Papers for

health insurance claims processors

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Semantic ids improve automatic medical code prediction accuracy

Beyond ICD Codes: Semantic IDs for Generative Medical Coding

Abstract: Generative approaches to automatic International Classification of Diseases (ICD) coding use large language models (LLMs) to predict ICD codes from clinical documents. However, original ICD code strings are not ideal generation targets because the ICD hierarchy does not always reflect clinical relationships, and LLMs struggle to interpret and generate these codes. We introduce SemICD, a framework that trains LLMs to generate Semantic IDs (SIDs) instead. SemICD constructs SIDs by hierarchically clustering embeddings of textual code profiles, allowing semantically related codes to share prefixes while each complete SID uniquely identifies one ICD code. To align the SID tokens with clinical semantics, we employ two auxiliary tasks for bidirectional generation between textual code profiles and SIDs. Experiments on MIMIC-III and MIMIC-IV with four LLMs show that SemICD outperforms direct code generation, improving Micro-F1 by 1.34-8.00 points. Our code is available at https://github.com/youwillbethebest/SemICD.

Tue 13 OctArtificial Intelligence

The gist

Automatically assigning medical diagnosis codes from patient records is hard because the codes are complicated and don’t always match how doctors think. The authors created a new way to rewrite these codes into simpler meaningful ID strings that reflect their relationships. Using these new semantic IDs helps large language models understand and predict the correct codes better than using the original codes. This approach improves accuracy in coding real hospital data.

Open → 2610.19301v1