面向个性化跌倒风险预防的自适应多智能体特征选择
Adaptive Multi-Agent Feature Selection for Personalized Fall Risk Prevention
- University of Central Florida（中佛罗里达大学）
- Arizona State University（亚利桑那州立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对老年人跌倒风险识别的静态方法无法适配动态个性化风险因素，本文提出PAFIR框架，将自适应特征选择建模为强化学习问题，在PEER试验数据上验证其能更有效捕捉特征模式，实现动态个性化跌倒风险预防。
AI中文摘要：
老年人跌倒构成重大公共卫生挑战，其驱动因素是多个风险领域间复杂且随时间变化的相互作用。有效的跌倒风险因素识别需从异质性纵向数据中学习，同时考虑稀疏且延迟的跌倒相关结局事件。然而，现有方法大多是静态的，无法跨模态和时间自适应地建模不断演变的个性化风险因素。我们提出PAFIR——一种用于跌倒风险识别与预防的个性化自适应特征选择框架，该框架将自适应特征选择表述为针对纵向多模态健康数据的强化学习问题。PAFIR联合建模相关评估变量间的结构依赖关系，以及可穿戴设备衍生的身体活动数据中的时间动态，并利用稀疏跌倒发生率结局产生的奖励信号，在重复研究访视中学习自适应选择策略。我们将PAFIR应用于Physio反馈锻炼计划（PEER）整群随机试验的数据。实验结果表明，与最先进的基线方法相比，PAFIR能更有效地捕捉特征相关性的纵向和结构模式，并支持动态的、受试者特异性的特征选择。通过随时间调整所选特征，PAFIR可支持更及时且个性化的跌倒预防策略。
英文摘要：
Falls among older adults represent a major public health challenge driven by complex, time-varying interactions across multiple risk domains. Effective fall risk factor identification requires learning from heterogeneous longitudinal data while accounting for sparse and delayed fall-related outcome events. However, existing approaches are largely static and fail to adaptively model evolving, individualized risk factors across modalities and time. We propose PAFIR, a Personalized and Adaptive Feature selection framework for fall risk Identification and pRevention, which formulates adaptive feature selection as a reinforcement learning problem over longitudinal multimodal health data. PAFIR jointly models structural dependencies among correlated assessment variables and temporal dynamics in wearable-derived physical activity data, and learns adaptive selection policies across repeated study visits using reward signals derived from sparse fall incidence outcomes. We apply PAFIR to data from the Physio fEedback Exercise pRogram (PEER) cluster-randomized trial. Experimental results demonstrate that PAFIR more effectively captures longitudinal and structural patterns of feature relevance than state-of-the-art baselines, and enables dynamic, subject-specific feature selection. By adapting selected features over time, PAFIR supports more timely and personalized fall prevention strategies.
