Game Theoretic Approach for Systematic Feature Selection; Application in False Alarm Detection in Intensive Care Units.
Game Theoretic Approach for Systematic Feature Selection; Application in False Alarm Detection in Intensive Care Units.
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DOI：
10.3390/e20030190
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发表时间：
2018-03-12
期刊：
影响因子：
--
通讯作者：
Najarian K
中科院分区：
文献类型：
--
作者：
Afghah F;Razi A;Soroushmehr R;Ghanbari H;Najarian K
重症监护室（ICU）配备了许多复杂的传感器和监测设备，为重症患者提供最高质量的护理。然而，这些设备可能会产生错误警报，降低护理标准，并导致护理人员对警报不敏感。因此，减少误报警的数量是非常重要的。为此，已经开发了许多方法，例如信号处理和机器学习，以及设计更精确的传感器。然而，从不同的传感器提取的特征之间的显着的内在相关性大多被忽视。大多数当前的数据挖掘技术未能捕获从不同传感器收集的信号之间的这种相关性，这限制了它们的报警识别能力。在这里，我们提出了一种新的信息理论的预测建模技术的基础上的联合博弈论的思想，以提高在ICU的虚警检测的准确性，占信号属性的协同功率在特征选择阶段。这种方法汇集了来自信息论和博弈论的技术，通过计算每个特征的Banzhaf功率来考虑特征间互信息，以确定与虚警最相关的预测因子。数值结果表明，所提出的方法可以提高分类精度和改善ROC（接收器工作特性）曲线下的面积相比，其他特征选择技术，当集成在分类器，如贝叶斯网络，考虑功能间的依赖关系。
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.
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DOI：
10.1016/j.bpa.2008.07.008
发表时间：
2009-03-01
影响因子：
4.8
作者：
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通讯作者：
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影响因子：
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影响因子：
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Clifford, Gari D.
影响因子：
3.2
作者：
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通讯作者：
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