Relapse Prediction from Long-Term Wearable Data Using Self-Supervised Learning and Survival Analysis

The introduction of biometric signal analysis in psychiatry could potentially reshape the field by making it more accurate, proactive and personalized. Such biosignals usually acquired from wearables encompass the quantification of human behavior and traits. In this study, we use long-term data acquired from commercial smartwatches, including kinetic and physiological signals, to extract information-thick descriptors that are used for the prediction of subsequent relapses in patients in the psychotic spectrum. Specifically, we propose a novel combination of methods based on Self-Supervised Learning and Survival Analysis that operates on unlabeled and censored data. When combined with other static features that describe the past course of the patient’s health, the proposed methodology yields promising predictive results in terms of two standard survival analysis metrics.