SCH: INT: Collaborative Research: Context-Adaptive Multimodal Informatics for Psychiatric Discharge Planning

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Which psychiatric symptoms and behaviors are the most important to assess and manage during critical points in psychiatric healthcare, such as the time leading up to hospital discharge? At present, psychiatry lacks objective tests that could inform this and other clinically challenging–and potentially costly– decisions. Establishing valid objective markers of psychiatric disease processes is especially challenging compared with the development of biomarkers in other 5elds. One key challenge is lack of available data from psychiatrically ill patients during key periods in their care trajectory, which the present project seeks to address. A second major challenge, also addressed as a core feature in this project, is the complex, context-dependence of human behavioral expression, which greatly complicates efforts to establish robust, objective measures that re6ect underlying mental health disease processes. This project will address both barriers, introducing a new computational framework, named Context-Adaptive Multimodal Informatics, to identify and evaluate behavioral biomarkers related to discharge-readiness and symptoms in severe mental illness. The project aims to address 5ve fundamental research challenges: (1) Acquire a multimodal psychiatric discharge-planning dataset of 400 inpatients with severe mental illness; (2) Create self-aware linear and neural models to identify multimodal behavioral biomarkers; (3) Develop context-sensitive linear and neural models to contextualize behavioral biomarkers and quantify the in6uence of context on behavior; (4) Build a new adaptive assessment planning framework which creates a personalized patient analysis to rank contexts and modalities for the next assessment session; (5) Assess the trustworthiness and generalizability of our measurements, models, and insights. This research will improve basic understanding of social context and behavioral biomarkers, build objective measures for mental health assessment, and more broadly, pave the way for a restructured care-delivery system in which resources are allocated intelligently to ensure assessments are informative with respect to desired clinical objectives.