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

Fatemeh Afghah¹, Abolfazl Razi¹, Reza Soroushmehr²

¹School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011, USA.

Entropy (Basel, Switzerland)

|December 3, 2020

Summary

This summary is machine-generated.

Reducing false alarms in Intensive Care Units (ICUs) is crucial. A new method uses game theory to better select important data features, improving alarm detection accuracy.

Keywords:

Banzhaf power coalition game theory false alarm reduction feature selection intensive care units

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Area of Science:

Biomedical Engineering
Data Science
Critical Care Medicine

Background:

Intensive Care Units (ICUs) rely on sophisticated monitoring devices.
These devices generate numerous false alarms, leading to alarm fatigue and reduced care quality.
Existing methods often overlook correlations between sensor data features.

Purpose of the Study:

To enhance the accuracy of false alarm detection in ICUs.
To address the limitation of current data mining techniques in capturing inter-feature correlations.
To leverage information and game theory for improved feature selection.

Main Methods:

Proposed a novel information-theoretic predictive modeling technique.
Utilized coalition game theory, specifically Banzhaf power, for feature selection.
Accounted for inter-features mutual information to identify correlated predictors for false alarms.
Integrated the approach with classifiers like Bayes-Net that consider feature dependencies.

Main Results:

The proposed method demonstrated enhanced classification accuracy.
Improved the area under the receiver operating characteristic (ROC) curve compared to other techniques.
Effectively captured synergistic power among signal attributes in feature selection.

Conclusions:

The novel approach successfully improves false alarm detection in ICUs.
Accounting for inter-feature correlations via game theory enhances predictive modeling accuracy.
This method offers a promising direction for reducing alarm fatigue and improving patient monitoring.