FusionGraphRAG: An Adaptive Retrieval-Augmented Generation Framework for Complex Disease Management in the Elderly

Elderly patients often experience multimorbidity and long-term polypharmacy, making medication safety a critical challenge in disease management. In China, the concurrent use of Western medicines and proprietary Chinese medicines (PCMs) further complicates this issue, as potential drug interactions are often implicit, increasing risks for physiologically vulnerable older adults. Although large language model-based medical question-answering systems have been widely adopted, they remain prone to unsafe outputs in medication-related contexts. Existing retrieval-augmented generation (RAG) frameworks typically rely on static retrieval strategies, limiting their ability to appropriately allocate retrieval and verification efforts across different question types. This paper proposes FusionGraphRAG, an adaptive RAG framework for geriatric disease management. The framework employs query classification-based routing to distinguish questions by complexity and medication relevance; integrates dual-granularity knowledge alignment to connect fine-grained medical entities with higher-level contextual knowledge across diseases, medications, and lifestyle guidance; and incorporates explicit contradiction detection for high-risk medication scenarios. Experiments on the GeriatricHealthQA dataset (derived from the Huatuo corpus) indicate that FusionGraphRAG achieves a Safety Recall of 71.7%. Comparative analysis demonstrates that the framework improves retrieval accuracy and risk interception capabilities compared to existing graph-enhanced baselines, particularly in identifying implicit pharmacological conflicts. The results indicate that the framework supports more reliable geriatric medical question answering while providing enhanced safety verification for medication-related reasoning.