Key result
Multimodal machine learning algorithm effectively suppresses false arrhythmia alarms on validation testing.
Why the study?
False arrhythmia alarms in the ICU are common and require effective suppression methods to reduce alarm fatigue and improve patient monitoring accuracy.
Does a machine learning-based multi-modal detection algorithm effectively suppress false arrhythmia alarms in the ICU?
Does a machine learning-based multi-modal detection algorithm effectively suppress false arrhythmia alarms in the ICU?
A novel machine learning approach combining multi-modal beat detection algorithms effectively suppresses false ventricular tachycardia and fibrillation alarms in the ICU.
May mitigate alarm fatigue in monitored settings; leaves open prospective clinical validation before adoption.
This paper presents a novel approach for false alarm suppression using machine learning tools. It proposes a multi-modal detection algorithm to find the true beats using the information from all the available waveforms. This method uses a variety of beat detection algorithms, some of which are developed by the authors. The outputs of the beat detection algorithms are combined using a machine learning approach. For the ventricular tachycardia and ventricular fibrillation alarms, separate classification models are trained to distinguish between the normal and abnormal beats. This information, along with alarm-specific criteria, is used to decide if the alarm is false. The results indicate that the presented method was effective in suppressing false alarms when it was tested on a hidden validation dataset.
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Ansari et al. (2016) studied False arrhythmia alarms in the ICU. Machine learning-based multi-modal detection algorithm was evaluated on Suppression of false alarms. A machine learning-based multi-modal detection algorithm was effective in suppressing false arrhythmia alarms when tested on a hidden validation dataset.
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