CRII: SCH: A Computational Framework to False Alarm Suppression in Intensive Care Units

误报被广泛认为是使用医疗技术带来的头号危害。紧急护理研究所（Emergency Care Research Institute）将警报危害列为2012年、2013年和2015年“十大卫生技术危害”之首。医疗保健提供者通常不堪重负，每个病人每天350警报条件，其中80-99%是毫无意义或虚假的。这些假警报可能是由于多种因素造成的，如患者移动、单个传感器故障以及患者与设备接触的不完善，导致医疗保健提供者的警报疲劳，并有可能在多个警报的嘈杂声中错过真正危及生命的事件。这些假警报还会导致患者焦虑、睡眠结构低下和免疫系统低下。因此，警报安全已被联合委员会确定为国家患者安全目标，该委员会对美国近21,000个医疗机构和项目进行认证和认证。该项目将开发一个多方面的框架，通过整合信息论、博弈论、图论和信号处理的原理来降低重症监护病房（icu）的误报率。icu中的警报大多是基于单个机器/监视器的测量创建的，而这些单个机器产生的大多数警报被认为是假的。目前大多数抑制虚警率的方法都试图设计新的监视器或创建更精确的传感器。这些方法通常针对特定的设备或数据集进行定制，并且在这些方法中忽略了从不同传感器提取的特征之间的重要内在相关性。通过整合来自各种设备的信息，并使用新的博弈论方法考虑从这些设备收集的特征之间的非线性相关性和互信息，将开发一种实时，准确而通用的方法来减少假警报的数量，同时避免抑制真警报。该方法的性能将使用PhysionNet的公开可用的MIMIC II数据集进行评估，考虑ECG、PLETH和APB这三个重要信号。提出的虚警检测方法可以潜在地挽救许多患者的生命，并显著降低医疗成本。本研究通过开发博弈论优化的新课程，将本研究整合到几个本科水平的课程模块中，并对研究生进行培训，可以推进新成立的北亚利桑那大学信息、计算与网络系统学院（SICCS）的研究计划和教学实践。

False alarms are widely considered the number one hazard imposed by the use of medical technologies. The Emergency Care Research Institute named alarm hazards as the number one of the 'Top 10 Health Technology Hazards' for 2012, 2013 and 2015. Healthcare providers are usually overwhelmed with 350 alarm conditions per patient per day, of which 80-99% are meaningless or false. These false alarms can be due to several factors such as patient movement, malfunction of individual sensors and imperfections in the patient-equipment contact, resulting in alarm fatigue among healthcare providers and the possibility of missing a true life-threatening event lost in a cacophony of multiple alarms. These false alarms can also cause patient anxiety, inferior sleep structure and depressed immune systems. Thereby, alarm safety has been determined as a national patient safety goal by The Joint Commission, which accredits and certifies nearly 21,000 health care organizations and programs in the United States. This project will develop a multifaceted framework to reduce the false alarm rate in Intensive Care Units (ICUs) by integrating principles from information theory, game theory, graph theory and signal processing. The alarms in ICUs are mostly created based on the measurements made by individual machine/monitors, while the majority of the alarms produced by these individual machines are considered false. The majority of current methods to suppress the false alarm rate attempt to design new monitors or create more accurate sensors. These methods are often tailored to specific devices or datasets and the significant intrinsic correlations among the extracted features from different sensors are overlooked in these methods. A real-time and accurate yet general method will be developed to reduce the number of false alarms while avoiding the suppression of true alarms through integrating information from a variety of devices and considering the non-linear correlations and mutual information among the features collected from these devices using a new game theoretic approach. The performance of this proposed method will be evaluated using PhysionNet's publicly available MIMIC II dataset considering the three vital signals of ECG, PLETH, and APB. The proposed false alarm detection method can potentially save many patients' lives and significantly reduce medical costs. This work can advance the research program and practice of teaching at the newly established Northern Arizona University School of Informatics, Computing and Cyber System (SICCS) by developing a new course in game theoretical optimizations, integration of this research in several undergraduate-level course modules and training of graduate students.