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

虽然机械呼吸机提供救生呼吸支持， （PAMV）可导致严重的并发症（如肺炎）和增加的医疗费用-预计 到2020年将超过320亿美元，占所有医院成本的10%以上。因此，机械通气 需要尽早停药，通常采用一种称为"断奶"的方法。但 最好的断奶方法仍然是一个悬而未决的问题，并受到争议，估计 美国重症监护病房每年有17万例可预防的死亡是由于不适当的呼吸机造成的 断奶因此，紧急护理研究所（ECRI）将不正确的呼吸机设置列为 2019年十大健康技术危害。 自主机械通气脱机验证了医疗网络物理系统（MCPS）， 需要互连的医疗设备的集合（例如，患者监护仪）， 协调用于治疗患者（即，执行安全自主脱机）。这项建议旨在 在安全有效的数据驱动情境感知人在回路控制方面取得根本性进展 这将使自主机械通气脱机成为可能。虽然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MCPS，确保 使用数据驱动组件的安全性和可靠性仍然与人类操作员相适应 一个挑战.我们将展示我们的闭环设计和分析技术在 自主MCPS用于机械通气脱机。 拟议的项目直接符合国家生物医学成像研究所的使命， 生物工程（NIBIB）通过研究和开发新技术来推进医疗 通过医疗设备互操作性和临床决策支持进行护理， 机械通气脱机。也与NIBIB使命有关，实现了本项目的目标 需要多学科的方法和计算机科学和临床护理的互补进展。 从医疗保健的角度来看，该项目将提供基于证据和个性化的决策支持 帮助护理人员更好地进行机械通气脱机。自动机械的长期影响 通气脱机疼痛将缩短住院时间，减轻医疗保健的经济负担， 减少不适当断奶的致命副作用。

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.