ArticleScientific reports2024
Machine learning for outcome prediction in patients with non-valvular atrial fibrillation from the GLORIA-AF registry.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- AI-Driven Atrial Fibrillation Management: From Signal to Strategy.Balkan medical journal · 2026Review
- Establishing a Diagnostic Model Based on Plasma CDR1as and Alpha-Synuclein for Mild Cognitive Impairment in Type 2 Diabetes.Molecular neurobiology · 2026Article
- Comparative study of 11 machine learning algorithms for predicting recurrence risk after atrial fibrillation catheter ablation based on a real-world cohort: a retrospective study.BMC cardiovascular disorders · 2026Article
- Individualized Risk Stratification for Early Recurrence of Hepatocellular Carcinoma: A Clinical Tool Derived from a Large-Scale (n=3084) Cohort Study.Journal of hepatocellular carcinoma · 2026Article
- Predicting Incident Atrial Fibrillation after Stroke: A Scoping Review of Clinical Scores, Biomarkers, and AI-Enhanced Strategies.Cerebrovascular diseases extra · 2026Article
- Machine Learning and Arrhythmia: Advances in Atrial Fibrillation Detection and Management.Current atherosclerosis reports · 2025Review
- Artificial Intelligence in the Diagnosis and Management of Atrial Fibrillation.Diagnostics (Basel, Switzerland) · 2025Review
- Federated multimodal AI for precision-equitable diabetes care.Frontiers in digital health · 2025Review
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clinical risk scores that predict outcomes in patients with atrial fibrillation (AF) have modest predictive value. Machine learning (ML) may achieve greater results when predicting adverse outcomes in patients with recently diagnosed AF. Several ML models were tested and compared with current clinical risk scores on a cohort of 26,183 patients (mean age 70.13 (standard deviation 10.13); 44.8% female) with non-valvular AF. Inputted into the ML models were 23 demographic variables alongside comorbidities and current treatments. For one-year stroke prediction, ML achieved an area under the curve (AUC) of 0.653 (95% confidence interval 0.576-0.730), compared to the CHADS
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