Career: Learning-Enabled Medical Cyber-Physical Systems

安全关键医疗系统越来越多地旨在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的患者护理和健康结果，但确保其安全性和有效性仍然是一项艰巨的挑战。 现有的基于模型的设计范例，用于学习使能的网络物理系统，需要大量的“干净”数据或高保真仿真器-不幸的是，LE-MCPS没有这样的奢侈品。 因此，LE-MCPS的开发在很大程度上依赖于实验来生成设计和保证数据。在安全关键的医疗应用中工作的伦理和经济约束需要实验效率。然而，实验设计和学习支持的组件设计通常是弱耦合的，这导致了效率低下，开发成本增加，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。 具体而言，研究重点是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）相关的基本挑战。该项目的广泛意义将推动医疗系统设计的最新发展，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，为确保LE-MCPS开发基础和工具。拟议的研究将导致一个高保证LE-CPS设计框架，跨越前，内，后实验。在实验之前，这项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差距。在实验期间，将实现新的平台和功能，可以支持防篡改运行时实验数据管理，以确保LE-CPS。在实验之后，利用历史证据和实验数据的技术将最大限度地确保LE-CPS设计。该项目中开发的基础在工业LE-MCPS应用中进行了前瞻性评估。虽然这项研究是由医疗方案的动机，开发的技术是立即适用于广泛的LE-CPS应用。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.