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.

重症患者接受多种治疗，这些治疗可以自动化，使临床医生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。 然而，多个重症监护治疗的自动化提出了独特的挑战。 给予病人的每次治疗都会引起多种生理变化。 因此，当对患者进行多个孤立的自动化治疗时，它们可能会发生冲突并将患者推向不安全的生理状态。 尽管如此，大多数以前的工作都忽略了这样的冲突。 为了应对这一挑战，我们将开发创新的数学算法，以实现安全的自动化患者监测，治疗指导和协调潜在的冲突医疗。 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的知识。 该项目的成功完成将为复杂的交互自动化系统（包括医疗保健和医疗系统）的协调和调解带来新的方法。该项目汇集了数据科学和控制理论的（网络）学科，支持患者治疗学（物理）学科中的安全和冲突感知自动化治疗。我们建议开发一个变革性的通用框架，可以调解冲突，并确保病人的安全，在医疗CPS复杂的重症监护回路的自动化。 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;（ii）通过屏障证书增强的多变量非线性输出反馈控制实现的复杂治疗回路的调解;以及（iii）通过人群知情适应实现的治疗设定点的动态更新，以确定安全可达的患者特定治疗设定点。 我们将证明所提出的框架的有效性，使用一个复杂的重症监护复苏应用程序作为一个引人注目的案例研究。 该项目的成功将导致一套可部署到各种医疗保健环境的创新技术，这可以大大提高下一代医疗CPS的自主性。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。