Career: Learning-Enabled Medical Cyber-Physical Systems

职业：支持学习的医疗网络物理系统

• 批准号: 2339637
• 负责人: JAMES WEIMER
• 金额: $ 57.51万
• 依托单位: Vanderbilt University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-01 至 2029-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339637&HistoricalAwards=false
• 关键词: Career; Learning; Enabled; Medical; Cyber

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.

安全关键医疗系统越来越多地旨在结合使用机器学习和AI开发的学习组件。尽管这些支持学习的医学网络物理系统（LE-MCP）的影响正在彻底改变个性化的患者护理和健康状况，但确保其安全性和有效性仍然是一个巨大的挑战。现有的基于模型的设计范例用于支持学习的网络物理系统需要抽象“干净”数据或高保真模拟器 - 不幸的是，LE-MCP没有这种奢侈品。因此，LE-MCP的开发在很大程度上取决于实验以生成设计和保证的数据。在安全至关重要的医学应用中工作的道德和经济限制必要的实验效率。然而，实验设计和支持学习的组​​件设计通常会微弱地耦合 - 这会导致效率低下，增加的发展成本和增加患者的风险。该职业建议旨在通过在实验和基于模型的设计之间弥合差距来开发基础和工具来确保支持学习的医疗网络物理系统（MCP）。具体而言，该研究的重点是利用基于模型的设计技术来解决与实验设计（前方实验），协议执行（在实验期间）和系统保证（经验后）相关的基础挑战。项目的更广泛的意义将推进医疗系统设计，加速支持学习的CPS（LE-CPS）创新，并提供全面的跨学科和使用启发的教育机会和外展活动。该项目的目的是开发基础和工具，以通过实验和基于模型的设计在实验和模型的设计之间来确保LE-MCP。拟议的研究将导致跨越，内部和实验后的高增强LE-CPS设计框架。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。在实验过程中，将实现新的平台和功能，以支持篡改运行时实验数据策展以确保LE-CPS。经过实验，利用历史证据和实验数据的技术将最大程度地确保LE-CPS设计。该项目中开发的基础在工业LE-MCPS应用程序中进行了前瞻性评估。尽管该研究是受医疗方案激励的，但已开发的技术立即适用于广泛的LE-CPS应用程序。该奖项反映了NSF的法定使命，并通过使用基金会的知识分子优点和更广泛的审查标准评估来诚实地获得支持。