http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Chen, Sanjian,Sokolsky, Oleg,Weimer, James,Lee, Insup Korean Institute of Information Scientists and Eng 2016 Journal of Computing Science and Engineering Vol.10 No.3
Medical cyber-physical systems (MCPS) integrate sensors, actuators, and software to improve patient safety and quality of healthcare. These systems introduce major challenges to safety analysis because the patient's physiology is complex, nonlinear, unobservable, and uncertain. To cope with the challenge that unidentified physiological parameters may exhibit short-term variances in certain clinical scenarios, we propose a novel run-time predictive safety monitoring technique that leverages a maximal model coupled with online training of a computational virtual subject (CVS) set. The proposed monitor predicts safety-critical events at run-time using only clinically available measurements. We apply the technique to a surgical glucose control case study. Evaluation on retrospective real clinical data shows that the algorithm achieves 96% sensitivity with a low average false alarm rate of 0.5 false alarm per surgery.
Data-driven Adaptive Safety Monitoring Using Virtual Subjects in Medical Cyber-Physical Systems
Sanjian Chen,Oleg Sokolsky,James Weimer,Insup Lee 한국정보과학회 2016 Journal of Computing Science and Engineering Vol.10 No.3
Medical cyber-physical systems (MCPS) integrate sensors, actuators, and software to improve patient safety and quality of healthcare. These systems introduce major challenges to safety analysis because the patient’s physiology is complex, nonlinear, unobservable, and uncertain. To cope with the challenge that unidentified physiological parameters may exhibit short-term variances in certain clinical scenarios, we propose a novel run-time predictive safety monitoring technique that leverages a maximal model coupled with online training of a computational virtual subject (CVS) set. The proposed monitor predicts safety-critical events at run-time using only clinically available measurements. We apply the technique to a surgical glucose control case study. Evaluation on retrospective real clinical data shows that the algorithm achieves 96% sensitivity with a low average false alarm rate of 0.5 false alarm per surgery.
Challenges and Research Directions in Medical Cyber–Physical Systems
Insup Lee,Sokolsky, O.,Sanjian Chen,Hatcliff, J.,Eunkyoung Jee,BaekGyu Kim,King, A.,Mullen-Fortino, Margaret,Soojin Park,Roederer, A.,Venkatasubramanian, K. K. IEEE 2012 Proceedings of the Institute of Electrical and Ele Vol.100 No.1
<P>Medical cyber-physical systems (MCPS) are life-critical, context-aware, networked systems of medical devices. These systems are increasingly used in hospitals to provide high-quality continuous care for patients. The need to design complex MCPS that are both safe and effective has presented numerous challenges, including achieving high assurance in system software, intoperability, context-aware intelligence, autonomy, security and privacy, and device certifiability. In this paper, we discuss these challenges in developing MCPS, some of our work in addressing them, and several open research issues.</P>