Next Article in Journal
Gaussian Processes and Polynomial Chaos Expansion for Regression Problem: Linkage via the RKHS and Comparison via the KL Divergence
Next Article in Special Issue
Nash Bargaining Game-Theoretic Framework for Power Control in Distributed Multiple-Radar Architecture Underlying Wireless Communication System
Previous Article in Journal
An Investigation into the Relationship among Psychiatric, Demographic and Socio-Economic Variables with Bayesian Network Modeling
Previous Article in Special Issue
Entropy Affects the Competition of Ordered Phases
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Entropy 2018, 20(3), 190; https://doi.org/10.3390/e20030190

Game Theoretic Approach for Systematic Feature Selection; Application in False Alarm Detection in Intensive Care Units

1
School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011, USA
2
Department of Emergency Medicine, University of Michigan, Ann Arbor, MI 48109, USA
*
Author to whom correspondence should be addressed.
Received: 10 January 2018 / Revised: 27 February 2018 / Accepted: 5 March 2018 / Published: 12 March 2018
(This article belongs to the Special Issue Information Theory in Game Theory)
Full-Text   |   PDF [472 KB, uploaded 12 March 2018]   |  

Abstract

Intensive Care Units (ICUs) are equipped with many sophisticated sensors and monitoring devices to provide the highest quality of care for critically ill patients. However, these devices might generate false alarms that reduce standard of care and result in desensitization of caregivers to alarms. Therefore, reducing the number of false alarms is of great importance. Many approaches such as signal processing and machine learning, and designing more accurate sensors have been developed for this purpose. However, the significant intrinsic correlation among the extracted features from different sensors has been mostly overlooked. A majority of current data mining techniques fail to capture such correlation among the collected signals from different sensors that limits their alarm recognition capabilities. Here, we propose a novel information-theoretic predictive modeling technique based on the idea of coalition game theory to enhance the accuracy of false alarm detection in ICUs by accounting for the synergistic power of signal attributes in the feature selection stage. This approach brings together techniques from information theory and game theory to account for inter-features mutual information in determining the most correlated predictors with respect to false alarm by calculating Banzhaf power of each feature. The numerical results show that the proposed method can enhance classification accuracy and improve the area under the ROC (receiver operating characteristic) curve compared to other feature selection techniques, when integrated in classifiers such as Bayes-Net that consider inter-features dependencies. View Full-Text
Keywords: false alarm reduction; intensive care units; feature selection; coalition game theory; Banzhaf power false alarm reduction; intensive care units; feature selection; coalition game theory; Banzhaf power
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Afghah, F.; Razi, A.; Soroushmehr, R.; Ghanbari, H.; Najarian, K. Game Theoretic Approach for Systematic Feature Selection; Application in False Alarm Detection in Intensive Care Units. Entropy 2018, 20, 190.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top