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This paper introduces PAFIR, a reinforcement learning framework that dynamically selects features from longitudinal wearable and clinical data to personalize fall risk assessment for older adults. By treating feature selection as an adaptive policy problem, the method overcomes the limitations of static models in capturing evolving individualized risk patterns across multiple modalities. The approach demonstrates superior performance in identifying relevant temporal dependencies compared to current state-of-the-art baselines within a healthcare trial setting.
Adaptive Multi-Agent Feature Selection for Personalized Fall Risk Prevention
arXiv:2608.18450v1 Announce Type: new Abstract: 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.
Abstract: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.
38 pages, 10 figures, 12 tables. Accepted at Machine Learning for Healthcare (MLHC 2026)
Machine Learning (cs.LG)
arXiv:2608.18450 [cs.LG]
(or arXiv:2608.18450v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.18450 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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