Collaborative Research: CPS: Medium: Automating Complex Therapeutic Loops with Conflicts in Medical Cyber-Physical Systems

合作研究：CPS：中：自动化医疗网络物理系统中存在冲突的复杂治疗循环

• 批准号: 2322534
• 负责人: Rajesh Rajamani
• 金额: $ 33.33万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-15 至 2027-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2322534&HistoricalAwards=false
• 关键词: Collaborative; Research; CPS; Medium; Automating

Critically ill patients receive multiple treatments, which can be automated for clinicians to enable focus on high-level decision-making tasks, while reducing effort on tedious monitoring and titration tasks. However, automation of multiple critical care treatments presents unique challenges. Each treatment given to a patient elicits multiple physiological changes. Thus, when multiple isolated automated treatments are given to a patient, they can conflict and drive the patient to unsafe physiological states. Regardless, most previous work has neglected such conflicts. To address this challenge, we will develop innovative mathematical algorithms to enable safe automated patient monitoring, treatment guidance, and reconciliation of potentially conflicting medical treatments. We will achieve this goal by leveraging and advancing theory of control, inference, and optimization, thereby creating new knowledge in these fields. Successful completion of this project will lead to new approaches to the coordination and mediation of complex interacting automation systems, including healthcare and medical systems.This project brings together the (cyber) disciplines of data science and control theory, in support of safe and conflict-aware automated treatments in the (physical) discipline of patient therapeutics. We propose to develop a transformative universal framework that can mediate conflicts and assure patient safety in the automation of complex critical care loops in medical CPS. We will develop (i) physiological monitoring enabled by sequential filtering to track the safety of a patient’s internal state using limited measurements; (ii) mediation of complex therapeutic loops enabled by multivariable nonlinear output-feedback control augmented by barrier certificates; and (iii) dynamic updating of treatment setpoints enabled by population-informed adaptation to determine safely reachable patient-specific treatment setpoints. We will demonstrate the efficacy of the proposed framework using a complex critical care resuscitation application as a compelling case study. The success of this project will lead to a suite of innovative technologies deployable to various medical care settings, which can drastically advance autonomy in next-generation medical CPS.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

重症患者接受多种治疗，这些治疗可以自动化，让忠诚者能够专注于高级决策任务，同时减少繁琐的监测和滴定任务的工作量。然而，多种重症护理治疗的自动化带来了独特的挑战。因此，当对患者进行多种孤立的自动化治疗时，它们可能会发生冲突并导致患者进入不安全的生理状态，但为了解决这一挑战，我们将忽略这种冲突。开发创新的数学算法以实现安全的自动化患者监测、治疗我们将通过利用和推进控制、推理和优化理论来实现这一目标，从而在这些领域创造新知识，该项目的成功完成将带来协调和协调的新方法。复杂的交互自动化系统（包括医疗保健和医疗系统）的调解。该项目汇集了数据科学和控制理论的（网络）学科，以支持患者治疗（物理）学科中的安全和冲突感知自动化治疗。提议制定一个变革性的通用框架在医疗 CPS 中复杂重症监护循环的自动化中调解冲突并确保患者安全。我们将开发 (i) 通过顺序过滤实现的生理监测，以使用有限的测量来跟踪患者内部状态的安全性；通过障碍证书增强的多变量非线性输出反馈控制启用的循环；以及（iii）通过人群知情的适应来动态更新治疗设定点，以确定安全可达到的患者特定治疗设定点。使用复杂的重症监护复苏应用程序作为引人注目的案例研究的框架，该项目的成功将带来一套可部署到各种医疗保健环境的创新技术，这可以极大地提高下一代医疗 CPS 的自主性。该奖项反映了 NSF 的成就。法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。