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

> **NIH NIH R01** · MCLEAN HOSPITAL · 2021 · $346,542

## Abstract

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.

## Key facts

- **NIH application ID:** 10167040
- **Project number:** 1R01MH125740-01
- **Recipient organization:** MCLEAN HOSPITAL
- **Principal Investigator:** JUSTIN T BAKER
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $346,542
- **Award type:** 1
- **Project period:** 2021-04-15 → 2025-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10167040

## Citation

> US National Institutes of Health, RePORTER application 10167040, SCH: INT: Collaborative Research: Context-Adaptive Multimodal Informatics for Psychiatric Discharge Planning (1R01MH125740-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10167040. Licensed CC0.

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