Collaborative Research: Data-Driven Smart Monitoring of Alzheimer's Disease via Data Fusion, Personalized Prognostics, and Selective Sensing

合作研究：通过数据融合、个性化预后和选择性传感对阿尔茨海默病进行数据驱动的智能监测

• 批准号: 1505260
• 负责人: Shuai Huang
• 金额: $ 22.43万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1505260&HistoricalAwards=false
• 关键词: Collaborative; Research; Data; Driven; Smart

The objective of this project is to develop a data-driven smart monitoring methodology of Alzheimer?s disease (AD). AD follows an accelerated degradation trajectory as compared to normal aging. Accurate monitoring and prognosis of the disease trajectory is critical for the success of many preventative interventions. Currently, no first-line screening system for monitoring the fast-growing preclinical population is available. While emerging personalized health screening systems provide the infrastructure to routinely screen massive numbers of individuals, it is an essential challenge to transform the role of these systems from passive information collection into smart monitoring to proactively characterize the underlying complex time-varying disease trajectory shaped by an individual?s risk factors. This project aims at developing such a ?smart monitoring? approach that will equip nowadays cyber infrastructure with powerful data-driven decision-making capabilities for better management of the preclinical individuals, leading to more efficient targeted screening and affordable care, better treatment planning and management, and improved quality of life for both patients and caregivers. Successful implementation will provide a substantial boost for the detection of the 4.5 million preclinical individuals anticipated in the next 20 years. Its generic nature will also impact monitoring of other progressive medical conditions, given the rapid adoption of personalized screening systems in other areas. The interdisciplinary nature of this research across data-driven monitoring, prognostics, optimization, and health care will prepare students a diversified education background. Broader impacts will be also generated through new curriculum modules, online software toolkits for implementation, and involving underrepresented undergraduate and graduate students in research experience programs.The success of the project will significantly advance the state of the art in data-driven monitoring, prognostics, and selective sensing, and contribute to the science base of the emerging personalized screening systems. Specifically, to model and quantify the disease trajectory, a health index (HI) model will be constructed by synthesizing the degradation information from multiple biomarkers via the development of non-parametric and semi-parametric data fusion schemes. Then, to predict the personalized disease trajectory, personalized prognostics methodologies will be developed that can offline predict and online update the personalized HI model via the development of multi-level degradation models and Bayesian updating approaches. Capitalizing on the personalized prognostics methodologies, selective sensing methodologies will be developed to adaptively identify the screening tests that are most informative for the statistical estimation of the HI via a seamlessly integration of a novel Bayesian network model with robust optimization techniques. A team of five PIs with diverse but complementary research backgrounds will be working closely with two leading AD research institutes in the U.S. to develop, test, and validate the methodologies.

该项目的目标是开发一个数据驱动的智能监测方法的阿尔茨海默氏症？的疾病（AD）。与正常老化相比，AD遵循加速降解轨迹。对疾病轨迹的准确监测和预后对于许多预防性干预措施的成功至关重要。目前，没有用于监测快速增长的临床前人群的一线筛查系统。虽然新兴的个性化健康筛查系统提供了常规筛查大量个体的基础设施，但将这些系统的作用从被动信息收集转变为智能监测，以主动表征个体形成的潜在复杂时变疾病轨迹，这是一个重要的挑战。的危险因素。该项目旨在开发这样一个？智能监控？该方法将为当今的网络基础设施提供强大的数据驱动决策能力，以更好地管理临床前个体，从而实现更有效的靶向筛查和负担得起的护理，更好的治疗规划和管理，并改善患者和护理人员的生活质量。成功的实施将为未来20年预计的450万临床前个体的检测提供实质性的推动。鉴于个性化筛查系统在其他领域的迅速采用，其通用性也将影响对其他进展性医疗状况的监测。这项研究的跨学科性质，包括数据驱动的监测，自动化，优化和医疗保健，将为学生提供多元化的教育背景。更广泛的影响也将通过新的课程模块，在线软件工具包的实施，并在研究经验programmes.The项目的成功将显着推进数据驱动的监测，自动化和选择性传感的最新技术水平，并有助于新兴的个性化筛选系统的科学基础。具体而言，为了对疾病轨迹进行建模和量化，将通过开发非参数和半参数数据融合方案，综合来自多个生物标志物的退化信息，构建健康指数（HI）模型。然后，为了预测个性化的疾病轨迹，将开发个性化的预测方法，该方法可以通过开发多级退化模型和贝叶斯更新方法来离线预测和在线更新个性化的HI模型。利用个性化的概率统计学方法，将开发选择性感知方法，通过将新型贝叶斯网络模型与稳健的优化技术无缝集成，自适应地识别对HI的统计估计信息量最大的筛选试验。一个由五名具有不同但互补研究背景的PI组成的团队将与美国两个领先的AD研究机构密切合作，开发，测试和验证方法。