AIMC Topic: Atrial Fibrillation

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Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation.

Scientific reports
Critically ill patients affected by atrial fibrillation are at high risk of adverse events: however, the actual risk stratification models for haemorrhagic and thrombotic events are not validated in a critical care setting. With this paper we aimed t...

ECG data dependency for atrial fibrillation detection based on residual networks.

Scientific reports
Atrial fibrillation (AF) is an arrhythmia that can cause blood clot and may lead to stroke and heart failure. To detect AF, deep learning-based detection algorithms have recently been developed. However, deep learning models were often trained with l...

Redefining β-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis.

Lancet (London, England)
BACKGROUND: Mortality remains unacceptably high in patients with heart failure and reduced left ventricular ejection fraction (LVEF) despite advances in therapeutics. We hypothesised that a novel artificial intelligence approach could better assess m...

A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia.

Sensors (Basel, Switzerland)
Atrial fibrillation (AF) is the most common cardiovascular disease (CVD), and most existing algorithms are usually designed for the diagnosis (i.e., feature classification) or prediction of AF. Artificial intelligence (AI) algorithms integrate the di...

Artificial Intelligence-Enabled ECG to Identify Silent Atrial Fibrillation in Embolic Stroke of Unknown Source.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVES: Embolic strokes of unknown source (ESUS) are common and often suspected to be caused by unrecognized paroxysmal atrial fibrillation (AF). An AI-enabled ECG (AI-ECG) during sinus rhythm has been shown to identify patients with unrecognized...

Prediction of Incident Atrial Fibrillation in Chronic Kidney Disease: The Chronic Renal Insufficiency Cohort Study.

Clinical journal of the American Society of Nephrology : CJASN
BACKGROUND AND OBJECTIVES: Atrial fibrillation (AF) is common in CKD and associated with poor kidney and cardiovascular outcomes. Prediction models developed using novel methods may be useful to identify patients with CKD at highest risk of incident ...

Prediction model for thyrotoxic atrial fibrillation: a retrospective study.

BMC endocrine disorders
BACKGROUND: Thyrotoxic atrial fibrillation (TAF) is a recognized significant complication of hyperthyroidism. Early identification of the individuals predisposed to TAF would improve thyrotoxic patients' management. However, to our knowledge, an inst...

An automated method for detecting atrial fat using convolutional neural network.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
Atrial Fibrillation (A-fib) is a common cardiac rhythm problem in the population these days in which irregular heartbeat leads to blood clots, heart failure, stroke, and other significant clinical complications. Researchers have found that the atrial...