Big Data to Classify, Characterize, and Treat Depression

This research harnesses the power of large-scale datasets and advanced computational methods to identify patterns and subtypes within the heterogeneous condition we call depression. We integrate diverse data sources including electronic health records, genetic databases, brain imaging repositories, smartphone sensor data, social media activity, and longitudinal clinical trials to uncover depression subtypes with distinct biological signatures, symptom profiles, and treatment responses. Using machine learning, artificial intelligence, and network analysis approaches, we develop predictive algorithms for depression risk, treatment outcomes, and suicide risk. Our work also involves creating digital phenotypes that capture the multidimensional nature of depression beyond traditional diagnostic categories. This big data approach enables precision psychiatry by revealing hidden patterns that would be impossible to detect in small-scale studies, ultimately leading to more accurate diagnosis, prognosis, and treatment selection. We are particularly interested in developing AI-based solutions to enable “Just-in-Time” intervention, that is, the deployment of person-specific, context-dependent interventions when they are most needed.