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Clinical Alarms

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Predict In-Hospital Code Blue Events using Monitor Alarms through Deep Learning Approach.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2018
Bedside monitors in hospital intensive care units (ICUs) are known to produce excessive false alarms that could desensitize caregivers, resulting in delayed or even missed clinical interventions to life-threatening events. Our previous studies propos...
Cardiopulmonary Resuscitation Clinical Alarms Deep Learning Monitoring, Physiologic Pilot Projects
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Applying machine learning to continuously monitored physiological data.

Journal of clinical monitoring and computing Oct 1, 2019
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...
Adult Algorithms Arrhythmias, Cardiac Atrial Fibrillation Clinical Alarms Critical Care Databases, Factual Decision Support Systems, Clinical Delirium Electronic Health Records Expert Systems Humans Intensive Care Units Machine Learning Monitoring, Physiologic Randomized Controlled Trials as Topic Respiration, Artificial Sepsis Software Wavelet Analysis
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Machine learning applied to multi-sensor information to reduce false alarm rate in the ICU.

Journal of clinical monitoring and computing Apr 1, 2020
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...
Clinical Alarms Critical Care Decision Support Techniques Expert Systems False Positive Reactions Humans Intensive Care Units Knowledge Bases Machine Learning Monitoring, Physiologic Pattern Recognition, Automated Retrospective Studies
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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 ...
Adaptation, Psychological Algorithms Artificial Intelligence Clinical Alarms Fatigue Humans Monitoring, Physiologic Reproducibility of Results
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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 Apr 1, 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...
Adenoma Adult Case-Control Studies China Clinical Alarms Colonic Polyps Colonoscopy Colorectal Neoplasms Deep Learning Diagnosis, Computer-Assisted Double-Blind Method Early Diagnosis Female Humans Incidence Male Mass Screening Middle Aged Observer Variation
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Single-modal and multi-modal false arrhythmia alarm reduction using attention-based convolutional and recurrent neural networks.

PloS one Jan 1, 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...
Algorithms Arrhythmias, Cardiac Clinical Alarms False Positive Reactions Humans Intensive Care Units Monitoring, Physiologic Neural Networks, Computer Sensitivity and Specificity Supervised Machine Learning
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Development of an Alarm Algorithm, With Nanotechnology Multimodal Sensor, to Predict Impending Infusion Failure and Improve Safety of Peripheral Intravenous Catheters in Neonates.

Advances in neonatal care : official journal of the National Association of Neonatal Nurses Jun 1, 2020
BACKGROUND: Peripheral intravenous catheters connected to an infusion pump are necessary for the delivery of fluids, nutrition, and medications to hospitalized neonates but are not without complications. These adverse events contribute to hospital-ac...
Artificial Intelligence Catheterization, Peripheral Clinical Alarms Equipment Failure Humans Infant, Newborn Infusion Pumps Infusions, Intravenous Monitoring, Physiologic Nanotechnology Patient Safety
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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 ...
Algorithms Child Clinical Alarms Equipment Design Female Humans Machine Learning Male Nocturnal Enuresis Signal Processing, Computer-Assisted Ultrasonography Urinary Bladder Urination Wearable Electronic Devices
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IoT-based incubator monitoring and machine learning powered alarm predictions.

Technology and health care : official journal of the European Society for Engineering and Medicine Jan 1, 2024
BACKGROUND: Incubators, especially the ones for babies, require continuous monitoring for anomaly detection and taking action when necessary.
Algorithms Clinical Alarms Cloud Computing Humans Humidity Incubators, Infant Infant, Newborn Internet of Things Machine Learning Mobile Applications Monitoring, Physiologic Neural Networks, Computer Temperature Wireless Technology
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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...
Algorithms Clinical Alarms Humans Intensive Care Units Machine Learning Oxygen Saturation
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