Predicting acute events using the movement patterns of older adults: an unsupervised clustering method

Abstract

Timely identification of individuals with a high risk of imminent acute events in long-term care facilities can aid in reducing the frequency or severity of such events and lead to safer residential environments. Specifically, an interval-based classification of mobility behavior (i.e., the real-time pattern of walking and physical activities in older adults) has been used for early recognition and prevention of acute events such as falls, delirium, and urinary tract infections. It has also been shown that supplementing such temporal mobility behavior data with static cognitive condition information (such as test scores) can yield better prediction results. However, classifying such multi-modal (static+time-series) data is a challenging task as it requires simultaneously taking different similarity relationships into account. In this work, we present an unsupervised clustering technique for classifying this type of multi-modal data points via jointly optimizing separate objective functions associated with the static and time-series parts. We show that our customized deep learning pipeline achieves competitive or superior results compared to several recent clustering baselines when studied on a few generic tasks aiming at clustering time-series data using both static and time-series data. Following this, we show that our clustering model can be used to cluster movement patterns into clinically meaningful clusters that can effectively capture the risk of near future acute events.