Learning Latent Mental States from Ecological Momentary Data with Generative Models

Music City Center 

One of the goals of precision psychiatry is to characterize mental disorders in an individualized manner,
taking into account the underlying dynamic processes. Recent advances in mobile technologies have enabled the collection
of Ecological Momentary Assessments (EMAs) that capture multiple responses in real-time at high frequency.
However, EMA data are often multi-dimensional, correlated, and hierarchical. Mixed-effect models are commonly
used but may require restrictive assumptions about the fixed and random effects and the correlation structure. We demonstrate the use of temporal generative models which can handle these challenges present in EMA data to learn latent mental states.

generative models

ecological momentary assessments

mental health

mHealth

high-dimensional data

longitudinal data