EAGER/Collaborative Research: Sensing, Modeling and Optimization of Postoperative Heart Health Management

术后结果对许多患者的生活质量至关重要。然而，在出院后，目前很少有基于传感器的决策支持系统扩展到家庭、工作场所和社区。术后护理主要依赖于偶发性随访和罕见的心电图。对术后患者临床参数的持续监测、临床状态的评估以及对术后恢复的优化管理的研究很少。这项探索性研究（EAGER）早期概念奖支持基础研究，以开发协作传感、统计建模和决策策略，以优化心脏健康的术后管理。这项研究将帮助临床医生和患者利用快速发展的传感和移动技术，实现智能术后管理的实质性推动。因此，该项目将提供心脏健康生活的教育，并提高智能健康的意识。此外，实现更好的术后护理可以降低医疗成本。通过新的课程模块、培训保健专业人员和招收代表性不足的学生，将对教育产生更广泛的影响。在目前的实践中，特别策略被广泛用于管理术后风险。该奖项将使一种新的基于传感器的、以患者为中心的心脏健康管理成为可能，这种管理可以克服现有实践的几个限制。特别是，它将使临床医生和患者能够(1)定量测量心脏手术前后的生活质量，(2)优化术后心脏护理，减少心律失常复发，(3)改善生活方式的改变，并对一般术后结果产生积极影响。如果成功，该研究将产生新的数据输入算法，以解决以患者为中心的传感中的不确定性，提取基于传感器的心脏风险生物标志物，模拟心脏状况的演变动态，并优化不确定性下的术后管理。该项目的成功将引发一种新的“感知-建模-优化”方法，从理论上制定连接术后患者生理信号的关系，从分析模型中获得有用信息，以及智能术后健康管理。分析方法和工具将普遍适用于处理基于传感器的心血管系统监测和控制中的数据准确性，特征提取，风险预测和过程优化。

Postoperative outcomes are critical to the quality of life of many patients. However, after discharge, there are currently few sensor-based decision support systems extending to home, workplace, and community. Postoperative care primarily depends on episodic follow-up visits and rare electrocardiograms. Very little has been done to continuously monitor clinical parameters of postoperative patients, estimate clinical status, and further help optimal management of postoperative recovery. This EArly-concept Grant for Exploratory Research (EAGER) award supports fundamental research to develop a collaborative sensing, statistical modeling and decision-making strategy for optimizing postoperative management of heart health. This research will help clinicians and patients leverage the fast development of sensing and mobile technology to achieve a substantial boost in smart postoperative management. As a result, this project will provide education on heart-healthy living and raise the awareness of smart health. In addition, realizing a better postoperative care will achieve a reduction in healthcare costs. A broader impact in education will be realized through new curriculum modules, training of healthcare professionals, and recruitment of under-represented students.In current practice, ad hoc strategies are widely used for managing postoperative risks. This award will make possible a new sensor-based, patient-centered management of heart health that can overcome several limitations of existing practices. In particular, it will empower clinicians and patients to (1) quantitatively measure the quality of life before and after cardiac procedures, (2) optimize postoperative cardiac care and decrease arrhythmia recurrences, and (3) improve lifestyle modifications and positively influence general postoperative outcomes. If successful, this research will lead to new data imputation algorithms to tackle uncertainty in patient-centered sensing, extract sensor-based biomarkers of cardiac risks, model the evolving dynamics of cardiac conditions, and optimize postoperative management under uncertainty. The success of this project will invoke a new "sensing-modeling-optimization" approach to theoretically formulate relationships connecting physiological signals from postoperative patients, useful information from analytical models with smart postoperative health management. Analytical methods and tools will be generally applicable to handle data veracity, feature extraction, risk prognostics, and process optimization in sensor-based monitoring and control of cardiovascular systems.