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

危重期间最需要评估和处理的精神症状和行为是什么 精神卫生保健方面的积分，例如出院前的时间？目前，精神病学 缺乏客观的测试来为这一点和其他临床上具有挑战性的-可能代价高昂的-提供信息 决定。建立有效的精神疾病过程的客观标记物尤其具有挑战性 与其他5个领域生物标志物的发展情况进行了比较。一个关键挑战是缺乏可用的数据 从精神病患者的护理轨迹的关键时期，这是本项目所寻求的 致信地址。第二个主要挑战，也是这个项目的核心特征，是复杂的， 人类行为表达的上下文依赖性，这极大地使建立 反映潜在精神健康疾病过程的稳健、客观的措施。这个项目将 解决这两个障碍，引入了一个新的计算框架，名为上下文自适应多模式 信息学，识别和评估与出院准备相关的行为生物标记物 严重精神疾病的症状。该项目旨在应对5VE基础研究挑战： (1)获取400例住院重度精神病患者的多模式精神病出院计划数据集 疾病；(2)创建自我感知的线性和神经模型，以识别多峰行为生物标记物；(3) 开发上下文敏感的线性和神经模型，以确定行为生物标记物的上下文并量化 情境对行为的影响；(4)构建一个新的适应性评估规划框架，该框架 创建个性化的患者分析，以对下一次评估会议的背景和模式进行排名； (5)评估我们的测量、模型和见解的可信性和概括性。 这项研究将提高对社会背景和行为生物标记物的基本理解，建立 心理健康评估的客观措施，更广泛地说，为重组的 护理提供系统，其中资源被智能分配，以确保评估 与期望的临床目标相关的信息。

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.