Zaeri-Amirani Mohammad, Afghah Fatemeh, Mousavi Sajad
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:319-323. doi: 10.1109/EMBC.2018.8512266.
High false alarm rate in intensive care units (ICUs) has been identified as one of the most critical medical challenges in recent years. This often results in overwhelming the clinical staff by numerous false or unurgent alarms and decreasing the quality of care through enhancing the probability of missing true alarms as well as causing delirium, stress, sleep deprivation and depressed immune systems for patients. One major cause of false alarms in clinical practice is that the collected signals from different devices are processed individually to trigger an alarm, while there exists a considerable chance that the signal collected from one device is corrupted by noise or motion artifacts. In this paper, we propose a low-computational complexity yet accurate game-theoretic feature selection method which is based on a genetic algorithm that identifies the most informative biomarkers across the signals collected from various monitoring devices and can considerably reduce the rate of false alarms .
重症监护病房（ICU）中的高误报率已被确认为近年来最关键的医学挑战之一。这通常会导致大量虚假或非紧急警报使临床工作人员应接不暇，并通过增加错过真正警报的可能性以及导致患者出现谵妄、压力、睡眠剥夺和免疫系统抑制，从而降低护理质量。临床实践中误报的一个主要原因是，来自不同设备的采集信号被单独处理以触发警报，而从一个设备采集的信号很有可能被噪声或运动伪影破坏。在本文中，我们提出了一种计算复杂度低但准确的博弈论特征选择方法，该方法基于遗传算法，可识别从各种监测设备采集的信号中最具信息量的生物标志物，并能显著降低误报率。
