Description
This model maps extracted medical entities to ICD-10-CM codes using sbiobert_base_cased_mli_onnx Sentence Bert Embeddings. It predicts ICD-10-CM codes up to 3 characters (according to ICD-10-CM code structure the first three characters represent general type of the injury or disease). 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("sbert_embeddings")\
.setCaseSensitive(False)
resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10cm_generalised_augmented","en","clinical/models")\
.setInputCols(["sbert_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("sbert_embeddings")\
.setCaseSensitive(False)
resolver = medical.SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10cm_generalised_augmented","en","clinical/models")\
.setInputCols(["sbert_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("sbert_embeddings")
.setCaseSensitive(false)
val resolver = SentenceEntityResolverModel
.pretrained("sbiobertresolve_icd10cm_generalised_augmented", "en", "clinical/models")
.setInputCols(Array("sbert_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:::O24:::E13:::E10:::Z86 | 0.0000:::0.0360:::0.0397:::0.0462:::0.0554 | type 2 diabetes mellitus [type 2 diabetes mellitus]:::pre-existing type 2 diabet... |
| essential hypertension | PROBLEM | I10 | essential hypertension [essential (primary) hypertension] | I10:::I15:::Z87:::P29:::Z13 | 0.0000:::0.0446:::0.0489:::0.0580:::0.0605 | essential hypertension [essential (primary) hypertension]:::intermittent hyperte... |
| acute appendicitis | PROBLEM | K35 | acute appendicitis [acute appendicitis] | K35:::K36:::K37:::Z87 | 0.0000:::0.0414:::0.0579:::0.0627 | acute appendicitis [acute appendicitis]:::subacute appendicitis [other appendici... |
| chronic obstructive pulmonary disease | PROBLEM | J44 | chronic obstructive pulmonary disease [other chronic obstructive pulmonary disease] | J44:::J62:::J84:::I27:::J98:::Z76:::Z87 | 0.0000:::0.0433:::0.0481:::0.0622:::0.0641:::0.0717:::0.0740 | chronic obstructive pulmonary disease [other chronic obstructive pulmonary disea... |
| major depressive disorder | PROBLEM | F32 | major depressive disorder [depressive episode] | F32:::F33:::F06:::F34:::F41:::F43:::F31 | 0.0000:::0.0434:::0.0478:::0.0578:::0.0649:::0.0636:::0.0647 | major depressive disorder [depressive episode]:::major depressive disorder, recu... |
Model Information
| Model Name: | sbiobertresolve_icd10cm_generalised_augmented |
| Compatibility: | Healthcare NLP 6.4.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sbert_embeddings] |
| Output Labels: | [icd10cm_code] |
| Language: | en |
| Size: | 1.3 GB |
| Case sensitive: | false |