Description
This model maps extracted medical entities to ICD10-CM codes using chunk embeddings (augmented with synonyms, four times richer than previous resolver). It also adds support of 7-digit codes with HCC status.
For reference: http://www.formativhealth.com/wp-content/uploads/2018/06/HCC-White-Paper.pdf
Predicted Entities
Outputs 7-digit billable ICD codes. In the result, look for aux_label parameter in the metadata to get HCC status. The HCC status can be divided to get further information: billable status, hcc status, and hcc score.
For example, in the example shared below the billable status is 1, hcc status is 1, and hcc score is 8.
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sbert_embedder = BertSentenceEmbeddings\
.pretrained("sbiobert_base_cased_mli","en","clinical/models")\
.setInputCols(["document"])\
.setOutputCol("sbert_embeddings")
icd10_resolver = SentenceEntityResolverModel.pretrained("sbert_biobertresolve_icd10cm_augmented_billable_hcc","en", "clinical/models") \
.setInputCols(["document", "sbert_embeddings"]) \
.setOutputCol("icd10cm_code")\
.setDistanceFunction("EUCLIDEAN").setReturnCosineDistances(True)
bert_pipeline_icd = PipelineModel(
stages = [
document_assembler,
sbert_embedder,
icd10_resolver])
model = nlpPipeline.fit(spark.createDataFrame([["metastatic lung cancer"]]).toDF("text"))
results = model.transform(data)
Results
| | chunks | code | resolutions | all_codes | billable_hcc_status_score | all_distances |
|---:|:-----------------------|:-------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------|:----------------------------|:-------------------------------------------------------------------------------------------------------------------------|
| 0 | metastatic lung cancer | C7800 | ['cancer metastatic to lung', 'metastasis from malignant tumor of lung', 'cancer metastatic to left lung', 'history of cancer metastatic to lung', 'metastatic cancer', 'history of cancer metastatic to lung (situation)', 'metastatic adenocarcinoma to bilateral lungs', 'cancer metastatic to chest wall', 'metastatic malignant neoplasm to left lower lobe of lung', 'metastatic carcinoid tumour', 'cancer metastatic to respiratory tract', 'metastatic carcinoid tumor'] | ['C7800', 'C349', 'C7801', 'Z858', 'C800', 'Z8511', 'C780', 'C798', 'C7802', 'C799', 'C7830', 'C7B00'] | ['1', '1', '8'] | ['0.0464', '0.0829', '0.0852', '0.0860', '0.0914', '0.0989', '0.1133', '0.1220', '0.1220', '0.1253', '0.1249', '0.1260'] |
Model Information
| Model Name: | sbert_biobertresolve_icd10cm_augmented_billable_hcc |
| Compatibility: | Spark NLP 2.7.3+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [icd10cm_code] |
| Language: | en |