Interventions Recommendation System for Preventing future Falls in Older Adults

Falls are prevalent in the elderly population and there is an urgent need for public health strategies to decrease their incidence and identify those who are at risk. Physicians are increasingly confronted by critical and serious situations in patients at risk of falls who require effective interventions. Being aware of the patient's medical record, they need to quickly and efficiently recommend the best therapeutic intervention to reduce the incidence of future falls. In this respect, we propose an Interventions Recommendation System that aids practitioners make decisions about elderly falls by recommending individualized intervention that would reduce the risk of patient's future falls. This paper describes our work in progress on a probabilistic causal model for preventing falls in older adults. We conduct an initial empirical study for such a model on an elderly personal information base and report the initial promising results of our causal model in terms of the usefulness and effectiveness of our approach. (C) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://crativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International.