KREL: Automatic Medical Coding via Knowledge-Guided Reasoning over Clinical Evidence with LLMs
Year: 2026
Type: conference-paper
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 pretrained language model (PLM)-based methods typically formulate AMC as an extreme multilabel 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 selected structured coding relations as external knowledge.This design enables tight coupling between domain knowledge and LLM reasoning, reducing hallucinations and supporting rule-aware verification.Experiments on benchmark datasets show that KREL consistently outperforms strong PLM-based and state-of-theart LLM-based baselines.Our code is available here.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7204105230