CAREER: Embracing Complexity: A Fractal Calculus Approach to the Modeling and Optimization of Medical Cyber-Physical Systems

这项跨学科研究提出了一种针对患者的成本节约方法，用于设计和优化医疗保健网络物理系统（HCPS）。HCPS基于传感器计算患者的生理状态，经由网络将该信息从家庭传送到医院以用于量化风险指数，以真实的时间发出对关键医疗干预的需要的信号，并且控制生命健康信号（例如，心律、血糖）。在HCPS范式下提出的研究将把人体视为一个复杂的系统。这将需要开发数学模型，以捕获生理过程的时间依赖性和分形行为，并设计医疗器械的生活质量（QoL）控制策略。这项研究将促进对生理过程、药物治疗、压力水平和生活方式之间相关性的理解。迄今为止，在医疗器械和人工器官的设计中尚未考虑到生理过程的复杂的相互依赖性、可变性和个体特征。现有的数学方法依赖于还原论和马尔可夫假设。该研究项目将通过捕捉高度动态网络中生理过程的相互依赖性和分形特征，重新思考医疗保健网络物理系统设计的理论基础。为了建立HCPS的理论基础，将遵循三步方法：（i）构建用于患者建模的多尺度非平衡统计物理启发框架，其捕获生理过程的时间依赖性、非高斯行为、相互依赖性和多重分形行为;（ii）开发自适应患者特异性和生理感知的（多重分形）闭环控制算法的动态复杂网络;（iii）设计算法和方法的HCPS网络组件，占生物和技术的限制。这项研究将大大有助于早期慢性疾病的检测和治疗。模型和可实现的算法，既可以预测生理动力学和评估急性和慢性疾病的风险，将是以患者为中心的医疗保健的宝贵工具。通过对生理复杂性的深入数学分析，为CPS设计和医疗应用提供了一种变革性的多模态和多尺度方法。该项目不仅解决了CPS当前的科学和技术差距，还可以促进相关领域的新研究方向，例如相互依赖网络的研究，对理解稳态和疾病以及复杂系统的研究和控制具有重要意义。根据这一新提出的范例设计的网络物理系统将具有重要的社会和经济影响，包括改善生活质量和降低因慢性疾病造成的生产率损失。该项目将为研究生、本科生和K-12学生提供跨学科培训。主要研究者将把研究成果整合到他在南加州大学的课程中，并通过项目网站广泛提供。此外，PI将通过让大学和K-12学生参与社区外展活动，提高对健康监测重要作用的认识，加强公民参与。

This cross-disciplinary research proposes a patient-specific cost-saving approach to the design and optimization of healthcare cyber-physical systems (HCPS). The HCPS computes the patient's physiological state based on sensors, communicates this information via a network from home to hospital for quantifying risk indices, signals the need for critical medical intervention in real time, and controls vital health signals (e.g., cardiac rhythm, blood glucose). The research proposed under the HCPS paradigm will treat the human body as a complex system. It will entail the development of mathematical models that capture the time-dependence and fractal behavior of physiological processes and the design of quality-of-life (QoL) control strategies for medical devices. The research will advance the understanding of the correlations between physiological processes, drug treatment, stress level and lifestyle. To date, the complex interdependence, variability and individual characteristics of physiological processes have not been taken into account in the design of medical devices and artificial organs. The existing mathematical approaches rely on reductionist and Markovian assumptions. This research project will rethink the theoretical foundations for the design of healthcare cyber-physical systems by capturing the interdependencies and fractal characteristics of physiological processes within a highly dynamic network. To establish the theoretical foundations of HCPS, a three-step approach will be followed: (i) construct a multi-scale non-equilibrium statistical physics inspired framework for patient modeling that captures the time dependence, non-Gaussian behavior, interdependencies and multi-fractal behavior of physiological processes; (ii) develop adaptive patient-specific and physiology-aware (multi-fractal) close-loop control algorithms for dynamic complex networks; (iii) design algorithms and methodologies for the HCPS networked components that account for biological and technological constraints. This research will significantly contribute to early chronic disease detection and treatment. Models and implementable algorithms, which can both predict physiological dynamics and assess the risk of acute and chronic diseases, will be valuable instruments for patient-centered healthcare. This in-depth mathematical analysis of physiological complexity facilitates a transformative multimodal and multi-scale approach to CPS design with healthcare applications.The project not only addresses the current scientific and technological gap in CPS, but can also foster new research directions in related fields such as the study of interdependent networks with implications for understanding homeostasis and diseases and the study and control of complex systems. The cyber-physical systems designed under this newly proposed paradigm will have vital social and economic implications, including the improvement of QoL and the reduction of lost productivity rates due to chronic diseases. The project will offer interdisciplinary training for graduate, undergraduate and K-12 students. The PI will integrate the research results within his courses at University of Southern California and make them widely available through the project website. Moreover, the PI will enhance civic engagement by involving college and K-12 students in community outreach activities that will raise awareness of the important role of health monitoring.