根据输入信息完整度自适应调整检索，提升疾病诊断准确率
ICA-RAG: Information Completeness Guided Adaptive Retrieval-Augmented Generation for Disease Diagnosis
- 通过信息完备性评估决定是否检索，避免无效调用
- 在三个中文病历数据集上显著优于基线方法
- 适合临床诊断场景，减少噪声干扰
检索增强的大语言模型（LLM）在医疗领域，如临床诊断中表现出色。然而，现有RAG方法难以根据诊断难度和输入样本的信息量动态调整检索策略，导致频繁且不必要的检索，降低计算效率并增加引入噪声的风险，影响诊断准确性。为此，我们提出ICA-RAG（信息完备性引导的自适应检索增强生成），一种提升疾病诊断中RAG可靠性的新框架。ICA-RAG利用自适应控制模块，基于输入信息的完备性判断检索必要性，并通过优化检索过程与知识过滤机制，使检索更贴合临床需求。在三个中文电子病历数据集上的实验表明，ICA-RAG显著优于基线方法，验证了其在临床诊断中的有效性。
原文摘要 · Abstract (English)
Retrieval-Augmented Large Language Models (LLMs), which integrate external knowledge, have shown remarkable performance in medical domains, including clinical diagnosis. However, existing RAG methods often struggle to tailor retrieval strategies to diagnostic difficulty and input sample informativeness. This limitation leads to excessive and often unnecessary retrieval, impairing computational efficiency and increasing the risk of introducing noise that can degrade diagnostic accuracy. To address this, we propose ICA-RAG (\textbf{I}nformation \textbf{C}ompleteness Guided \textbf{A}daptive \textbf{R}etrieval-\textbf{A}ugmented \textbf{G}eneration), a novel framework for enhancing RAG reliability in disease diagnosis. ICA-RAG utilizes an adaptive control module to assess the necessity of retrieval based on the input's information completeness. By optimizing retrieval and incorporating knowledge filtering, ICA-RAG better aligns retrieval operations with clinical requirements. Experiments on three Chinese electronic medical record datasets demonstrate that ICA-RAG significantly outperforms baseline methods, highlighting its effectiveness in clinical diagnosis.
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