Career: Learning-Enabled Medical Cyber-Physical Systems

安全关键型医疗系统越来越多地致力于整合使用机器学习和人工智能开发的支持学习的组件。虽然这些学习型医疗网络物理系统（LE-MCPS）的影响正在彻底改变个性化患者护理和健康结果，但确保其安全性和有效性仍然是一个艰巨的挑战。现有的基于模型的设计范例需要大量“干净”的数据或高保真度的模拟器——不幸的是，LE-MCPS没有这种奢侈。因此，LE-MCPS的开发强烈依赖于实验来生成设计和保证的数据。在安全关键医疗应用中工作的伦理和经济约束要求实验效率。然而，实验设计和支持学习的组件设计通常是弱耦合的，这导致效率低下、开发成本增加和患者风险增加。该CAREER提案旨在通过弥合实验和基于模型的设计之间的差距，为确保学习型医疗网络物理系统（MCPS）开发基础和工具。具体地说，该研究侧重于利用基于模型的设计技术来解决与实验设计（实验前）、协议执行（实验期间）和系统保证（实验后）相关的基础挑战。该项目具有更广泛的意义，将推动最先进的医疗系统设计，加速学习型CPS （LE-CPS）创新，并提供丰富的跨学科和使用启发式教育机会和推广活动。该项目的目标是通过弥合实验和基于模型的设计之间的差距，开发确保LE-MCPS的基础和工具。拟议的研究将产生一个高保证的LE-CPS设计框架，涵盖实验前、实验内和实验后。在实验之前，本工作将开发基础技术，以解决高保证LE-CPS设计暴露的传统实验设计中的空白。在实验过程中，将实现新的平台和功能，可以支持可篡改的运行时实验数据管理，以确保LE-CPS。经过实验，利用历史证据和实验数据的技术将最大限度地保证LE-CPS设计。该项目开发的基础在工业LE-MCPS应用中进行了前瞻性评估。虽然这项研究的动机是医疗场景，但开发的技术立即适用于广泛的LE-CPS应用。该奖项反映了美国国家科学基金会的法定使命，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。

Safety critical medical systems increasingly aim to incorporate learning-enabled components that are developed using machine learning and AI. While the impact of these learning-enabled medical cyber-physical systems (LE-MCPS) are revolutionizing personalized patient care and health outcomes, assuring their safety and efficacy remains a formidable challenge. Existing model-based design paradigms for learning-enabled cyber-physical systems require an abundance of “clean” data or high-fidelity simulators – unfortunately, LE-MCPS do not have that luxury. Consequently, LE-MCPS development strongly depends on experimentation to generate data for design and assurance. The ethical and economic constraints of working in safety-critical medical applications necessitate experimentation efficiency. Yet, experimental design and learning-enabled component design are often weakly coupled -- which contributes to inefficiencies, increased development costs, and increased patient risk. This CAREER proposal aims to develop foundations and tools for assuring learning-enabled medical cyber physical systems (MCPS) by bridging-the-gap between experimentation and model-based design. Specifically, the research focuses on leveraging model-based design techniques to address foundational challenges associated with experimental design (ante-experimentation), protocol execution (during experimentation), and system assurance (post-experimentation). The project’s broader significance will advance the state-of-the-art in medical system design, accelerate learning-enabled CPS (LE-CPS) innovation, and provide abundant interdisciplinary and use-inspired education opportunities and outreach activities.The goal of this project is to develop foundations and tools for assuring LE-MCPS by bridging-the-gap between experimentation and model-based design. The proposed research will result in a high-assurance LE-CPS design framework spanning ante-, intra-, and post-experimentation. Prior to experimentation, this work will develop foundational techniques to address gaps in traditional experimental designed exposed by high-assurance LE-CPS design. During experimentation, new platforms and capabilities will be realized that can support tamper-evident run-time experimental data curation for assuring LE-CPS. After experimentation, techniques that leverage historical evidence and experimental data will maximally assure LE-CPS designs. Foundations developed in the project are prospectively evaluated in industrial LE-MCPS applications. While the research is motivated by medical scenarios, the developed technologies are immediately applicable to a wide range of LE-CPS applications.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.