CPS: AutoWean: Foundations of Autonomous Medical CPS for Mechanical Ventilation Weaning

CPS：AutoWean：机械通气脱机自主医疗 CPS 的基础

• 批准号: 10022304
• 负责人: BARRY D FUCHS
• 金额: $ 33.19万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-30 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10022304
• 关键词: Accounting; Acute; Awareness; Caregivers; Caring; Collection; Data; Economic Burden; Emergency research; Foundations; Health Care Costs; Health Technology; Healthcare; Hospital Costs; Human; Intensive Care Units; Lead; Length of Stay; Life; Machine Learning; Mechanical Ventilators; Mechanical ventilation; Medical; Medical Device; Mission; National Institute of Biomedical Imaging and Bioengineering; Pain; Patient Monitoring; Patients; Pneumonia; Process; Research Institute; Safety; Savings; System; Techniques; Ventilator; Ventilator Weaning; Weaning; clinical care; clinical decision support; computer science; cyber physical; design; evidence base; hazard; human-in-the-loop; interdisciplinary approach; interoperability; new technology; personalized decision; preventable death; research and development; respiratory; side effect; technology development

While mechanical ventilators provide life-saving respiratory support, prolonged acute mechanical ventilation (PAMV) can lead to severe complications (e.g. , pneumonia) and increased healthcare costs - predicted to be over $32 billion in 2020 and accounting for over 10% of all hospital costs. Thus, mechanical ventilation needs to be discontinued as early as possible, often by using a process known as "weaning". However, the best approach to weaning remains an open question and is subject to controversy, where estimated 170,000 preventable deaths per year in US intensive care units are a result of inappropriate ventilator weaning. Consequently, the Emergency Care Research Institute (ECRI) lists improper ventilator settings as a Top 10 Health Technology Hazard in 2019. Autonomous mechanical ventilation weaning exemplifies a medical cyber-physical systems (MCPS) that requires collections of interconnected medical devices (e.g., ventilators and patient monitors) that are coordinated for treating a patient (i.e. , performing safe autonomous weaning). This proposal aims to develop fundamental advances in safe and effective data-driven context-aware human-in-the-loop control that will enable autonomous mechanical ventilation weaning . While closed-loop control and data-driven techniques (e.g., system identification and machine learning) have been applied to MCPS, assuring the safety and reliability of using data-driven components that adapt in-the-loop with a human operator remains a challenge. We will demonstrate the impact of our closed-loop design and analysis techniques in autonomous MCPS for mechanical ventilation weaning . The proposed project directly aligns with the mission of the National Institute of Biomedical Imaging and Bioengineering (NIBIB) through the research and development of new technologies to advance medical care through medical device interoperability and clinical decision support for enabling autonomous mechanical ventilation weaning. Also relevant to the NIBIB mission, achieving the aims of this project requires a multidisciplinary approach and complementary advances in computer science and clinical care. From the healthcare perspective, this project will offer evidence-based and personalized decision support to caregivers for better mechanical ventilation weaning. The long-term impact of autonomous mechanical ventilation weaning pain will lead to shorter hospital stays, reduce the economic burden of health care, and reduce the deadly side effects of inappropriate weaning.

机械呼吸机提供了挽救生命的呼吸支持，但急性机械通气延长 （PAMV）可能导致严重的并发症（例如肺炎）和医疗保健成本增加 - 预计 在2020年，超过320亿美元，占所有医院费用的10％以上。因此，机械通气 经常使用称为“断奶”的过程，需要尽早停止。但是， 断奶的最佳方法仍然是一个悬而未决的问题，并有争议，估计 美国重症监护病房每年170,000例可预防的死亡是不适当的呼吸机的结果 断奶。因此，紧急护理研究所（ECRI）列出了不当的呼吸机设置为 2019年排名前10的卫生技术危害。 自主机械通气断奶说明了医学网络物理系统（MCP） 需要集合相互联系的医疗设备（例如呼吸机和患者监视器） 协调治疗患者（即执行安全的自主断奶）。该建议旨在 在安全有效的数据驱动的上下文感知的人类在环境控制方面发展基本进步 这将使自动机械通风断奶。而闭环控制和数据驱动 技术（例如系统识别和机器学习）已应用于MCP，以确保 使用数据驱动组件的安全性和可靠性，这些组件与人类操作员一起适应环境 挑战。我们将证明我们的闭环设计和分析技术在 用于机械通气断奶的自动MCP。 拟议的项目直接与国家生物医学成像研究所的使命保持一致 通过研究和开发新技术来推进医学 通过医疗设备的互操作性和临床决策支持，以实现自主 机械通风断奶。也与尼比布任务有关，实现了该项目的目标 需要一种多学科的方法以及计算机科学和临床护理的互补进步。 从医疗保健的角度来看，该项目将提供循证和个性化的决策支持 给护理人员提供更好的机械通风断奶。自动机械的长期影响 通风断奶疼痛将导致医院较短，减轻医疗保健的经济负担，并 减少不适当断奶的致命副作用。