False arrhythmia alarms in intensive care units (ICUs) are a significant challenge, contributing to alarm fatigue and potentially compromising patient safety. Ventricular tachycardia (VT) alarms are particularly difficult to detect accurately due to their complex nature. This paper presents a machine learning approach to reduce false VT alarms using the VTaC dataset, a benchmark dataset of annotated VT alarms from ICU monitors. We extract time-domain and frequency-domain features from waveform data, preprocess the data, and train deep learning models to classify true and false VT alarms. Our results demonstrate high performance, with ROC-AUC scores exceeding 0.96 across various training configurations. This work highlights the potential of machine learning to improve the accuracy of VT alarm detection in clinical settings.

Remote Patient Monitoring (RPM) is an emerging technology paradigm that helps reduce clinician workload by automated monitoring and raising intelligent alarm signals. High sensitivity and intelligent data-processing algorithms used in RPM devices result in frequent false-positive alarms, resulting in alarm fatigue. This study aims to critically review the existing literature to identify the causes of these false-positive alarms and categorize the various interventions used in the literature to eliminate these causes. That act as a catalog and helps in false alarm reduction algorithm design. A step-by-step approach to building an effective alarm signal generator for clinical use has been proposed in this work. Second, the possible causes of false-positive alarms amongst RPM applications were analyzed from the literature. Third, a critical review has been done of the various interventions used in the literature depending on causes and classification based on four major approaches: clinical knowledge, physiological data, medical sensor devices, and clinical environments. A practical clinical alarm strategy could be developed by following our pentagon approach. The first phase of this approach emphasizes identifying the various causes for the high number of false-positive alarms. Future research will focus on developing a false alarm reduction method using data mining.

By now, you've likely heard of AIOps, a technique that promises to inject new levels of efficiency into IT operations with the help of AI and machine learning. But what, exactly, does AIOps mean in practice? Which specific use cases can IT organizations enable or improve with the help of AIOps? Those may be more difficult questions to answer if you have yet to see AIOps at work in your organization. To provide clarity on what AIOps looks like in practice, let's walk through five of the top use cases for AIOps in the modern enterprise.

When it comes to staying on top of security threats, machine learning, unquestionably, must be part of the equation. The volume of data is simply too great to cope without it. But as it's currently being used, ML may be doing more harm than good, particularly when it comes to alarm fatigue. Alarm fatigue is a condition that occurs when an operator is overloaded with alarms; in many of these cases, the majority of alarms turn out to be false positives. With too many alarms to investigate in a limited amount of time–and the knowledge that most of them are false positives–the operator begins to ignore some alarms, which invariably leads to bad outcomes.

From interpreting CT scans to diagnosing eye disease, artificial intelligence is taking on medical tasks once reserved for only highly trained medical specialists -- and in many cases outperforming its human counterparts. Now AI is starting to show up in intensive care units, where hospitals treat their sickest patients. Doctors who have used the new systems say AI may be better at responding to the vast trove of medical data collected from ICU patients -- and may help save patients who are teetering between life and death. "Critical care is essentially this interface between humans and technology," says Peter Laussen, chief of critical care medicine at Toronto's Hospital for Sick Children. "The amount of data streaming from the patient in the ICU is huge," encompassing readings of blood pressure, heartbeat, oxygen levels and other vital signs.