Description
This model maps extracted medical entities to ICD-10-CM codes using mpnet_embeddings_biolord_2023_c MPNet Embeddings and it supports 7-digit codes with Hierarchical Condition Categories (HCC) status. In the result, look for the all_k_aux_labels parameter in the metadata to get HCC status: billable status, hcc status, and hcc score (e.g. 1||1||8). 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 = MPNetEmbeddings.pretrained("mpnet_embeddings_biolord_2023_c","en")\
.setInputCols(["ner_chunk_doc"])\
.setOutputCol("embeddings")\
.setCaseSensitive(False)
resolver = SentenceEntityResolverModel.pretrained("biolordresolve_icd10cm_augmented_billable_hcc","en","clinical/models")\
.setInputCols(["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.MPNetEmbeddings.pretrained("mpnet_embeddings_biolord_2023_c","en")\
.setInputCols(["ner_chunk_doc"])\
.setOutputCol("embeddings")\
.setCaseSensitive(False)
resolver = medical.SentenceEntityResolverModel.pretrained("biolordresolve_icd10cm_augmented_billable_hcc","en","clinical/models")\
.setInputCols(["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 = MPNetEmbeddings
.pretrained("mpnet_embeddings_biolord_2023_c", "en")
.setInputCols(Array("ner_chunk_doc"))
.setOutputCol("embeddings")
.setCaseSensitive(false)
val resolver = SentenceEntityResolverModel
.pretrained("biolordresolve_icd10cm_augmented_billable_hcc", "en", "clinical/models")
.setInputCols(Array("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 | E11.8 | disorder due to type 2 diabetes mellitus [type 2 diabetes mellitus with unspecified complications] | E11.8:::E11.9:::Z86.39:::E11.65:::Z79.4:::E11.6:::E66.9:::E11.64:::E13.9 | 0.0532:::0.0876:::0.0925:::0.0998:::0.1170:::0.1312:::0.1353:::0.1454:::0.1587 | disorder due to type 2 diabetes mellitus [type 2 diabetes mellitus with unspecif... | 1||1||38:::1||1||38:::1||0||0:::1||1||38:::1||1||38:::0||0||0:::1||0||0:::0||0||... |
| essential hypertension | PROBLEM | I10 | essential hypertension [essential (primary) hypertension] | I10:::O10.0:::P29.2:::I11.9:::O10.03:::I16:::I16.1 | 0.0925:::0.0953:::0.1958:::0.2210:::0.2442:::0.2446:::0.2662 | essential hypertension [essential (primary) hypertension]:::benign essential hyp... | 1||0||0:::0||0||0:::1||0||0:::1||0||0:::1||0||0:::0||0||0:::1||0||0 |
| acute appendicitis | PROBLEM | K35 | acute appendicitis [acute appendicitis] | K35:::K35.8:::K35.89:::K35.80:::K35.30:::K35.3:::K35.2:::K35.20:::K35.890:::K37:... | 0.0565:::0.0844:::0.0870:::0.0895:::0.0936:::0.0988:::0.1064:::0.1316:::0.1463::... | acute appendicitis [acute appendicitis]:::acute suppurative appendicitis [other ... | 0||0||0:::0||0||0:::0||0||0:::1||0||0:::1||0||0:::0||0||0:::0||0||0:::0||0||0:::... |
| chronic obstructive pulmonary disease | PROBLEM | J44.9 | copd - chronic obstructive pulmonary disease [chronic obstructive pulmonary disease, unspecified] | J44.9:::Z87.0:::J44:::J44.89:::J44.1:::J62.8 | 0.0960:::0.1268:::0.1356:::0.1601:::0.1810:::0.1991 | copd - chronic obstructive pulmonary disease [chronic obstructive pulmonary dise... | 1||1||280:::0||0||0:::0||0||0:::1||1||280:::1||1||280:::1||1||280 |
| major depressive disorder | PROBLEM | F33.3 | major depression with psychotic features [major depressive disorder, recurrent, severe with psychotic symptoms] | F33.3:::F32.9:::F32:::F32.2:::F32.8:::F06.3:::F32.1:::F32.3:::F32.0:::F33:::G70.... | 0.0834:::0.1032:::0.1113:::0.1165:::0.1268:::0.1726:::0.1767:::0.1785:::0.1859::... | major depression with psychotic features [major depressive disorder, recurrent, ... | 1||1||152:::1||0||0:::0||0||0:::1||1||155:::0||0||0:::0||0||0:::1||1||155:::1||1... |
Model Information
| Model Name: | biolordresolve_icd10cm_augmented_billable_hcc |
| Compatibility: | Healthcare NLP 6.4.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [embeddings] |
| Output Labels: | [icd10cm_code] |
| Language: | en |
| Size: | 1.3 GB |
| Case sensitive: | false |