用超图结构整合脑电数据，提升临床诊断准确性
EEG-MedRAG: Enhancing EEG-based Clinical Decision-Making via Hierarchical Hypergraph Retrieval-Augmented Generation
- 构建三层超图框架，融合知识、病历与数据集
- 在7类疾病5种角色上实现更高诊断准确率
- 首个跨病种跨角色的脑电临床问答基准
随着脑电图（EEG）在神经科学和临床实践中的广泛应用，高效检索并语义解析大规模、多源、异构的EEG数据已成为迫切挑战。本文提出EEG-MedRAG，一种基于三层超图的检索增强生成框架，将脑电领域知识、个体患者病例与大规模数据资源统一为可遍历的n元关系超图，支持联合语义-时间检索与因果链诊断生成。同时，我们构建了首个跨疾病、跨角色的脑电临床问答基准，涵盖七种疾病及五种真实临床视角，可用于系统评估疾病无关泛化能力与角色感知上下文理解。实验表明，EEG-MedRAG在答案准确率和检索性能上显著优于TimeRAG与HyperGraphRAG，展现出在真实临床决策支持中的强潜力。数据与代码已公开于https://github.com/yi9206413-boop/EEG-MedRAG。
原文摘要 · Abstract (English)
With the widespread application of electroencephalography (EEG) in neuroscience and clinical practice, efficiently retrieving and semantically interpreting large-scale, multi-source, heterogeneous EEG data has become a pressing challenge. We propose EEG-MedRAG, a three-layer hypergraph-based retrieval-augmented generation framework that unifies EEG domain knowledge, individual patient cases, and a large-scale repository into a traversable n-ary relational hypergraph, enabling joint semantic-temporal retrieval and causal-chain diagnostic generation. Concurrently, we introduce the first cross-disease, cross-role EEG clinical QA benchmark, spanning seven disorders and five authentic clinical perspectives. This benchmark allows systematic evaluation of disease-agnostic generalization and role-aware contextual understanding. Experiments show that EEG-MedRAG significantly outperforms TimeRAG and HyperGraphRAG in answer accuracy and retrieval, highlighting its strong potential for real-world clinical decision support. Our data and code are publicly available at https://github.com/yi9206413-boop/EEG-MedRAG.
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