Automated mood disorder symptoms monitoring from multivariate time-series sensory data: Getting the full picture beyond a single number

Abstract Mood disorders are among the leading causes of disease burden worldwide. They manifest with changes in mood, sleep, and motor-activity, observable with physiological data. Despite effective treatments being available, limited specialized care availability is a major bottleneck, hindering preemptive interventions. Nearcontinuous and passive collection of physiological data from wearables in daily life, analyzable with machine learning, could mitigate this problem, bringing mood disorders monitoring outside the doctor’s office. Previous works attempted predicting a single label, e.g. disease state or a psychometric scale total score. However, clinical practice suggests that the same label can underlie different symptom profiles, requiring personalized treatment. In this work we address this limitation by proposing a new task: inferring all items from the Hamilton Depression Rating Scale (HDRS) and the Young Mania Rating Scale (YMRS), the most-widely used standardized questionnaires for assessing depression and mania symptoms respectively, the two polarities of mood disorders. Using a naturalistic, single-center cohort of patients with a mood disorder (N=75), we develop an artificial neural network (ANN) that inputs physiological data from a wearable device and scores patients on HDRS and YMRS in moderate agreement (quadratic Cohen’s κ = 0.609) with assessments by a clinician. We also show that, when using as input physiological data recorded further away from when HDRS and YMRS were collected by the clinician, the ANN performance deteriorates, pointing to a distribution shift, likely across both psychometric scales and physiological data. This suggests the task is challenging and research into domain-adaptation should be prioritized towards real-world implementations.