AF: Small: Collaborative Research: Personalized Environmental Monitoring of Type 1 Diabetes (T1D): A Dynamic System Perspective

AF：小型：合作研究：1 型糖尿病 (T1D) 的个性化环境监测：动态系统视角

• 批准号: 1718513
• 负责人: Xiaoning Qian
• 金额: $ 18.37万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2021-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718513&HistoricalAwards=false
• 关键词: AF; Small; Collaborative; Research; Personalized; AF; Small; Collaborative; Research; Personalized

The progression of many chronic diseases, such as Type 1 diabetes (T1D), manifests dynamic processes that can be modified by environmental exposures. Modeling of the underlying disease progression holds critical values for better understanding of disease development, effective monitoring, and prevention. While the emerging big data studying these diseases provide great resources, current pace for translating these data into effective monitoring and intervention strategies has been slow due to the analytic challenges caused by potential multi-layer characteristics of disease progression processes, the high-dimensional exogenous factors, heterogeneous biomarker signals, and the complexity of continuous-time stochastic processes. To mitigate these challenges with the development of new models and computational algorithms, this research will provide the desired personalized monitoring and risk factor identification capability, which is crucial not only for increasing the situational awareness of the individuals who are at risk, but also for providing evidences for design, validation, and deployment of intervention strategies. Its generic nature will also help effective monitoring of many other dynamic systems in engineering and life sciences. The interdisciplinary nature of this research across data-driven risk monitoring, dynamic systems, high-dimensional variable selection, and healthcare, will prepare students with a diversified education background. The objective of this project is to create a generic suite of computational approaches that can be applied for modeling, learning, and monitoring a set of dynamic diseases, whose progression processes may be modified by exogenous factors such as environmental exposures. Several methodological contributions are expected, including: (1) a novel rule-based monitoring methodology to convert high-dimensional complex biomarkers into disease risk evaluation, via the development of an efficient screening method for high-throughput rule discovery and an optimal design method for risk monitoring; (2) a multi-layer dynamic model that can investigate how the exogenous risk factors regulate the disease process, with integration of sparse multi-task learning to mitigate the high-dimensionality of exogenous factors; and (3) a high-dimensional robust risk factor identification framework that can identify exogenous factors with integration of knowledge learned from historical data, new measurements, and clinician's prognostics. These proposed methods will be evaluated with a practical example studying T1D in partnership with The Environmental Determinant of Diabetes in the Young (TEDDY) study.

许多慢性疾病（例如1型糖尿病（T1D））的进展表现出可以通过环境暴露来改变的动态过程。基础疾病进程的建模具有关键价值，以更好地理解疾病发展，有效的监测和预防。尽管新兴的大数据研究这些疾病提供了丰富的资源，但由于疾病进展过程的潜在多层特征，高维外源性因素，异质生物标志物信号，异质生物标记信号以及持续时间的持续时间的复杂性，将这些数据转化为有效的监测和干预策略的最新速度一直很慢。为了通过开发新模型和计算算法来缓解这些挑战，这项研究将提供所需的个性化监控和风险因素识别能力，这不仅对于提高处于风险的个人的情境认识至关重要，而且对于为设计，验证和设计策略的部署提供了证据。它的通用性质还将有助于对工程和生命科学中许多其他动态系统有效监测。这项研究的跨学科性质跨数据驱动的风险监测，动态系统，高维变量选择和医疗保健，将为学生提供多种教育背景的准备。该项目的目的是创建一套通用的计算方法套件，可用于建模，学习和监测一组动态疾病，其进展过程可以通过外源性因素（例如环境暴露）来修改。预计有几种方法上的贡献，包括：（1）通过开发有效的高通量规则发现的有效筛选方法，将高维复杂生物标志物转换为疾病风险评估的新型监测方法，以及用于风险监测的最佳设计方法； （2）一个多层动态模型，可以通过稀疏多任务学习的整合来调查外源危险因素如何调节疾病过程，从而减轻外源性因素的高维度； （3）一个高维的鲁棒风险因素识别框架，可以通过从历史数据，新测量和临床医生的预后中学到的知识来识别外源性因素。这些提出的方法将通过与年轻（泰迪）研究中糖尿病的环境决定因素合作研究T1D的实际例子进行评估。