ReviewJRSM cardiovascular disease
Clinical phenotypes of atrial fibrillation: A review of machine learning applications in personalized treatment.
Review in JRSM cardiovascular disease. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Prognostic Value of Right Ventricular Performance and Left Atrial Mechanical Efficiency in Paroxysmal Atrial Fibrillation.Journal of cardiovascular development and disease · 2026Article
- Multi-Omics and Artificial Intelligence in Cardiovascular Medicine: From Mechanistic Insights to Clinical Translation.Biomedicines · 2026Review
- The Obesity-OSA-Arrhythmia Axis: Pathophysiological Mechanisms and Translational Therapeutic Targets.Life (Basel, Switzerland) · 2026Review
- A paradigm shift toward full-cycle management of atrial fibrillation: integrating digital twins and artificial intelligence.Frontiers in cardiovascular medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Atrial fibrillation (AF) is a clinically heterogeneous syndrome where traditional 'one-size-fits-all' management strategies are often suboptimal. This review synthesizes the contemporary application of machine learning (ML) and deep learning (DL) in identifying distinct clinical AF phenotypes to advance personalized treatment. We provide a comprehensive overview of over a dozen key phenotyping studies, highlighting the consistent identification of core patient subgroups across diverse international cohorts, including low-risk/younger, atherosclerotic/high-cardiovascular-risk, and elderly/multi-morbid phenotypes. A detailed comparative summary of these studies, their methodologies, and their prognostic findings is presented. Our review also illustrates how these data-driven phenotypes are being leveraged to guide personalized therapy. We detail specific ML applications in optimizing medication selection and dosing, particularly for anticoagulants, and in advancing catheter ablation strategies. Key innovations in ablation include AI-guided anatomical mapping,
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.