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

许多慢性疾病的进展，如1型糖尿病（T1 D），表现出可以通过环境暴露来改变的动态过程。基础疾病进展的建模对于更好地理解疾病发展、有效监测和预防具有重要价值。虽然研究这些疾病的新兴大数据提供了大量资源，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以及连续时间随机过程的复杂性所造成的分析挑战，目前将这些数据转化为有效监测和干预策略的步伐一直很慢。为了缓解这些挑战，开发新的模型和计算算法，本研究将提供所需的个性化监测和风险因素识别能力，这不仅是至关重要的，以提高个人的处境意识的风险，但也为设计，验证和部署干预策略提供证据。它的通用性也将有助于有效地监测工程和生命科学中的许多其他动态系统。这项研究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的跨学科性质，将为学生提供多元化的教育背景。该项目的目标是创建一套通用的计算方法，可用于建模，学习和监测一组动态疾病，其进展过程可能会被外部因素，如环境暴露修改。本研究的主要贡献包括：（1）通过开发高效的高通量规则发现筛选方法和风险监测优化设计方法，提出一种新的基于规则的监测方法，将高维复杂生物标志物转化为疾病风险评估;（2）一个多层动态模型，可以研究外源性危险因素如何调节疾病过程，结合稀疏多任务学习，减轻外生因素的高维性;以及（3）高维鲁棒风险因素识别框架，其可以通过整合从历史数据，新测量，和临床医生的诊断这些建议的方法将与一个实际的例子，研究T1 D的合作伙伴关系，在年轻的糖尿病的环境决定因素（TEDDY）研究进行评估。

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.