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

哪些精神病症状和行为是最重要的，以评估和管理在关键 精神病保健的要点，如出院前的时间？目前，精神病学 缺乏客观的测试，可以告知这一点和其他临床挑战-和潜在的昂贵- 决策建立有效的精神疾病过程的客观标志物尤其具有挑战性 与其他5ELD中生物标志物的发展相比。一个关键挑战是缺乏可用数据 从精神病患者在其护理轨迹的关键时期，本项目寻求 来解决。第二个主要挑战，也是本项目的核心特征，是复杂的， 人类行为表达的上下文依赖性，这大大复杂化了建立 反映潜在心理健康疾病过程的可靠、客观的措施。该项目将 解决这两个障碍，引入一个新的计算框架，命名为上下文自适应多模态 信息学，识别和评估与出院准备相关的行为生物标志物， 严重精神疾病的症状。该项目旨在解决5VE基础研究挑战： (1)获取400例严重精神病住院患者的多模式精神病出院计划数据集 （2）创建自我意识线性和神经模型，以识别多模式行为生物标志物;（3） 开发情境敏感的线性和神经模型，以情境化行为生物标志物并量化 （4）建立一个新的适应性评价规划框架， 创建个性化的患者分析，以对下一个评估会话的上下文和模态进行排名; (5)评估我们的测量、模型和见解的可信度和可推广性。 这项研究将提高对社会背景和行为生物标志物的基本理解， 心理健康评估的客观措施，更广泛地说，为重建 一个医疗服务系统，其中资源被智能地分配，以确保评估 关于期望的临床目的的信息。

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.