Accurate and explainable ICD-10-CM diagnosis coding through multi-stage model adaptation and evidence-guided verification
The International Classification of Diseases (ICD) coding system is central to reimbursement, registry construction, and secondary analysis, yet automated inpatient International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis coding from discharge documentation remains difficult because clinical notes are long, diagnoses are multi-label, and supporting evidence is often scattered across multiple note sections. Existing systems also provide limited justification for why a code was assigned. This study develops and evaluates a Large Language Model (LLM)-based approach for accurate and evidence-grounded ICD-10-CM coding. Our framework combines three components: a two-stage Supervised Fine-Tuning (SFT) strategy that first aligns the base model with medical instructions and then specializes it for ICD coding, an ICD-aligned Reinforcement Learning (RL) algorithm with reward and advantage functions tailored to the hierarchical structure of ICD codes, and a Retrieval-Augmented Generation (RAG)-based verifier that removes predictions lacking external support. On the Medical Information Mart for Intensive Care IV (MIMIC-IV) benchmark, two-stage SFT improves Micro-F1 from 14.4% to 64.0%, ICD-specific RL further raises it to 66.7%, and the verifier reaches 67.5%, exceeding the strongest previously reported MIMIC-IV result by 8.8 points. These results show that domain adaptation, task-aligned optimization, and evidence-guided verification can improve both coding accuracy and the auditability of model outputs. The resulting system is intended as a decision-support tool for professional coders rather than a fully autonomous deployment.