KREL: Automatic Medical Coding via Knowledge-Guided Reasoning over Clinical Evidence with LLMs

2026-08-21

AI Summary

本文提出KREL框架，通过结合外部ICD编码指南和大型语言模型来解决自动医疗编码中的长文本解读、广泛标签空间及复杂编码规则问题。

Abstract

Automatic Medical Coding (AMC), which assigns standardized International Classification of Diseases (ICD) codes to clinical notes, is essential for medical reimbursement, quality reporting, and clinical research. Existing pre-trained language model (PLM)-based methods typically formulate AMC as an extreme multi-label classification problem over a predefined code set, while recent large language model (LLM)-based approaches instead frame it as generation or multi-step reasoning. However, key challenges remain, including the extreme length of clinical notes that hinders effective interpretation, the vast ICD label space, and complex coding rules that are not explicitly captured by LLMs. In this work, we propose Knowledge-Guided Reasoning over Clinical Evidence with LLMs (KREL), a framework that leverages LLMs for clinical text understanding and reasoning while integrating external ICD coding guidelines as structured knowledge. This design enables tight coupling between domain knowledge and LLM reasoning, reducing hallucinations and improving compliance with coding standards. Experiments on benchmark datasets show that KREL consistently outperforms strong PLM-based and state-of-the-art LLM-based baselines.

Problem

• Automatic Medical Coding
• Clinical Notes
• ICD Codes
• Large Language Models
• Coding Rules

Innovation

• Knowledge-Guided Reasoning
• Clinical Evidence
• Large Language Models (LLMs)
• ICD Coding Guidelines