Mapping Entities with Corresponding SNOMED Codes

Description

This model maps clinical entities to their corresponding SNOMED codes. It provides fast and accurate clinical code mapping without requiring embeddings.

Predicted Entities

SNOMED Codes

Download 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_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"])

snomed_mapper = ChunkMapperModel.pretrained("snomed_mapper", "en", "clinical/models")\
    .setInputCols(["ner_chunk"])\
    .setOutputCol("mappings")\
    .setRels(["snomed_code"])

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

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_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"])

snomed_mapper = medical.ChunkMapperModel.pretrained("snomed_mapper", "en", "clinical/models")\
    .setInputCols(["ner_chunk"])\
    .setOutputCol("mappings")\
    .setRels(["snomed_code"])

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

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_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 snomedMapper = ChunkMapperModel.pretrained("snomed_mapper", "en", "clinical/models")
    .setInputCols(Array("ner_chunk"))
    .setOutputCol("mappings")
    .setRels(Array("snomed_code"))

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

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

|ner_chunk        |mapping_result|
|-----------------|--------------|
|ofloxacin        |387551000     |
|secondary        |2603003       |
|conjunctivitis   |9826008       |
|cefixime         |387536009     |
|cystic urethritis|1259233009    |
|ibuprofen        |387207008     |
|inflammation     |257552002     |
|cilnidipine      |1177123004    |
|hypertension     |38341003      |

Model Information

Model Name:	snomed_mapper
Compatibility:	Healthcare NLP 6.2.0+
License:	Licensed
Edition:	Official
Input Labels:	[ner_chunk]
Output Labels:	[mappings]
Language:	en
Size:	26.6 MB

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