Enhancing Automated Medical Coding: Evaluating Embedding Models for ICD-10-CM Code Mapping

Abstract

AbstractPurposeThe goal of this study is to enhance automated medical coding (AMC) by evaluating the effectiveness of modern embedding models in capturing semantic similarity and improving the retrieval process for ICD-10-CM code mapping. Achieving consistent and accurate medical coding practices is crucial for effective healthcare management.MethodsWe compared the performance of embedding models, including text-embedding-3-large, text-embedding-004, voyage-large-2-instruct, and mistralembed, against ClinicalBERT. These models were assessed for their ability to capture semantic similarity between long and short ICD-10-CM descriptions and to improve the retrieval process for mapping diagnosis strings from the eICU database to the correct ICD-10-CM codes.ResultsThe text-embedding-3-large and text-embedding-004 models outperformed ClinicalBERT in capturing semantic similarity, with text-embedding-3-large achieving the highest accuracy. For ICD-10 code retrieval, the voyage-large-2-instruct model demonstrated the best performance. Using the 15 nearest neighbors provided the best results. Increasing the number beyond this did not improve accuracy due to a lack of meaningful information.ConclusionModern embedding models significantly outperform specialized models like ClinicalBERT in AMC tasks. These findings underscore the potential of these models to enhance medical coding practices, in spite of the challenges with ambiguous diagnosis descriptions.