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  • John Snow Labs
  • Sep 11, 2026

Sentence Entity Resolver for SNOMED CT (Conditions) (sbiobert_base_cased_mli_onnx embeddings)

  • en
  • snomed
  • resolver
  • licensed
  • clinical
  • conditions
  • sbiobert

Description

This model maps extracted clinical NER entities to SNOMED CT concepts using sbiobert_base_cased_mli_onnx embeddings.

It is trained on SNOMED CT US Edition 20260901 release.

Live Demo Open in Colab Copy S3 URI

How to use

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

ner_converter = NerConverterInternal()\
    .setInputCols(["sentence","token","ner_tags"])\
    .setOutputCol("ner_chunk")\
    .setWhiteList(["Kidney_Disease", "Cerebrovascular_Disease", "Heart_Disease", "Disease_Syndrome_Disorder", "ImagingFindings", "Symptom", "VS_Finding", "EKG_Findings", "Communicable_Disease", "Pregnancy", "Obesity", "Hypertension", "Overweight", "Hyperlipidemia", "Triglycerides", "Diabetes", "Oncological", "Psychological_Condition", "Injury_or_Poisoning"])

chunk2doc = Chunk2Doc()\
    .setInputCols(["ner_chunk"])\
    .setOutputCol("ner_chunk_doc")

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

resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_snomed_conditions_20260901","en","clinical/models")\
    .setInputCols(["sbert_embeddings"])\
    .setOutputCol("snomed_code")\
    .setDistanceFunction("EUCLIDEAN")\
    .setThreshold(1000)

pipeline = Pipeline(stages=[\
    documentAssembler, sentenceDetectorDL, tokenizer, word_embeddings, ner_model, ner_converter, chunk2doc, embedder, resolver\
])

data = spark.createDataFrame([["The patient has a history of type 2 diabetes mellitus and essential hypertension. She was admitted with an acute myocardial infarction and later diagnosed with hyperlipidemia."]]).toDF("text")
result = pipeline.fit(data).transform(data)

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

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

ner_converter = medical.NerConverterInternal()\
    .setInputCols(["sentence","token","ner_tags"])\
    .setOutputCol("ner_chunk")\
    .setWhiteList(["Kidney_Disease", "Cerebrovascular_Disease", "Heart_Disease", "Disease_Syndrome_Disorder", "ImagingFindings", "Symptom", "VS_Finding", "EKG_Findings", "Communicable_Disease", "Pregnancy", "Obesity", "Hypertension", "Overweight", "Hyperlipidemia", "Triglycerides", "Diabetes", "Oncological", "Psychological_Condition", "Injury_or_Poisoning"])

chunk2doc = nlp.Chunk2Doc()\
    .setInputCols(["ner_chunk"])\
    .setOutputCol("ner_chunk_doc")

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

resolver = medical.SentenceEntityResolverModel.pretrained("sbiobertresolve_snomed_conditions_20260901","en","clinical/models")\
    .setInputCols(["sbert_embeddings"])\
    .setOutputCol("snomed_code")\
    .setDistanceFunction("EUCLIDEAN")\
    .setThreshold(1000)

pipeline = nlp.Pipeline(stages=[\
    documentAssembler, sentenceDetectorDL, tokenizer, word_embeddings, ner_model, ner_converter, chunk2doc, embedder, resolver\
])

data = spark.createDataFrame([["The patient has a history of type 2 diabetes mellitus and essential hypertension. She was admitted with an acute myocardial infarction and later diagnosed with hyperlipidemia."]]).toDF("text")
result = pipeline.fit(data).transform(data)


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

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

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

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

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

val ner_converter = new NerConverterInternal()
    .setInputCols(Array("sentence", "token", "ner_tags"))
    .setOutputCol("ner_chunk")
    .setWhiteList(Array("Kidney_Disease", "Cerebrovascular_Disease", "Heart_Disease", "Disease_Syndrome_Disorder", "ImagingFindings", "Symptom", "VS_Finding", "EKG_Findings", "Communicable_Disease", "Pregnancy", "Obesity", "Hypertension", "Overweight", "Hyperlipidemia", "Triglycerides", "Diabetes", "Oncological", "Psychological_Condition", "Injury_or_Poisoning"))

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

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

val resolver = SentenceEntityResolverModel
    .pretrained("sbiobertresolve_snomed_conditions_20260901", "en", "clinical/models")
    .setInputCols(Array("sbert_embeddings"))
    .setOutputCol("snomed_code")
    .setDistanceFunction("EUCLIDEAN")
    .setThreshold(1000)

val pipeline = new Pipeline().setStages(Array(
    documentAssembler, sentenceDetectorDL, tokenizer, word_embeddings, ner_model, ner_converter, chunk2doc, embedder, resolver
))

val data = Seq("The patient has a history of type 2 diabetes mellitus and essential hypertension. She was admitted with an acute myocardial infarction and later diagnosed with hyperlipidemia.").toDF("text")
val res = pipeline.fit(data).transform(data)

Results

| chunk                    | label          |   snomed_code | resolution               | all_codes                                                                           | all_resolutions                                                                     |
|:-------------------------|:---------------|--------------:|:-------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type 2 diabetes mellitus | Diabetes       |      44054006 | type 2 diabetes mellitus | 44054006:::199230006:::422014003:::8801005:::73211009:::422099009:::443694000:::... | type 2 diabetes mellitus:::pre-existing type 2 diabetes mellitus:::disorder due ... |
| essential hypertension   | Hypertension   |      59621000 | essential hypertension   | 59621000:::371125006:::38341003:::697929007:::19769006:::31992008:::429457004:::... | essential hypertension:::labile essential hypertension:::hypertensive disorder::... |
| myocardial infarction    | Heart_Disease  |      22298006 | myocardial infarction    | 22298006:::380001000004106:::57054005:::466635291000119109:::194856005:::1648650... | myocardial infarction:::subendocardial myocardial infarction:::acute myocardial ... |
| hyperlipidemia           | Hyperlipidemia |      55822004 | hyperlipidemia           | 55822004:::190774002:::129589009:::267434003:::402727002:::129590000:::238089003... | hyperlipidemia:::hyperlipidemia, group a:::endogenous hyperlipidemia:::multiple-... |

Model Information

Model Name: sbiobertresolve_snomed_conditions_20260901
Compatibility: Healthcare NLP 6.4.1+
License: Licensed
Edition: Official
Input Labels: [sbert_embeddings]
Output Labels: [snomed_code]
Language: en
Size: 597.8 MB
Case sensitive: false