A Machine Learning Algorithm to Predict Health Clinical Situations (Fall, Undernutrition Risk, Depression Risk, Heart Failure Risk) and Improv Decision-support Tools in Primary Healthcare for Older Adults Living at Home.

Introduction: We developed a machine learning algorithm to predict the risk of emergency hospitalization within the new 7 to 14 days with a good predictive performance (AUC=0.85). Data recorded by home aides were send in real time to a secure server to be analyzed by our machine learning algorithm, which predicted risk level and displayed it on a secure web-based medical device. This study aims to implement and to evaluate the sensitivity and specificity's predictions of Presage system for four clinical situations with a high impact on unscheduled hospitalization of older adults living at home: falls, risk of depression (is sadder), risk of undernutrition (eat less well) and risk of heart failure (swollen leg). Methods This is a retrospective observational multicenter study. To gain insight on both short-and middle-term predictions and how the risk factors evolve through different periods of observation, we developed a series of models which predict the risk of future clinical symptoms.