Date of this Version
Sanjian Chen, Oleg Sokolsky, James Weimer, and Insup Lee, "Data-driven Adaptive Safety Monitoring using Virtual Subjects in Medical Cyber-Physical Systems: A Glucose Control Case Study", Journal of Computer Science and Engineering 10(3), 75-84. September 2016. http://dx.doi.org/10.5626/JCSE.2016.10.3.75
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.
Journal of Computer Science and Engineering
medical cyber-physical systems; data-driven approach; computational virtual subjects; safety monitoring; glucose control
Date Posted: 05 February 2017
This document has been peer reviewed.