用多知识图谱增强大模型，帮轻度痴呆患者记忆与情绪管理
DEMENTIA-PLAN: An Agent-Based Framework for Multi-Knowledge Graph Retrieval-Augmented Generation in Dementia Care
- 构建多维知识图谱，融合日常作息与个人记忆
- 自反思规划代理动态调整检索权重，优化生成结果
- 适合临床认知支持系统研发者与老年护理研究者
轻度痴呆患者主要表现为严重记忆力减退和情绪不稳。为此，我们提出 DEMENTIA-PLAN，一种基于智能体的检索增强生成框架，利用大语言模型提升对话支持能力。该模型采用多知识图谱架构，整合日常作息图谱与人生记忆图谱等多维度知识表示。通过此多图谱结构，DEMENTIA-PLAN 同时应对即时照护需求并借助个人记忆实现深层情感共鸣，帮助稳定患者情绪并提供可靠记忆支持。其核心创新在于自反思规划智能体，可系统协调跨多个知识图谱的知识检索与语义融合，并对日常作息与人生记忆图谱的检索内容进行评分，动态调整其检索权重以优化响应生成。DEMENTIA-PLAN 在痴呆照护的大语言模型临床应用中具有显著进展，弥合了人工智能工具与照护干预之间的差距。
原文摘要 · Abstract (English)
Mild-stage dementia patients primarily experience two critical symptoms: severe memory loss and emotional instability. To address these challenges, we propose DEMENTIA-PLAN, an innovative retrieval-augmented generation framework that leverages large language models to enhance conversational support. Our model employs a multiple knowledge graph architecture, integrating various dimensional knowledge representations including daily routine graphs and life memory graphs. Through this multi-graph architecture, DEMENTIA-PLAN comprehensively addresses both immediate care needs and facilitates deeper emotional resonance through personal memories, helping stabilize patient mood while providing reliable memory support. Our notable innovation is the self-reflection planning agent, which systematically coordinates knowledge retrieval and semantic integration across multiple knowledge graphs, while scoring retrieved content from daily routine and life memory graphs to dynamically adjust their retrieval weights for optimized response generation. DEMENTIA-PLAN represents a significant advancement in the clinical application of large language models for dementia care, bridging the gap between AI tools and caregivers interventions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。