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  • John Snow Labs
  • Dec 03, 2025

Sentence Entity Resolver for SNOMED Codes ( bge_base_en_v1_5_onnx )

  • resolver
  • snomed
  • en
  • licensed
  • clinical
  • bge
  • sentence_entity_resolver

Description

This is a Sentence Entity Resolver model that maps clinical entities to SNOMED codes using bge_base_en_v1_5_onnx embeddings. It leverages contextual embeddings to improve code resolution accuracy for medical concepts, including diseases, symptoms, procedures, and drugs.

Predicted Entities

SNOMED Codes

Copy S3 URI

How to use


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

sentence_detector = 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", "Hypertension", "Obesity",
                   "Injury_or_Poisoning","Test","Hyperlipidemia","BMI","Oncological",
                   "Psychological_Condition", "LDL", "Diabetes"])

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

bge_embeddings = BGEEmbeddings.pretrained("bge_base_en_v1_5_onnx", "en")\
    .setInputCols(["ner_chunk_doc"])\
    .setOutputCol("bge_embeddings")

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

pipeline = Pipeline().setStages([
    document_assembler,
    sentence_detector,
    tokenizer,
    word_embeddings,
    ner_jsl,
    ner_jsl_converter,
    chunk2doc,
    bge_embeddings,
    snomed_resolver
])

text = """John's doctor prescribed ofloxacin for his secondary conjunctivitis, cefixime for his cystic urethritis, ibuprofen for his inflammation, and cilnidipine for his hypertension on 2023-12-01."""

data = spark.createDataFrame([[text]]).toDF("text")

result = pipeline.fit(data).transform(data)



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

sentence_detector = 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.NerConverter()\
    .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", "Hypertension", "Obesity",
                   "Injury_or_Poisoning","Test","Hyperlipidemia","BMI","Oncological",
                   "Psychological_Condition", "LDL"," Diabetes"])

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

bge_embeddings = nlp.BGEEmbeddings.pretrained("bge_base_en_v1_5_onnx", "en")\
    .setInputCols(["ner_chunk_doc"])\
    .setOutputCol("bge_embeddings")

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

pipeline = nlp.Pipeline().setStages([
    document_assembler,
    sentence_detector,
    tokenizer,
    word_embeddings,
    ner_jsl,
    ner_jsl_converter,
    chunk2doc,
    bge_embeddings,
    snomed_resolver
])

text = """John's doctor prescribed ofloxacin for his secondary conjunctivitis, cefixime for his cystic urethritis, ibuprofen for his inflammation, and cilnidipine for his hypertension on 2023-12-01."""

data = spark.createDataFrame([[text]]).toDF("text")

result = pipeline.fit(data).transform(data)



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 NerConverterInternal()
    .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", "Hypertension", "Obesity",
                        "Injury_or_Poisoning","Test","Hyperlipidemia","BMI","Oncological",
                        "Psychological_Condition", "LDL", "Diabetes"))

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

val bgeEmbeddings = BGEEmbeddings.pretrained("bge_base_en_v1_5_onnx", "en")
    .setInputCols(Array("ner_chunk_doc"))
    .setOutputCol("bge_embeddings")

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

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

val data = Seq("""John's doctor prescribed ofloxacin for his secondary conjunctivitis, cefixime for his cystic urethritis, ibuprofen for his inflammation, and cilnidipine for his hypertension on 2023-12-01.""").toDF("text")

val result = pipeline.fit(data).transform(data)

Results


|sent_id|ner_chunk        |entity                   |snomed_code|resolutions       |all_codes                                      |all_resolutions                                |
|-------|-----------------|-------------------------|-----------|------------------|-----------------------------------------------|-----------------------------------------------|
|0      |ofloxacin        |Drug_Ingredient          |387551000  |ofloxacin         |[387551000, 96086002, 392417006, 392415003, ...]|[ofloxacin, ofloxacin product, parenteral oflo...]|
|0      |secondary        |Modifier                 |2603003    |secondary         |[2603003, 262134003, 721071000000106, ...]     |[secondary, secondary procedure, secondary and...]|
|0      |conjunctivitis   |Disease_Syndrome_Disorder|9826008    |conjunctivitis    |[9826008, 473460002, 45261009, 128350005, ...] |[conjunctivitis, allergic conjunctivitis, vira...]|
|0      |cefixime         |Drug_Ingredient          |387536009  |cefixime          |[387536009, 96052006, 713750001, 294548002, ...]|[cefixime, cefixime product, oral form cefixim...]|
|0      |cystic urethritis|Disease_Syndrome_Disorder|1259233009 |cystic urethritis |[1259233009, 70795003, 31822004, 429728004, ...]|[cystic urethritis, urethral cyst, urethritis,...]|
|0      |ibuprofen        |Drug_Ingredient          |387207008  |ibuprofen         |[387207008, 350321003, 293619005, 38268001, ...]|[ibuprofen, oral ibuprofen, ibuprofen allergy,...]|
|0      |inflammation     |Symptom                  |257552002  |inflammation      |[257552002, 225540005, 274144001, 3723001, ...] |[inflammation, wound inflammation, inflammatio...]|
|0      |cilnidipine      |Drug_Ingredient          |1177123004 |cilnidipine       |[1177123004, 1179035008, 1179037000, ...]      |[cilnidipine, cilnidipine-containing product, ...]|
|0      |hypertension     |Hypertension             |38341003   |hypertension      |[38341003, 75367002, 73578008, 28119000, ...]  |[hypertension, blood pressure, hyperdistension...]|

Model Information

Model Name: bgeresolve_snomed
Compatibility: Healthcare NLP 6.2.0+
License: Licensed
Edition: Official
Input Labels: [bge_embeddings]
Output Labels: [snomed_code]
Language: en
Size: 2.7 GB
Case sensitive: false