Cart (Loading....) | Create Account
Close category search window
 

Identification of patient deterioration in vital-sign data using one-class support vector machines

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Clifton, L. ; Dept. of Eng. Sci., Univ. of Oxford, Oxford, UK ; Clifton, D.A. ; Watkinson, P.J. ; Tarassenko, L.

Adverse hospital patient outcomes due to deterioration are often preceded by periods of physiological deterioration that is evident in the vital signs, such as heart rate, respiratory rate, etc. Clinical practice currently relies on periodic, manual observation of vital signs, which typically occurs every 2-to-4 hours in most hospital wards, and so patient deterioration may go unidentified. While continuous patient monitoring systems exist for those patients who are confined to a hospital bed, the false alarm rate of conventional systems is typically so high that the alarms generated by them are ignored. This paper explores the use of machine learning methods for automatically identifying patient deterioration, using data acquired from continuous patient monitors. We compare generative and discriminative techniques (a probabilistic method using a mixture model, and a support vector machine, respectively). It is well-known that parameter tuning affects the performance of such methods, and we propose a method to optimise parameter values using “partial AUC”. We demonstrate the performance of the proposed method using both synthetic data and patient vital-sign data collected from a recent observational clinical study.

Published in:

Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on

Date of Conference:

18-21 Sept. 2011

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.