Collaborative Research: Automated Knowledge Discovery in Reliability and Healthcare from Complex Data with Covariates

收集和分析带有温度、湿度和辐射水平等协变量的数据是科学和工程中的日常活动。一个重要的例子是锂离子电池等产品设计中使用的加速寿命测试数据。这样的数据是通过将测试单元暴露在比正常情况更恶劣的条件下来收集的，以加快故障过程。由此产生的失效时间由概率分布和寿命-应力关系来建模。然而，如果所选的概率分布和/或寿命-应力关系不能充分描述潜在的失效过程，则由此产生的可靠性预测可能具有误导性。在医疗系统中也遇到了类似的例子，其中至关重要的是量化重要指标的概率分布，如住院时间、等待时间和疾病进展，以及影响这些指标的协变量的影响。该奖项支持从可靠性和医疗保健领域的复杂数据中自动发现知识的基础研究，这些数据可能会对制造、医疗保健、能源、交通和航空航天行业产生影响。研究小组将努力扩大代表不足的群体和少数群体的参与，并积极影响工程教育。这个项目的目标是研究一种新的方法，用于使用矩阵分析模型从带有协变量的复杂数据中自动发现知识。将开发统计工具和优化算法，以便有效地收集此类数据或从海量数据中选择有用的子集，以便快速实施。研究结果将有助于在数据生成机制未知或难以使用现有统计工具进行分析的情况下，为建模和解释此类数据开辟一条新的途径。为此，将通过数学优化探索一种自动建模方法，以构建包含协变量的一般相型分布。为了提高数据收集的统计效率，将研究最优实验设计方法，并将研究可行的计算工具，以计划阶段类型模型的加速测试实验。此外，还将开发一种基于最佳试验设计方法的数据选择方法，以最大限度地利用医疗保健数据。研究结果将通过在实验室进行锂离子电池的加速测试以及与生物医学信息学服务机构就目标医疗应用进行合作来验证。

Collecting and analyzing data with covariates such as temperature, humidity, and radiation level are everyday activities in science and engineering. An important example is accelerated life testing data used in the design of products such as lithium-ion batteries. Such data are collected by exposing test units to harsher-than-normal conditions to expedite the failure process. The resulting failure times are modeled by a probability distribution and a life-stress relationship. However, if the probability distribution and/or the life-stress relationship selected cannot adequately describe the underlying failure process, the resulting reliability prediction may be misleading. A similar example is also encountered in healthcare systems, where it is crucial to quantify probability distributions of important measures such as the length-of-stay, waiting time, and disease progression, and the effects of influential covariates on these measures. This award supports fundamental research on automated knowledge discovery from complex data in reliability and healthcare with potential impacts in the areas of manufacturing, healthcare, energy, transportation, and aerospace industries. The research team will strive to broaden participation of underrepresented groups and minorities, and positively impact engineering education. The objective of this project is to investigate a new methodology for automated knowledge discovery from complex data with covariates using matrix-analytic models. Statistical tools and optimization algorithms will be developed for efficiently collecting such data or selecting the useful subsets from massive data for quick implementation. The research findings will help create a new avenue for modeling and interpreting such data in situations in which the data-generating mechanisms are unknown or difficult to analyze using existing statistical tools. To this end, an automated modeling methodology to construct general phase-type distributions incorporating covariates will be explored via mathematical optimization. To improve the statistical efficiency of data collection, an optimal experimental design methodology will be investigated, and viable computational tools for planning accelerated testing experiment with phase-type models will be studied. In addition, a data-selection approach based on the optimal experimental design methodology will be developed to maximize the utilization of healthcare data. The research findings will be validated by conducting accelerated tests of lithium-ion battery in the laboratory and collaborating with biomedical informatics services on targeted healthcare applications.