Departmental Papers (CIS)

Date of this Version

9-2016

Document Type

Journal Article

Comments

Journal of Computer Science and Engineering, 10(3), pp.75-84, September 2016.

Open Access

Abstract

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.

Subject Area

CPS Medical

Publication Source

Journal of Computer Science and Engineering

Volume

10

Issue

3

Start Page

75

Last Page

84

DOI

10.5626/JCSE.2016.10.3.75

Keywords

medical cyber-physical systems; data-driven approach; computational virtual subjects; safety monitoring; glucose control

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Date Posted: 05 February 2017

This document has been peer reviewed.