SynthesisJournal of anesthesia2024
Machine learning in the prediction and detection of new-onset atrial fibrillation in ICU: a systematic review.
Synthesis in Journal of anesthesia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 11 citations in OpenAlex.
- Clinical subtypes and prognosis of new-onset atrial fibrillation in critically ill patients.International journal of cardiology. Cardiovascular risk and prevention · 2026Article
- Machine-learning models for new-onset atrial fibrillation in critical care: clarity, calibration, and confirmation.Journal of anesthesia · 2026Article
- Clinician Performance in Training Data Curation for an Arrhythmia Machine Learning Model: Is Anyone Qualified?CJC pediatric and congenital heart disease · 2026Article
- ScaHybNet: a scalogram-based hybrid ensemble network for ECG arrhythmia classification.Scientific reports · 2026Article
- Circulating miR-10b-5p as a candidate biomarker of atrial fibrillation recurrence after catheter ablation: a two-phase translational study.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2026Article
- Development of an interpretable machine learning model for predicting new-onset atrial fibrillation in patients with sepsis-associated acute kidney injury: A retrospective cohort study.Science progress · 2026Article
- Artificial Intelligence in Intensive Care: An Overview of Systematic Reviews with Clinical Maturity and Readiness Mapping.Journal of clinical medicine · 2025Review
- Machine learning algorithms to predict the risk of admission to intensive care units in HIV-infected individuals: a single-centre study.Virology journal · 2025Article
- Advancing cardiac diagnostics: high-accuracy arrhythmia classification with the EGOLF-net model.Frontiers in physiology · 2025Article
- A nomogram for predicting CRT response based on multi-parameter features.BMC cardiovascular disorders · 2024Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Atrial fibrillation (AF) stands as the predominant arrhythmia observed in ICU patients. Nevertheless, the absence of a swift and precise method for prediction and detection poses a challenge. This study aims to provide a comprehensive literature review on the application of machine learning (ML) algorithms for predicting and detecting new-onset atrial fibrillation (NOAF) in ICU-treated patients. Following the PRISMA recommendations, this systematic review outlines ML models employed in the prediction and detection of NOAF in ICU patients and compares the ML-based approach with clinical-based methods. Inclusion criteria comprised randomized controlled trials (RCTs), observational studies, cohort studies, and case-control studies. A total of five articles published between November 2020 and April 2023 were identified and reviewed to extract the algorithms and performance metrics. Reviewed studies sourced 108,724 ICU admission records form databases, e.g., MIMIC. Eight prediction and detection methods were examined. Notably, CatBoost exhibited superior performance in NOAF prediction, while the support vector machine excelled in NOAF detection. Machine learning algorithms emerge as promising tools for predicting and detecting NOAF in ICU patients. The incorporation of these algorithms in clinical practice has the potential to enhance decision-making and the overall management of NOAF in ICU settings.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.