinical/models")\
     .setInputCols(["ner_chunk_doc"])\
     .setOutputCol("sbert_embeddings")\
     .setCaseSensitive(False)

snomed_resolver = medical.SentenceEntityResolverModel.pretrained("sbiobertresolve_snomed_findings_aux_concepts", "en", "clinical/models") \
     .setInputCols(["sbert_embeddings"]) \
     .setOutputCol("snomed_code")\
     .setDistanceFunction("EUCLIDEAN")

nlpPipeline= nlp.Pipeline(stages = [
    documentAssembler,
    sentenceDetector,
    tokenizer,
    word_embeddings,
    ner_jsl,
    ner_jsl_converter,
    chunk2doc,
    sbert_embedder,
    snomed_resolver
])

sample_text = """This is an 82-year-old male with a history of prior tobacco use, hypertension, chronic renal insufficiency, COPD, gastritis, and TIA. He initially presented to Braintree with a nonspecific ST-T abnormality and was transferred to St. Margaret’s Center. He underwent cardiac catheterization because of occlusion of the mid left anterior descending coronary artery lesion, which was complicated by hypotension and bradycardia. He required atropine, IV fluids, and dopamine, possibly secondary to a vagal reaction. He was subsequently transferred to the CCU for close monitoring. He was hemodynamically stable at the time of admission to the CCU."""

df= spark.createDataFrame([[sample_text]]).toDF("text")

result= nlpPipeline.fit(df).transform(df)






val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")
  .setInputCols(Array("document"))
  .setOutputCol("sentence")

val tokenizer = new Tokenizer()
  .setInputCols(Array("sentence"))
  .setOutputCol("token")

val wordEmbeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
  .setInputCols(Array("sentence", "token"))
  .setOutputCol("embeddings")

val nerJsl = MedicalNerModel.pretrained("ner_jsl", "en", "clinical/models")
  .setInputCols(Array("sentence", "token", "embeddings"))
  .setOutputCol("ner_jsl")

val nerJslConverter = new NerConverter()
  .setInputCols(Array("sentence", "token", "ner_jsl"))
  .setOutputCol("ner_jsl_chunk")
  .setWhiteList(Array("Procedure","Kidney_Disease","Cerebrovascular_Disease","Heart_Disease",
                     "Disease_Syndrome_Disorder", "ImagingFindings", "Symptom", "VS_Finding",
                     "EKG_Findings", "Communicable_Disease","Substance","Drug_Ingredient",
                     "Internal_organ_or_component","External_body_part_or_region","Modifier",
                     "Triglycerides","Alcohol","Smoking","Pregnancy","Hypertension","Obesity",
                     "Injury_or_Poisoning","Test","Hyperlipidemia","BMI","Oncological","Psychological_Condition","LDL","Diabetes"))

val chunk2doc = new Chunk2Doc()
  .setInputCols(Array("ner_jsl_chunk"))
  .setOutputCol("ner_chunk_doc")

val sbertEmbedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "en", "clinical/models")
  .setInputCols(Array("ner_chunk_doc"))
  .setOutputCol("sbert_embeddings")
  .setCaseSensitive(false)

val snomedResolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_snomed_findings_aux_concepts", "en", "clinical/models")
  .setInputCols(Array("sbert_embeddings"))
  .setOutputCol("snomed_code")
  .setDistanceFunction("EUCLIDEAN")

val nlpPipeline = new Pipeline().setStages(Array(
  documentAssembler,
  sentenceDetector,
  tokenizer,
  wordEmbeddings,
  nerJsl,
  nerJslConverter,
  chunk2doc,
  sbertEmbedder,
  snomedResolver
))


val sample_text = """This is an 82-year-old male with a history of prior tobacco use, hypertension, chronic renal insufficiency, COPD, gastritis, and TIA. He initially presented to Braintree with a nonspecific ST-T abnormality and was transferred to St. Margaret’s Center. He underwent cardiac catheterization because of occlusion of the mid left anterior descending coronary artery lesion, which was complicated by hypotension and bradycardia. He required atropine, IV fluids, and dopamine, possibly secondary to a vagal reaction. He was subsequently transferred to the CCU for close monitoring. He was hemodynamically stable at the time of admission to the CCU."""

val df= Seq(sample_text).toDF("text")

val result= nlpPipeline.fit(df).transform(df)

Results


| sent_id | ner_chunk                                          | entity                    | snomed_code | resolution                                         | all_codes                                          | all_resolutions                                    | aux_list                                           |
|---------|----------------------------------------------------|---------------------------|-------------|----------------------------------------------------|----------------------------------------------------|----------------------------------------------------|----------------------------------------------------|
| 0       | tobacco                                            | Smoking                   | 57264008    | tobacco                                            | ['57264008', '102407002', '39953003', '11048300... | ['tobacco', 'tobacco smoke', 'tobacco - substan... | ['Organism', 'Substance', 'Substance', 'Clinica... |
| 0       | hypertension                                       | Hypertension              | 38341003    | hypertension                                       | ['38341003', '417312002', '59621000', '27044000... | ['hypertension', 'hypertension suspected', 'ess... | ['Disorder', 'Context-dependent', 'Disorder', '... |
| 0       | chronic renal insufficiency                        | Kidney_Disease            | 723190009   | chronic renal insufficiency                        | ['723190009', '709044004', '90688005', '4253690... | ['chronic renal insufficiency', 'chronic renal ... | ['Disorder', 'Disorder', 'Disorder', 'Disorder'... |
| 0       | COPD                                               | Disease_Syndrome_Disorder | 13645005    | copd                                               | ['13645005', '414400006', '223821008', '6034900... | ['copd', 'coning', 'cos', 'ump', 'ling', 'chive... | ['Disorder', 'Disorder', 'Location', 'Substance... |
| 0       | gastritis                                          | Disease_Syndrome_Disorder | 4556007     | gastritis                                          | ['4556007', '413241009', '235656001', '10867910... | ['gastritis', 'gastritis suspected', 'chemical ... | ['Disorder', 'Context-dependent', 'Disorder', '... |
| 0       | TIA                                                | Cerebrovascular_Disease   | 266257000   | tia                                                | ['266257000', '31597007', '160363004', '3961450... | ['tia', 'tui', 'fh: tia', 'tidac', 'tu', 'trna'... | ['Disorder', 'Organism', 'Context-dependent', '... |
| 1       | nonspecific                                        | Modifier                  | 10003008    | non-specific                                       | ['10003008', '261992003', '863956004', '3008440... | ['non-specific', 'non-biological', 'non-sterile... | ['Qualifier Value', 'Qualifier Value', 'Qualifi... |
| 1       | ST-T abnormality                                   | EKG_Findings              | 428750005   | nonspecific st-t abnormality                       | ['428750005', '399504009', '55930002', '4557210... | ['nonspecific st-t abnormality', 'clinical t ca... | ['Clinical Finding', 'Observable Entity', 'Clin... |
| 2       | cardiac catheterization                            | Procedure                 | 41976001    | cardiac catheterization                            | ['41976001', '705923009', '721968000', '4677350... | ['cardiac catheterization', 'cardiac catheter',... | ['Procedure', 'Physical Object', 'Record Artifa... |
| 2       | occlusion of the mid left anterior descending c... | Symptom                   | 840313007   | occlusion of mid left anterior descending coron... | ['840313007', '44771000087108', '840315000', '4... | ['occlusion of mid left anterior descending cor... | ['Disorder', 'Disorder', 'Disorder', 'Disorder'... |
| 2       | hypotension                                        | VS_Finding                | 45007003    | hypotension                                        | ['45007003', '241727003', '19721008', '28651003... | ['hypotension', 'induced hypotension', 'globe h... | ['Disorder', 'Procedure', 'Disorder', 'Disorder... |
| 2       | bradycardia                                        | VS_Finding                | 48867003    | bradycardia                                        | ['48867003', '49710005', '44273001', '42177007'... | ['bradycardia', 'sinus bradycardia', 'reflex br... | ['Clinical Finding', 'Disorder', 'Clinical Find... |
| 3       | atropine                                           | Drug_Ingredient           | 73949004    | atropine                                           | ['73949004', '105075009', '349945006', '4104930... | ['atropine', 'atropine measurement', 'oral atro... | ['Pharma/Biol Product', 'Procedure', 'Clinical ... |
| 3       | fluids                                             | Drug_Ingredient           | 255765007   | fluid                                              | ['255765007', '246498002', '258442002', '251851... | ['fluid', 'fluid used', 'fluid sample', 'fluid ... | ['Qualifier Value', 'Attribute', 'Specimen', 'O... |
| 3       | dopamine                                           | Drug_Ingredient           | 59187003    | dopamine                                           | ['59187003', '412383006', '37484001', '32779004... | ['dopamine', 'dopamine agent', 'dopamine recept... | ['Pharma/Biol Product', 'Substance', 'Substance... |
| 3       | vagal reaction                                     | Symptom                   | 128968000   | vagal stimulation                                  | ['128968000', '398665005', '106160007', '888820... | ['vagal stimulation', 'vaso vagal episode', 'va... | ['Procedure', 'Clinical Finding', 'Clinical Fin... |

Model Information

Model Name: sbiobertresolve_snomed_findings_aux_concepts
Compatibility: Healthcare NLP 6.3.0+
License: Licensed
Edition: Official
Input Labels: [sentence_embeddings]
Output Labels: [snomed_code]
Language: en
Size: 2.3 GB
Case sensitive: false