Improving Rare and Common ICD Coding via a Multi-Agent LLM-Based Approach

Le résumé fourni par la source

Large Language Models (LLMs) have shown strong performance in tasks such as zero- and few-shot information extraction from clinical text without domain-specific training. However, in the ICD coding task, LLMs often hallucinate key details and produce high-recall but low-precision outputs due to the high-dimensional and imbalanced nature of ICD code distributions. Existing LLM-based approaches typically fail to capture the complex, dynamic interactions among human agents involved in real-world coding workflows-such as patients, physicians, and coders-and often lack interpretability and reliability. To address these challenges, we propose a novel multi-agent framework for ICD coding that simulates the real-world process using five role-specific LLM agents-patient, physician, coder, reviewer, and adjuster-and integrates the Subjective, Objective, Assessment, and Plan (SOAP) structure from Electronic Health Records to enhance performance. Evaluated on the MIMIC-III dataset, our method significantly outperforms zero-shot Chain-of-Thought prompting, self-consistency strategies, and LLM-designed agent baselines, particularly for rare codes. Ablation studies confirm the contribution of each agent role, and the system achieves competitive performance with state-of-the-art fine-tuned models, while offering better explainability and requiring no task-specific pre-training.

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