High-Confidence Data Programming for Evaluating Suppression of Physiological Alarms
High-Confidence Data Programming for Evaluating Suppression of Physiological Alarms
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DOI：
10.1109/chase52844.2021.00015
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Sydney Pugh;I. Ruchkin;Christopher P. Bonafide;S. Demauro;O. Sokolsky;Insup Lee;James Weimer
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文献类型：
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作者：
Sydney Pugh;I. Ruchkin;Christopher P. Bonafide;S. Demauro;O. Sokolsky;Insup Lee;James Weimer
生理监测器产生的假警报可能会使临床护理人员无法应付各种警报。由此产生的警报疲劳可以通过警报抑制来缓解。在部署之前，这种抑制机制需要通过昂贵的观察研究进行评估，这将确定和标记真正可抑制的警报。本文提出了一种轻量级的方法来评估报警抑制没有访问真正的报警标签。该方法是基于数据编程范式，它结合了噪声和廉价获得标签的概率标签。基于这些标签，该方法估计抑制机制的灵敏度/特异性，并以置信界限的形式描述观察性研究的可能结果。我们使用费城儿童医院收集的数据集在低SpO 2报警的案例研究中评估了所提出的方法，并表明我们的方法提供了严格和准确的界限，显着优于天真的比较方法。
False alarms generated by physiological monitors can overwhelm clinical caretakers with a variety of alarms. The resulting alarm fatigue can be mitigated with alarm suppression. Before being deployed, such suppression mechanisms need to be evaluated through a costly observational study, which would determine and label the truly suppressible alarms. This paper proposes a lightweight method for evaluating alarm suppression without access to the true alarm labels. The method is based on the data programming paradigm, which combines noisy and cheap-to-obtain labeling heuristics into probabilistic labels. Based on these labels, the method estimates the sensitivity/specificity of a suppression mechanism and describes the likely outcomes of an observational study in the form of confidence bounds. We evaluate the proposed method in a case study of low SpO2 alarms using a dataset collected at Children's Hospital of Philadelphia and show that our method provides tight and accurate bounds that significantly outperform the naive comparative method.