AIMC Topic: Clinical Alarms
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False Crisis Alarms in Cardiopulmonary Monitoring:: Identification, Causes, and Clinical Implications.
Critical care nursing clinics of North America
Mar 26, 2025
The systematic annotation of crisis alarms reveals a high number of false alarms for both ventricular tachycardia and asystole, which are best identified by inspecting simultaneous multilead electrocardiographs. Among the few true crisis alarms, 11 w...
Enabling machine learning models in alarm fatigue research: Creation of a large relevance-annotated oxygen saturation alarm data set.
Computers in biology and medicine
Oct 24, 2024
BACKGROUND: Too many unnecessary alarms in the intensive care unit are one of the main reasons for alarm fatigue: Medical staff is overburdened and fails to respond appropriately. This endangers both patients and staff. Currently, there are no algori...
A Pre-Voiding Alarm System Using Wearable Ultrasound and Machine Learning Algorithms for Children With Nocturnal Enuresis.
IEEE journal of translational engineering in health and medicine
Sep 10, 2024
Nocturnal enuresis is a bothersome condition that affects many children and their caregivers. Post-voiding systems is of little value in training a child into a correct voiding routing while existing pre-voiding systems suffer from several practical ...
Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study.
The lancet. Gastroenterology & hepatology
Jan 22, 2020
BACKGROUND: Colonoscopy with computer-aided detection (CADe) has been shown in non-blinded trials to improve detection of colon polyps and adenomas by providing visual alarms during the procedure. We aimed to assess the effectiveness of a CADe system...
Single-modal and multi-modal false arrhythmia alarm reduction using attention-based convolutional and recurrent neural networks.
PloS one
Jan 10, 2020
This study proposes a deep learning model that effectively suppresses the false alarms in the intensive care units (ICUs) without ignoring the true alarms using single- and multi- modal biosignals. Most of the current work in the literature are eithe...
Artificial Intelligence Technologies for Coping with Alarm Fatigue in Hospital Environments Because of Sensory Overload: Algorithm Development and Validation.
Journal of medical Internet research
Nov 26, 2019
BACKGROUND: Informed estimates claim that 80% to 99% of alarms set off in hospital units are false or clinically insignificant, representing a cacophony of sounds that do not present a real danger to patients. These false alarms can lead to an alert ...
Machine learning applied to multi-sensor information to reduce false alarm rate in the ICU.
Journal of clinical monitoring and computing
Apr 6, 2019
Studies reveal that the false alarm rate (FAR) demonstrated by intensive care unit (ICU) vital signs monitors ranges from 0.72 to 0.99. We applied machine learning (ML) to ICU multi-sensor information to imitate a medical specialist in diagnosing pat...
Applying machine learning to continuously monitored physiological data.
Journal of clinical monitoring and computing
Nov 11, 2018
The use of machine learning (ML) in healthcare has enormous potential for improving disease detection, clinical decision support, and workflow efficiencies. In this commentary, we review published and potential applications for the use of ML for moni...
Delirium
Arrhythmias, Cardiac
Atrial Fibrillation
Critical Care
Expert Systems
Wavelet Analysis
Clinical Alarms
Sepsis
Decision Support Systems, Clinical
Monitoring, Physiologic
Randomized Controlled Trials as Topic
Software
Electronic Health Records
Algorithms
Humans
Adult
Databases, Factual
Machine Learning
Respiration, Artificial
Intensive Care Units
Monitoring significant ST changes through deep learning.
Journal of electrocardiology
Aug 1, 2018
Internet of Health Things: Toward intelligent vital signs monitoring in hospital wards.
Artificial intelligence in medicine
Jun 2, 2018
BACKGROUND: Large amounts of patient data are routinely manually collected in hospitals by using standalone medical devices, including vital signs. Such data is sometimes stored in spreadsheets, not forming part of patients' electronic health records...
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