  With the growing popularity of wearable devices, the ability to utilize
physiological data collected from these devices to predict the wearer's mental
state such as mood and stress suggests great clinical applications, yet such a
task is extremely challenging. In this paper, we present a general platform for
personalized predictive modeling of behavioural states like students' level of
stress. Through the use of Auto-encoders and Multitask learning we extend the
prediction of stress to both sequences of passive sensor data and high-level
covariates. Our model outperforms the state-of-the-art in the prediction of
stress level from mobile sensor data, obtaining a 45.6 % improvement in F1
score on the StudentLife dataset.
