Description
This model maps entities to HCPCS (Healthcare Common Procedure Coding System) codes using sbiobert_base_cased_mli_onnx Sentence Bert Embeddings. Trained on the current CMS HCPCS Level II Alpha-Numeric master file (release 20260701). This update adds an aux label (domain_id — the OMOP CDM domain, e.g. Device/Drug/Procedure/Observation/Measurement) alongside the resolved code, which the previous version of this model did not return.
Live Demo Open in Colab Copy S3 URI
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("ner_chunk")
sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli_onnx", "en", "clinical/models")\
.setInputCols(["ner_chunk"])\
.setOutputCol("sentence_embeddings")\
.setCaseSensitive(False)
hcpcs_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_hcpcs", "en", "clinical/models")\
.setInputCols(["sentence_embeddings"])\
.setOutputCol("hcpcs_code")\
.setDistanceFunction("EUCLIDEAN")
resolver_pipeline = Pipeline(stages=[
document_assembler, sbert_embedder, hcpcs_resolver
])
# 4 domain terms in one DataFrame (Device/Drug/Observation/Measurement) -- each row resolved
# independently, demonstrates the domain_id aux label across all 4 categories at once.
data = spark.createDataFrame([[t] for t in ['Unilateral breast prosthesis mastectomy bra with integrated form', 'Injection, brentuximab vedotin, 1 mg', 'Spirometry results showing fev1/fvc below 70%', 'Alcohol and/or drug screening']], ["text"])
result = resolver_pipeline.fit(data).transform(data)
document_assembler = nlp.DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("ner_chunk")
sbert_embedder = nlp.BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli_onnx", "en", "clinical/models")\
.setInputCols(["ner_chunk"])\
.setOutputCol("sentence_embeddings")\
.setCaseSensitive(False)
hcpcs_resolver = medical.SentenceEntityResolverModel.pretrained("sbiobertresolve_hcpcs", "en", "clinical/models")\
.setInputCols(["sentence_embeddings"])\
.setOutputCol("hcpcs_code")\
.setDistanceFunction("EUCLIDEAN")
resolver_pipeline = nlp.Pipeline(stages=[
document_assembler, sbert_embedder, hcpcs_resolver
])
# 4 domain terms in one DataFrame (Device/Drug/Observation/Measurement) -- each row resolved
# independently, demonstrates the domain_id aux label across all 4 categories at once.
data = spark.createDataFrame([[t] for t in ['Unilateral breast prosthesis mastectomy bra with integrated form', 'Injection, brentuximab vedotin, 1 mg', 'Spirometry results showing fev1/fvc below 70%', 'Alcohol and/or drug screening']], ["text"])
result = resolver_pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("ner_chunk")
val sbertEmbedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli_onnx", "en", "clinical/models")
.setInputCols(Array("ner_chunk"))
.setOutputCol("sentence_embeddings")
.setCaseSensitive(false)
val hcpcsResolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_hcpcs", "en", "clinical/models")
.setInputCols(Array("sentence_embeddings"))
.setOutputCol("hcpcs_code")
.setDistanceFunction("EUCLIDEAN")
val resolverPipeline = new Pipeline().setStages(Array(
documentAssembler, sbertEmbedder, hcpcsResolver
))
// 4 domain terms in one DataFrame (Device/Drug/Observation/Measurement) -- each row resolved
// independently, demonstrates the domain_id aux label across all 4 categories at once.
val data = Seq("Unilateral breast prosthesis mastectomy bra with integrated form", "Injection, brentuximab vedotin, 1 mg", "Spirometry results showing fev1/fvc below 70%", "Alcohol and/or drug screening").toDF("text")
val result = resolverPipeline.fit(data).transform(data)
Results
| ner_chunk | hcpcs_code | resolution | all_k_results | all_k_cosine_distances | all_k_resolutions | all_k_aux_labels |
|:-----------------------------------------------------------------|:-------------|:----------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| Unilateral breast prosthesis mastectomy bra with integrated form | L8001 | Breast prosthesis, mastectomy bra, with integrated breast prosthesis form, unilateral, any size, any type | L8001:::L8020:::L8015:::S2066:::S2067:::S2068:::L8035:::L8002:::G9704:::L8031:::... | 0.1237:::0.1296:::0.1594:::0.1629:::0.1837:::0.1913:::0.2018:::0.2127:::0.2162::... | Breast prosthesis, mastectomy bra, with integrated breast prosthesis form, unila... | Device:::Device:::Device:::Procedure:::Procedure:::Procedure:::Device:::Device::... |
| Injection, brentuximab vedotin, 1 mg | J9042 | Injection, brentuximab vedotin, 1 mg | J9042:::J9326:::J0179:::J1823:::J0584:::J9273:::J2327:::Q9997:::J9347:::J9350:::... | 0.0000:::0.0502:::0.0497:::0.0504:::0.0555:::0.0612:::0.0599:::0.0619:::0.0617::... | Injection, brentuximab vedotin, 1 mg:::Injection, telisotuzumab vedotin-tllv, 1 ... | Drug:::Drug:::Drug:::Drug:::Drug:::Drug:::Drug:::Drug:::Drug:::null:::Drug:::Dru... |
| Spirometry results showing fev1/fvc below 70% | G8924 | Spirometry results documented (fev1/fvc < 70%) | G8924:::M1214:::A7006:::M1213:::G8395:::M1371:::G8694:::A4306:::G9243:::G8934:::... | 0.0521:::0.1662:::0.3001:::0.2925:::0.3165:::0.3009:::0.3166:::0.3170:::0.3217::... | Spirometry results documented (fev1/fvc < 70%):::Spirometry results with confirm... | Observation:::Observation:::Device:::Observation:::Observation:::Observation:::O... |
| Alcohol and/or drug screening | H0049 | Alcohol and/or drug screening | H0049:::H0001:::H0006:::H0003:::H0022:::T1007:::T1012:::H0014:::H0020:::H0005:::... | 0.0000:::0.0354:::0.0829:::0.0930:::0.1171:::0.1172:::0.1228:::0.1318:::0.1711::... | Alcohol and/or drug screening:::Alcohol and/or drug assessment:::Alcohol and/or ... | Measurement:::Procedure:::Procedure:::Measurement:::Procedure:::Observation:::Ob... |
Model Information
| Model Name: | sbiobertresolve_hcpcs |
| Compatibility: | Healthcare NLP 6.4.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [hcpcs_code] |
| Language: | en |
| Size: | 21.3 MB |
| Case sensitive: | false |