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: 2322533
• 负责人: Jin-Oh Hahn
• 金额: $ 66.67万
• 依托单位: University of Maryland, College Park
• 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=2322533&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.

重症患者会接受多种治疗，可以使临床医生自动化，以便将重点放在高级决策任务上，同时减少乏味的监测和滴定任务。但是，多种重症监护治疗的自动化提出了独特的挑战。每种治疗方法都会引起多次身体变化。当对患者进行多种孤立的自动疗法时，它们会冲突并驱使患者到不安全的生理状态。无论如何，大多数以前的工作都忽略了此类冲突。为了应对这一挑战，我们将开发创新的数学算法，以实现安全自动化的患者监测，治疗指导和对可能相互矛盾的医疗治疗的和解。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，从而在这些领域中创造新知识。成功完成该项目，我们建议开发一个变革性的通用框架，该框架可以调解冲突并确保患者安全性在医疗CPS中复杂的重症监护循环自动化。我们将通过有限的测量值来对（i）通过顺序过滤来跟踪患者内部状态的安全； （ii）通过屏障证书增强的多变量非线性输出反馈控制来启用复杂的治疗环的调解； （iii）通过人口信息适应来确定可安全到达的患者特异性治疗设定的动态更新治疗设定点。我们将使用复杂的重症监护复苏应用程序作为令人信服的案例研究来证明所提出的框架的效率。该项目的成功将导致一系列可部署到各种医疗环境的创新技术，这可以在下一代医学CPS中极大地提高自主权。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来通过评估而被认为是珍贵的支持。