Description
This model maps extracted medical entities to ICD-10-CM codes using sbiobert_base_cased_mli_onnx Sentence Bert Embeddings. Also, it has been augmented with synonyms to make it more accurate. Trained on the ICD-10-CM 20260401 (FY2026, effective April 1, 2026) code set.
Live Demo Open in Colab Copy S3 URI
How to use
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentenceDetector = 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("word_embeddings")
ner_model = MedicalNerModel.pretrained("ner_clinical","en","clinical/models")\
.setInputCols(["sentence","token","word_embeddings"])\
.setOutputCol("ner")
ner_converter = NerConverterInternal()\
.setInputCols(["sentence","token","ner"])\
.setOutputCol("ner_chunk")\
.setWhiteList(["PROBLEM"])
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("sentence_embeddings")\
.setCaseSensitive(False)
resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10cm_augmented","en","clinical/models")\
.setInputCols(["sentence_embeddings"])\
.setOutputCol("icd10cm_code")\
.setDistanceFunction("EUCLIDEAN")
pipeline = Pipeline(stages=[
documentAssembler, sentenceDetector, 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, presented with acute appendicitis and was noted to have chronic obstructive pulmonary disease on exam. Past psychiatric history notable for major depressive disorder."]]).toDF("text")
result = pipeline.fit(data).transform(data)
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("word_embeddings")
ner_model = medical.NerModel.pretrained("ner_clinical","en","clinical/models")\
.setInputCols(["sentence","token","word_embeddings"])\
.setOutputCol("ner")
ner_converter = medical.NerConverterInternal()\
.setInputCols(["sentence","token","ner"])\
.setOutputCol("ner_chunk")\
.setWhiteList(["PROBLEM"])
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("sentence_embeddings")\
.setCaseSensitive(False)
resolver = medical.SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10cm_augmented","en","clinical/models")\
.setInputCols(["sentence_embeddings"])\
.setOutputCol("icd10cm_code")\
.setDistanceFunction("EUCLIDEAN")
pipeline = nlp.Pipeline(stages=[
documentAssembler, sentenceDetector, 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, presented with acute appendicitis and was noted to have chronic obstructive pulmonary disease on exam. Past psychiatric history notable for major depressive disorder."]]).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("sentence")
.setOutputCol("token")
val word_embeddings = WordEmbeddingsModel
.pretrained("embeddings_clinical", "en", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("word_embeddings")
val ner_model = MedicalNerModel
.pretrained("ner_clinical", "en", "clinical/models")
.setInputCols(Array("sentence", "token", "word_embeddings"))
.setOutputCol("ner")
val ner_converter = new NerConverterInternal()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("ner_chunk")
.setWhiteList(Array("PROBLEM"))
val chunk2doc = new Chunk2Doc()
.setInputCols("ner_chunk")
.setOutputCol("ner_chunk_doc")
val embedder = BertSentenceEmbeddings
.pretrained("sbiobert_base_cased_mli_onnx", "en","clinical/models")
.setInputCols(Array("ner_chunk_doc"))
.setOutputCol("sentence_embeddings")
.setCaseSensitive(false)
val resolver = SentenceEntityResolverModel
.pretrained("sbiobertresolve_icd10cm_augmented", "en", "clinical/models")
.setInputCols(Array("sentence_embeddings"))
.setOutputCol("icd10cm_code")
.setDistanceFunction("EUCLIDEAN")
val pipeline = new Pipeline().setStages(Array(
documentAssembler, sentenceDetector, 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, presented with acute appendicitis and was noted to have chronic obstructive pulmonary disease on exam. Past psychiatric history notable for major depressive disorder.").toDF("text")
val res = pipeline.fit(data).transform(data)
Results
| ner_chunk | entity | icd10cm_code | resolution | all_k_results | all_k_cosine_distances | all_k_resolutions |
|:--------------------------------------|:---------|:---------------|:-------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type 2 diabetes mellitus | PROBLEM | E11 | type 2 diabetes mellitus [type 2 diabetes mellitus] | E11:::E11.9:::E11.8:::E11.3:::O24.11:::E13.9:::E11.43:::E10.9:::E11.69:::E11.4::... | 0.0000:::0.0134:::0.0212:::0.0357:::0.0360:::0.0397:::0.0455:::0.0462:::0.0499::... | type 2 diabetes mellitus [type 2 diabetes mellitus]:::diabetes mellitus type 2 [... |
| essential hypertension | PROBLEM | I10 | essential hypertension [essential (primary) hypertension] | I10:::I15.8:::Z87.5:::I15:::I15.2:::P29.2:::Z13.6 | 0.0000:::0.0446:::0.0489:::0.0514:::0.0541:::0.0580:::0.0605 | essential hypertension [essential (primary) hypertension]:::intermittent hyperte... |
| acute appendicitis | PROBLEM | K35 | acute appendicitis [acute appendicitis] | K35:::K35.8:::K35.2:::K35.89:::K35.20:::K36:::K37:::K35.80:::Z87.1 | 0.0000:::0.0218:::0.0331:::0.0379:::0.0385:::0.0414:::0.0579:::0.0604:::0.0627 | acute appendicitis [acute appendicitis]:::acute appendicitis (disorder) [other a... |
| chronic obstructive pulmonary disease | PROBLEM | J44.9 | chronic obstructive pulmonary disease [chronic obstructive pulmonary disease, unspecified] | J44.9:::J62.8:::J84.9:::I27.9:::J44:::J98.4:::J98.9:::Z76.89:::J98.0:::Z87.0:::Z... | 0.0000:::0.0433:::0.0481:::0.0622:::0.0632:::0.0641:::0.0687:::0.0717:::0.0755::... | chronic obstructive pulmonary disease [chronic obstructive pulmonary disease, un... |
| major depressive disorder | PROBLEM | F32.9 | major depressive disorder [major depressive disorder, single episode, unspecified] | F32.9:::F32.2:::F33:::F33.9:::F06.3:::F33.3:::F32.8:::F32.3:::F34.1:::F32:::F06.... | 0.0000:::0.0140:::0.0434:::0.0432:::0.0478:::0.0550:::0.0571:::0.0581:::0.0578::... | major depressive disorder [major depressive disorder, single episode, unspecifie... |
Model Information
| Model Name: | sbiobertresolve_icd10cm_augmented |
| Compatibility: | Healthcare NLP 6.4.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [icd10cm_code] |
| Language: | en |
| Size: | 1.3 GB |
| Case sensitive: | false |