Adaptive Multi-Agent Feature Selection for Personalized Fall Risk Prevention

Chang Liu, Ladda Thiamwong, Yanjie Fu, Rui Xie
Proceedings of the 11th Machine Learning for Healthcare Conference, PMLR 340:1140-1177, 2026.

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 \textbf{PAFIR}, a \textbf{P}ersonalized and \textbf{A}daptive \textbf{F}eature selection framework for fall risk \textbf{I}dentification and p\textbf{R}evention, 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v340-liu26a, title = {Adaptive Multi-Agent Feature Selection for Personalized Fall Risk Prevention}, author = {Liu, Chang and Thiamwong, Ladda and Fu, Yanjie and Xie, Rui}, booktitle = {Proceedings of the 11th Machine Learning for Healthcare Conference}, pages = {1140--1177}, year = {2026}, editor = {Krishnan, Rahul G. and van Amsterdam, Wouter A. C. and Chopra, Sumit and Overgaard, Shauna and Hughes, Michael and Ötleş, Erkin and Shen, Yiqiu and Shanmugam, Divya and Nayan, Madhur and Engelhard, Matthew and Fackler, Jim and Oberst, Michael}, volume = {340}, series = {Proceedings of Machine Learning Research}, month = {12--14 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v340/main/assets/liu26a/liu26a.pdf}, url = {https://proceedings.mlr.press/v340/liu26a.html}, 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 \textbf{PAFIR}, a \textbf{P}ersonalized and \textbf{A}daptive \textbf{F}eature selection framework for fall risk \textbf{I}dentification and p\textbf{R}evention, 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.} }
Endnote
%0 Conference Paper %T Adaptive Multi-Agent Feature Selection for Personalized Fall Risk Prevention %A Chang Liu %A Ladda Thiamwong %A Yanjie Fu %A Rui Xie %B Proceedings of the 11th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2026 %E Rahul G. Krishnan %E Wouter A. C. van Amsterdam %E Sumit Chopra %E Shauna Overgaard %E Michael Hughes %E Erkin Ötleş %E Yiqiu Shen %E Divya Shanmugam %E Madhur Nayan %E Matthew Engelhard %E Jim Fackler %E Michael Oberst %F pmlr-v340-liu26a %I PMLR %P 1140--1177 %U https://proceedings.mlr.press/v340/liu26a.html %V 340 %X 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 \textbf{PAFIR}, a \textbf{P}ersonalized and \textbf{A}daptive \textbf{F}eature selection framework for fall risk \textbf{I}dentification and p\textbf{R}evention, 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.
APA
Liu, C., Thiamwong, L., Fu, Y. & Xie, R.. (2026). Adaptive Multi-Agent Feature Selection for Personalized Fall Risk Prevention. Proceedings of the 11th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 340:1140-1177 Available from https://proceedings.mlr.press/v340/liu26a.html.

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