AI for Predicting Sleep and Mental Health Problems

Joshua J Gooley, Neuroscience and Behavioural Disorders
Enrico Petretto, Centre for Computational Biology (CCB) 

Healthy sleep is important for students’ learning performance and wellbeing. Sleep problems often emerge during adolescence and early adulthood and may contribute to the onset of depression. Hence, early treatment of sleep problems may help to decrease the incidence of depressive disorders. In this project, we seek to identify early markers of poor sleep health that can be used to identify individuals at risk of depression. We will use data from surveys, wearables, and digital traces to predict students’ wellbeing and academic performance. We envisage that AI-guided classification of sleep and diurnal activity phenotypes can be used to identify at-risk students in whom early intervention may prevent mental health problems and academic difficulties.