False alarm reduction in critical care.
False alarm reduction in critical care.
复制标题
误解重症监护。
DOI：
10.1088/0967-3334/37/8/e5
复制
发表时间：
2016-08
影响因子：
3.2
通讯作者：
Mark RG
中科院分区：
文献类型：
--
作者：
Clifford GD;Silva I;Moody B;Li Q;Kella D;Chahin A;Kooistra T;Perry D;Mark RG
高虚警率在ICU降低护理质量减慢工作人员的反应时间，同时增加患者谵妄通过噪音污染。2015年Physio-Net/Computing in Cardiology挑战赛提供了1250组与严重心律失常警报相关的多参数ICU数据段，并挑战一般研究界使用所有可用信号来解决假警报抑制问题。每个数据段的长度为5分钟（用于实时分析），在产生告警时结束。为了进行回顾性分析，我们在警报触发后又提供了30秒的数据。共有750个数据段供培训使用，500个数据段留作测试。每个警报都由专家注释者审查，其中至少有两人同意警报是真的还是假的。挑战参与者被邀请提交一个完整的、有效的算法来区分真假警报，并根据他们的程序在隐藏测试集中的表现获得一个分数。这个分数是基于警报正确的百分比，但对真实警报的压制比接受假警报的惩罚重五倍。我们提供了三个基于知名的开源信号处理算法的示例条目，作为比较的基础，并作为参与者开发自己代码的起点。在今年的挑战赛中，共有38个团队提交了215个参赛作品。这篇社论回顾了这次挑战的背景问题，挑战本身的设计，主要成就，以及由于挑战而产生的后续研究，发表在同期的生理测量特刊上。此外，我们对挑战后患者监测领域的未来变化提出了一些建议。
High false alarm rates in the ICU decrease quality of care by slowing staff response times while increasing patient delirium through noise pollution. The 2015 Physio-Net/Computing in Cardiology Challenge provides a set of 1,250 multi-parameter ICU data segments associated with critical arrhythmia alarms, and challenges the general research community to address the issue of false alarm suppression using all available signals. Each data segment was 5 minutes long (for real time analysis), ending at the time of the alarm. For retrospective analysis, we provided a further 30 seconds of data after the alarm was triggered. A total of 750 data segments were made available for training and 500 were held back for testing. Each alarm was reviewed by expert annotators, at least two of whom agreed that the alarm was either true or false. Challenge participants were invited to submit a complete, working algorithm to distinguish true from false alarms, and received a score based on their program's performance on the hidden test set. This score was based on the percentage of alarms correct, but with a penalty that weights the suppression of true alarms five times more heavily than acceptance of false alarms. We provided three example entries based on well-known, open source signal processing algorithms, to serve as a basis for comparison and as a starting point for participants to develop their own code. A total of 38 teams submitted a total of 215 entries in this year's Challenge. This editorial reviews the background issues for this Challenge, the design of the Challenge itself, the key achievements, and the follow-up research generated as a result of the Challenge, published in the concurrent special issue of Physiological Measurement. Additionally we make some recommendations for future changes in the field of patient monitoring as a result of the Challenge.