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CAREER: Enabling "White-Box" Autonomy in Medical Cyber-Physical Systems

职业：在医疗网络物理系统中实现“白盒”自治

基本信息

批准号：
1748762
负责人：
Jin-Oh Hahn
金额：
$ 50.04万
依托单位：
University of Maryland, College Park
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-03-15 至 2025-02-28
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1748762&HistoricalAwards=false
关键词：
CAREER Enabling White Box Autonomy

项目摘要

Despite a long-standing effort on the automation in the care of critically ill patients, prior automation capabilities have not been suitably mature for real-world use due to a few limitations: (1) the decisions/actions of the automation could not be easily interpreted by clinicians, preventing clinicians' effective interaction with and supervision of the automation for safe patient care; (2) the automation was designed to perform a particular task of interest without accounting for the overall physiological state of the patient; (3) multiple automation functions were not often coordinated to avoid possible conflicts in patient treatment; (4) automation was prone to errors in the medical devices; and (5) regulatory science for evaluation and approval of safety-critical automation capabilities was lacking. This research program seeks to address these fundamental challenges by studying novel methodologies for (1) mathematically representing the patient physiology in a way to facilitate the interpretation of clinicians and (2) designing automation capabilities that can facilitate clinician interaction and supervision, coordination of multiple treatment goals and functions, and resilience to device errors and faults. In addition, this research program will benefit society and Science, Technology, Engineering and Mathematics (STEM) education by creating a wide range of automation systems that can improve the quality of care of critically ill patients, expediting the deployment of new medical devices with advanced automation capabilities, facilitating the evaluation and approval of emerging healthcare automation capabilities, and training STEM workforce especially from underrepresented minorities. With the long-term vision of enabling interpretable white-box autonomy in healthcare by advancing generalizable methodologies for medical cyber-physical systems (M-CPS), the objective of this CAREER program is to investigate physiological modeling, coordinated and resilient multivariable closed-loop control, and regulatory science methodologies in circulatory resuscitation. An integrated research, education, and outreach program is proposed to achieve this objective. On the research front, physiological modeling and closed-loop control methodologies applicable to white-box autonomy in a wide range of circulatory resuscitation scenarios will be investigated. Specifically, generalizable methodologies for (1) patient physiology and pharmacology modeling and (2) coordinated and resilient multivariable closed-loop control will be developed. The value of these methodologies will be demonstrated by developing interpretable white-box physiological closed-loop control algorithms for circulatory resuscitation. On the education and outreach front, members of the next-generation M-CPS workforce will be trained and regulatory science for evaluating critical care autonomy capabilities will be advanced. Specifically, (1) graduate, undergraduate, and K-12 students will be attracted into STEM and M-CPS, (2) collaboration with the US Food and Drug Administration will be performed to investigate testing methodologies and tools for physiological models and closed-loop M-CPS via its Medical Device Development Tools Program, and (3) newly created knowledge will be disseminated to further advance M-CPS research and education.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.

尽管在危重病人的护理中对自动化进行了长期的努力，但由于以下几个限制，先前的自动化能力尚未适当地成熟以用于现实世界：(1)自动化的决定/动作不能被临床医生容易地解释，妨碍临床医生与自动化进行有效的交互和监督以实现安全的患者护理；(2)自动化被设计为执行特定的感兴趣的任务而不考虑患者的整体生理状态；(3)多个自动化功能经常不被协调以避免患者治疗中可能的冲突；(4)自动化容易在医疗设备中出错；以及(5)缺乏评估和批准安全关键自动化能力的监管科学。这项研究计划旨在通过研究新的方法来解决这些基本挑战：(1)以便于临床医生解释的方式以数学方式表示患者生理；(2)设计自动化功能，以促进临床医生的交互和监督，协调多个治疗目标和功能，以及对设备错误和故障的恢复能力。此外，这项研究计划将通过创建广泛的自动化系统来提高危重患者的护理质量，加快具有先进自动化能力的新医疗设备的部署，促进对新兴医疗自动化能力的评估和批准，以及培训STEM劳动力，特别是来自代表不足的少数族裔的STEM劳动力，从而使社会和科学、技术、工程和数学(STEM)教育受益。这个职业计划的目标是通过推进医疗网络物理系统(M-CPS)的通用方法来实现医疗保健中可解释的白盒自治，该职业计划的目标是研究循环复苏中的生理建模、协调和弹性多变量闭环控制以及调控科学方法。为了实现这一目标，提出了一个综合的研究、教育和推广计划。在研究方面，将研究在广泛的循环复苏场景中适用于白盒自主的生理建模和闭环控制方法。具体地说，将开发可推广的方法，用于(1)患者生理学和药理学建模和(2)协调和弹性多变量闭环控制。这些方法的价值将通过开发可解释的循环复苏白盒生理闭环控制算法来展示。在教育和外联方面，下一代M-CPS工作人员将接受培训，并将推进评估危重护理自主能力的监管科学。具体地说，(1)研究生、本科生和K-12学生将被吸引到STEM和M-CPS，(2)将与美国食品和药物管理局合作，通过其医疗器械开发工具计划，研究生理模型和闭环系统M-CPS的测试方法和工具，以及(3)将传播新创造的知识，以进一步推动M-CPS的研究和教育。该奖项反映了NSF的法定使命，并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，被认为值得支持。

项目成果

期刊论文数量（25）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Generative Modeling for Hardware-in-the-Loop Testing of Fluid Resuscitation Control Algorithms

液体复苏控制算法硬件在环测试的生成模型

DOI：
发表时间：
2022
期刊：
Military Health System Research Symposium
影响因子：
0
作者：
Ali Tivay, Ramin Bighamian
通讯作者：
Ali Tivay, Ramin Bighamian

Hemodynamic Safety Assurance in Closed-Loop Controlled Critical Care: Hemorrhage Resuscitation and Sedation Case Study

闭环控制重症监护中的血流动力学安全保证：出血复苏和镇静案例研究

DOI：
发表时间：
2023
期刊：
American Control Conference
影响因子：
0
作者：
W. Yin, A. Tivay
通讯作者：
W. Yin, A. Tivay

A Regularized System Identification Approach to Subject-Specific Physiological Modeling with Limited Data*

利用有限数据进行特定学科生理建模的正则系统识别方法*

DOI：
10.23919/acc.2019.8815199
发表时间：
2019
期刊：
American Control Conference
影响因子：
0
作者：
Tivay, Ali;Darreh Dor, Ghazal Arabi;Bighamian, Ramin;Kramer, George C.;Hahn, Jin-Oh
通讯作者：
Hahn, Jin-Oh

Virtual Patient Generation using Physiological Models through a Compressed Latent Parameterization