ReviewFrontiers in physiology2021
Prediction of Atrial Fibrillation Using Machine Learning: A Review.
Review in Frontiers in physiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06847100 (Development of Artificial Intelligence Models to Predict Intrahospital Atrial Fibrillation and Long-term Coronary Event Recurrence in High-risk Patients), which is not on this map. Cited by 19 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.
Development of Artificial Intelligence Models to Predict Intrahospital Atrial Fibrillation and Long-term Coronary Event Recurrence in High-risk Patients: PerCard Study
Who cites it
19 citing papers in PubMed, 43 citations in OpenAlex.
- Prognostic Modeling of Tricuspid Valve Regurgitation Outcomes Using Machine Learning-Based Survival Analysis.Journal of clinical medicine · 2026Article
- A machine learning model to simplify recognition of patients with atrial fibrillation based on diagnostic codes in Swedish primary health care.BMC medical informatics and decision making · 2026Article
- Predicting Atrial Fibrillation Ablation Outcomes: Machine Learning Model Development and Validation Using a Large Administrative Claims Database.JMIR cardio · 2025Article
- Artificial Intelligence in the Diagnosis and Management of Atrial Fibrillation.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial Intelligence in Atrial Fibrillation: From Early Detection to Precision Therapy.Journal of clinical medicine · 2025Review
- The mCJACC. Advances · 2025Article
- Wearables and Atrial Fibrillation: Advances in Detection, Clinical Impact, Ethical Concerns, and Future Perspectives.Cureus · 2025Review
- Identifying the presence of atrial fibrillation during sinus rhythm using a dual-input mixed neural network with ECG coloring technology.BMC medical research methodology · 2024Article
- A predictive score for atrial fibrillation in poststroke patients.Arquivos de neuro-psiquiatria · 2024Article
- Machine learning-based model to predict composite thromboembolic events among Chinese elderly patients with atrial fibrillation.BMC cardiovascular disorders · 2024Article
- A Novel Approach to Dual Feature Selection of Atrial Fibrillation Based on HC-MFS.Diagnostics (Basel, Switzerland) · 2024Article
- Simple risk scoring using sinus rhythm electrocardiograms predicts the incidence of atrial fibrillation in the general population.Scientific reports · 2024Article
- Fibrinaloid Microclots and Atrial Fibrillation.Biomedicines · 2024Review
- Optimization of Using Multiple Machine Learning Approaches in Atrial Fibrillation Detection Based on a Large-Scale Data Set of 12-Lead Electrocardiograms: Cross-Sectional Study.JMIR formative research · 2024Article
- Early Detection of Atrial Fibrillation in Chronic Obstructive Pulmonary Disease Patients.Medicina (Kaunas, Lithuania) · 2024Review
- Machine Learning to Identify Patients at Risk of Developing New-Onset Atrial Fibrillation after Coronary Artery Bypass.Journal of cardiovascular development and disease · 2023Article
- Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation.Arrhythmia & electrophysiology review · 2023Review
- Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study.Diagnostics (Basel, Switzerland) · 2022Review
- Personalized biomechanical insights in atrial fibrillation: opportunities & challenges.Expert review of cardiovascular therapyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There has been recent immense interest in the use of machine learning techniques in the prediction and screening of atrial fibrillation, a common rhythm disorder present with significant clinical implications primarily related to the risk of ischemic cerebrovascular events and heart failure. Prior to the advent of the application of artificial intelligence in clinical medicine, previous studies have enumerated multiple clinical risk factors that can predict the development of atrial fibrillation. These clinical parameters include previous diagnoses, laboratory data (e.g., cardiac and inflammatory biomarkers, etc.), imaging data (e.g., cardiac computed tomography, cardiac magnetic resonance imaging, echocardiography, etc.), and electrophysiological data. These data are readily available in the electronic health record and can be automatically queried by artificial intelligence algorithms. With the modern computational capabilities afforded by technological advancements in computing and artificial intelligence, we present the current state of machine learning methodologies in the prediction and screening of atrial fibrillation as well as the implications and future direction of this rapidly evolving field.
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What OpenQuestion 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 OpenQuestion graph.