Description
This model maps extracted clinical entities to ICD-10-CM codes using sbiobert_base_cased_mli_onnx sentence bert embeddings. In this model, synonyms having low cosine similarity to unnormalized terms are dropped. It returns billable and Hierarchical Condition Categories (HCC) information in the all_k_aux_labels parameter in the metadata. 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_slim_billable_hcc","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_slim_billable_hcc","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_slim_billable_hcc", "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 | all_k_aux_labels |
|:--------------------------------------|:---------|:---------------|:------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type 2 diabetes mellitus | PROBLEM | E11 | type 2 diabetes mellitus [type 2 diabetes mellitus] | E11:::E11.62:::E11.5:::E11.8:::E11.59:::E11.628:::E11.3:::E11.69:::E11.6:::E11.6... | 0.0000:::0.0549:::0.0621:::0.0648:::0.0666:::0.0787:::0.0792:::0.0792:::0.0806::... | type 2 diabetes mellitus [type 2 diabetes mellitus]:::type 2 diabetes mellitus w... | 0||0||0:::0||0||0:::0||0||0:::1||1||38:::1||1||37:::1||1||37:::0||0||0:::1||1||3... |
| essential hypertension | PROBLEM | I10 | essential (primary) hypertension [essential (primary) hypertension] | I10:::I15:::I1A.0:::I11:::H40.05:::I1A:::O10:::O10.0:::I16:::I16.1:::O10.9:::I15... | 0.0296:::0.0514:::0.0690:::0.0849:::0.0869:::0.0993:::0.1027:::0.1029:::0.1041::... | essential (primary) hypertension [essential (primary) hypertension]:::secondary ... | 1||0||0:::0||0||0:::1||0||0:::0||0||0:::0||0||0:::0||0||0:::0||0||0:::0||0||0:::... |
| acute appendicitis | PROBLEM | K35 | acute appendicitis [acute appendicitis] | K35:::K35.89:::K35.80:::K35.3:::K35.8:::K35.2:::K35.200:::K35.30:::K36:::K37:::K... | 0.0000:::0.0751:::0.0917:::0.1047:::0.1122:::0.1235:::0.1259:::0.1407:::0.1417::... | acute appendicitis [acute appendicitis]:::other acute appendicitis [other acute ... | 0||0||0:::0||0||0:::1||0||0:::0||0||0:::0||0||0:::0||0||0:::1||0||0:::1||0||0:::... |
| chronic obstructive pulmonary disease | PROBLEM | J44 | other chronic obstructive pulmonary disease [other chronic obstructive pulmonary disease] | J44:::J44.89:::J44.1:::J44.0:::J96.1:::J70.3:::J84.170:::I50.32:::I50.3:::J4A:::... | 0.0632:::0.0920:::0.1022:::0.1092:::0.1173:::0.1218:::0.1387:::0.1426:::0.1495::... | other chronic obstructive pulmonary disease [other chronic obstructive pulmonary... | 0||0||0:::1||1||280:::1||1||280:::1||1||280:::0||0||0:::1||0||0:::1||1||278:::1|... |
| major depressive disorder | PROBLEM | F33 | major depressive disorder, recurrent [major depressive disorder, recurrent] | F33:::F32:::F43.21:::F33.1:::F33.9:::F25.1:::F33.3:::F43.23:::F33.8:::F31.5:::F3... | 0.0434:::0.0586:::0.0636:::0.0793:::0.0954:::0.1006:::0.0976:::0.0985:::0.1064::... | major depressive disorder, recurrent [major depressive disorder, recurrent]:::de... | 0||0||0:::0||0||0:::1||0||0:::1||1||155:::1||0||0:::1||1||151:::1||1||152:::1||0... |
Model Information
| Model Name: | sbiobertresolve_icd10cm_slim_billable_hcc |
| Compatibility: | Healthcare NLP 6.4.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [icd10cm_code] |
| Language: | en |
| Size: | 444.3 MB |
| Case sensitive: | false |