mDOT TR&D2 (Optimization): Dynamic Optimization of Continuously Adapting mHealth Interventions via Prudent, Statistically Efficient, and Coherent Reinforcement Learning

mDOT TR

• 批准号: 10541807
• 负责人: SUSAN A MURPHY
• 金额: $ 19.46万
• 依托单位: UNIVERSITY OF MEMPHIS
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-15 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10541807
• 关键词: Acceleration; Accounting; Address; Behavioral; Biological Markers; Cellular Phone; Chronic Disease; Clinical; Cloud Computing; Collaborations; Communities; Companions; Data; Detection; Development; Distal; Distant; Ensure; Event; Feedback; Future; Health; Health Benefit; Healthcare; Individual; Infrastructure; Intervention; Lead; Learning; Location; Maps; Medical; Medicine; Methodology; Methods; Modeling; Outcome; Patients; Persons; Population; Principal Investigator; Psychological reinforcement; Research; Research Personnel; Resources; Risk; Risk Estimate; Services; Software Framework; Software Tools; Stress; Structure; System; Technology; Testing; Time; Training; Translations; Treatment Efficacy; Universities; Variant; Work; biopsychosocial; cloud based; cohort; data sharing; flexibility; habituation; healthy lifestyle; improved; interest; intervention delivery; intervention effect; learning algorithm; learning strategy; mHealth; member; multiple chronic conditions; next generation; online intervention; open source; personalized decision; personalized intervention; prevent; public health relevance; sensor; sensor technology; social; software systems; synergism; technological innovation; technology research and development; tool; treatment effect; wearable device

Project Lead: Murphy, Susan Principal Investigator: Kumar, Santosh TR&D2: Dynamic Optimization of Continuously Adapting mHealth Interventions via Prudent, Statistically Efficient, and Coherent Reinforcement Learning Lead: Dr. Susan Murphy, Harvard University; 10% effort (1.2CM) Abstract: The mHealth Center for Discovery, Optimization & Translation of Temporally-Precise Interventions (the mDOT Center) will enable a new paradigm of temporally-precise medicine to maintain health and manage the growing burden of chronic diseases. The mDOT Center will develop and disseminate the methods, tools, and infrastructure necessary for researchers to pursue the discovery, optimization and translation of temporally- precise mHealth interventions. Such interventions, when dynamically personalized to the moment-to-moment biopsychosocial-environmental context of each individual, will precipitate a much-needed transformation in healthcare by enabling patients to initiate and sustain the healthy lifestyle choices necessary for directly managing, treating, and in some cases even preventing the development of medical conditions. Organized around three Technology Research & Development (TR&D) projects, mDOT represents a unique national resource that will develop multiple methodological and technological innovations and support their translation into research and practice by the mHealth community in the form of easily deployable wearables, apps for wearables and smartphones, and a companion mHealth cloud system, all open-source. Technology Research and Development project 2 (TR&D2) will address three key limitations of current online reinforcement learning (RL) when applied to personalize mobile interventions to individuals. Two of these limitations are related to the need to increase efficacy and reduce negative delayed intervention burden effects leading to disengagement. The third looks to future needs involving the personalization of multiple intervention components each operating at a different time scale. In particular, we will accommodate the ever-present mobile health challenge of user disengagement by developing a continuum of approaches between RL algorithms that ignore delayed intervention effects and RL algorithms that attempt to capture noisy delayed intervention effects over a more distant future. Second, we will increase the rate at which personalization occurs via optimally leveraging data across time and across users to more quickly personalize the interventions to each user. Third, we will develop the first RL approaches to coherently personalize multiple intervention components holistically. In addition, to enhance impact and dissemination, the methods will be developed in close collaboration with three collaborative projects with an emphasis on model interpretability. We will provide the two service projects and the broader research community with open-source software tools and systems consisting of smartphone and cloud computing components for online personalization. TR&D2 will synergistically work in partnership with the other TR&D projects, the Training and Dissemination Core, and the Administration Core to maximize the societal impact of TR&D2 technologies. 0

项目负责人：Murphy，Susan首席研究员：Kumar，Santosh TR&D2：通过审慎的， 统计有效且一致的强化学习 负责人：Susan Murphy博士，哈佛大学; 10%努力（1.2厘米） 摘要：移动健康中心致力于发现、优化和翻译时间精确的干预措施 (the mDOT中心）将使一个新的模式，时间精确的医学，以保持健康和管理 慢性病日益加重的负担。mDOT中心将开发和传播方法，工具， 和基础设施所需的研究人员追求的发现，优化和翻译的时间- 精准的移动健康干预措施。这种干预，当动态个性化的时刻到时刻， 每个人的生物心理社会环境背景，将促成一个急需的转变， 通过使患者能够启动和维持健康的生活方式的选择， 管理，治疗，在某些情况下甚至防止医疗条件的发展。有组织 围绕三个技术研究与开发（TR&D）项目，mDOT代表了一个独特的国家 资源，将开发多种方法和技术创新，并支持其翻译 移动健康社区以可轻松部署的可穿戴设备、 可穿戴设备和智能手机，以及配套的mHealth云系统，全部开源。 技术研究和开发项目2（TR&D2）将解决当前在线 强化学习（RL），当应用于个性化的移动的干预个人。两个这样 局限性与需要提高疗效和减少延迟干预的负面负担效应有关 导致脱离接触。第三个展望未来的需要，涉及多种干预的个性化 每个组件在不同的时间尺度上操作。特别是，我们将适应始终存在的移动的 通过开发RL算法之间的连续方法， 忽略延迟干预效应和试图捕获噪声延迟干预效应的RL算法 在更遥远的未来。第二，我们将通过最佳方式提高个性化的发生率， 利用跨时间和跨用户的数据，更快地为每个用户个性化干预。第三、 我们将开发第一个强化学习方法，以整体地连贯地个性化多个干预组件。 此外，为了加强影响和传播，将与三个机构密切合作， 强调模型可解释性的合作项目。我们将提供两个服务项目， 更广泛的研究社区与开源软件工具和系统组成的智能手机和 用于在线个性化的云计算组件。TR&D2将协同合作， 其他研发项目，培训和传播核心，以及管理核心，以最大限度地提高社会 TR&D2技术的影响 0