ArticlePloS one2019
Predicting atrial fibrillation in primary care using machine learning.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers, 5 of them syntheses that pooled it.
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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
75 citing papers in PubMed, 5 syntheses or guidelines pooled it, 144 citations in OpenAlex.
- The Use of Machine Learning for Analyzing Real-World Data in Disease Prediction and Management: Systematic Review.JMIR medical informatics · 2025Pooled it
- Pooled it
- Prediction of incident atrial fibrillation in community-based electronic health records: a systematic review with meta-analysis.Heart (British Cardiac Society) · 2022Pooled it
- Pooled it
- Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making.BMC medical informatics and decision making · 2021Pooled it
- Systematic, randomized atrial fibrillation screening using detailed phenotyping with a risk prediction model combined with patch electrocardiogram in a Swedish population aged 65 years or older: the CONSIDERING-AF trial.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 · 2025Trial
- Trial
- Machine learning-based prediction of postoperative atrial fibrillation risk in coronary artery bypass grafting patients.Journal of cardiothoracic surgery · 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
- Short-Term Arrhythmia Prediction Using AI Based on Daily Data From Implantable Devices: Multicenter Prospective Observational Study.JMIR cardio · 2026Observational
- A risk model to predict atrial fibrillation in diabetes using machine learning: The ACCORD study.American heart journal plus : cardiology research and practice · 2026Article
- Machine learning-enhanced prediction model for atrial fibrillation development in patients with concurrent type 2 diabetes and obstructive sleep apnea syndrome: a comorbidity perspective.Nutrition & metabolism · 2025Article
- Are Machine Learning methods effective in detecting undiagnosed atrial fibrillation in primary care settings using electronic health records? A systematic review.PLOS digital health · 2025Article
- Predicting New York Heart Association (NYHA) heart failure classification from medical student notes following simulated patient encounters.Scientific reports · 2025Article
- Artificial Intelligence in Atrial Fibrillation: From Early Detection to Precision Therapy.Journal of clinical medicine · 2025Review
- Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats.Nature communications · 2025Article
- Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis.European heart journal. Digital health · 2025Review
- The Efficacy of Artificial Intelligence in the Detection and Management of Atrial Fibrillation.Cureus · 2025Review
- Optimising test intervals for individuals with type 2 diabetes: A machine learning approach.PloS one · 2025Article
- Research Hotspots and Prospects of Artificial Intelligence in Cardiovascular Disease: A Bibliometric Analysis.Journal of multidisciplinary healthcare · 2025Article
15 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAtrial fibrillation (AF) is the most common sustained heart arrhythmia. However, as many cases are asymptomatic, a large proportion of patients remain undiagnosed until serious complications arise. Efficient, cost-effective detection of the undiagnosed may be supported by risk-prediction models relating patient factors to AF risk. However, there exists a need for an implementable risk model that is contemporaneous and informed by routinely collected patient data, reflecting the real-world pathology of AF.
methodsThis study sought to develop and evaluate novel and conventional statistical and machine learning models for risk-predication of AF. This was a retrospective, cohort study of adults (aged ≥30 years) without a history of AF, listed on the Clinical Practice Research Datalink, from January 2006 to December 2016. Models evaluated included published risk models (Framingham, ARIC, CHARGE-AF), machine learning models, which evaluated baseline and time-updated information (neural network, LASSO, random forests, support vector machines), and Cox regression.
resultsAnalysis of 2,994,837 individuals (3.2% AF) identified time-varying neural networks as the optimal model achieving an AUROC of 0.827 vs. 0.725, with number needed to screen of 9 vs. 13 patients at 75% sensitivity, when compared with the best existing model CHARGE-AF. The optimal model confirmed known baseline risk factors (age, previous cardiovascular disease, antihypertensive medication usage) and identified additional time-varying predictors (proximity of cardiovascular events, body mass index (both levels and changes), pulse pressure, and the frequency of blood pressure measurements).
conclusionThe optimal time-varying machine learning model exhibited greater predictive performance than existing AF risk models and reflected known and new patient risk factors for AF.
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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.