ReviewArrhythmia & electrophysiology review2023
Machine Learning and the Conundrum of Stroke Risk Prediction.
Review in Arrhythmia & electrophysiology review, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic Accuracy of Machine Learning Models in Predicting Functional Outcome of Thrombectomy for Acute Posterior Circulation Artery Occlusion: a Systematic Review and Meta-Analysis.Clinical neuroradiology · 2026Pooled it
- Opportunities and Challenges of Cardiovascular Disease Risk Prediction for Primary Prevention Using Machine Learning and Electronic Health Records: A Systematic Review.Reviews in cardiovascular medicine · 2025Pooled it
- Artificial Intelligence and Emerging Digital Technologies Across the Stroke Continuum: From Risk Prediction to Real-Time Monitoring and Rapid Response.Medicina (Kaunas, Lithuania) · 2026Review
- Left atrial flow and thrombosis risk from 4D CT contrast dynamics by physics-informed neural network and indicator dilution theory.bioRxiv : the preprint server for biology · 2026Article
- Prediction models for incident stroke in the community: a systematic review and meta-analysis of predictive performance.European heart journal. Digital health · 2026Article
- Prediction of recurrent ischemic stroke using machine learning from real-world data.BMC medical research methodology · 2026Article
- Intracardiac thrombus formations despite continuous oral anticoagulation in atrial fibrillation patients undergoing catheter ablation procedures: pilot development of a machine learning prediction model.Frontiers in cardiovascular medicine · 2026Article
- Artificial Intelligence-driven Detection, Mapping, and Personalized Therapy for Atrial Fibrillation.The Journal of innovations in cardiac rhythm management · 2026Review
- A multi-modal data fusion and real-time monitoring on stroke risk prediction using federated learning.PloS one · 2026Article
- Prediction and Classification the Risk of Stroke Patients Using Machine Learning Techniques: A Retrospective Cross-Sectional Study.Health science reports · 2025Article
- Article
- Development of explainable artificial intelligence based machine learning model for predicting 30-day hospital readmission after renal transplantation.BMC nephrology · 2025Article
- Choroidal thickness as predictor of subclinical carotid atherosclerosis in adults with type 1 diabetes.Scientific reports · 2025Article
- Urban and rural disparities in stroke prediction using machine learning among Chinese older adults.Scientific reports · 2025Article
- Super-resolution left ventricular flow and pressure mapping by Navier-Stokes-informed neural networks.Computers in biology and medicine · 2025Article
- Evaluating machine learning models for stroke prediction based on clinical variables.Frontiers in neurology · 2025Article
- Development and validation of an interpretable risk prediction model for perioperative ischemic stroke in noncardiac, nonvascular, and nonneurosurgical patients: a retrospective study.Frontiers in physiology · 2025Article
- Prediction of subjective well-being level in residents of Dali City: Where modern tourism meets traditional ethnic culture.PloS one · 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
- Computational Medicine: What Electrophysiologists Should Know to Stay Ahead of the Curve.Current cardiology reports · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Stroke is a leading cause of death worldwide. With escalating healthcare costs, early non-invasive stroke risk stratification is vital. The current paradigm of stroke risk assessment and mitigation is focused on clinical risk factors and comorbidities. Standard algorithms predict risk using regression-based statistical associations, which, while useful and easy to use, have moderate predictive accuracy. This review summarises recent efforts to deploy machine learning (ML) to predict stroke risk and enrich the understanding of the mechanisms underlying stroke. The surveyed body of literature includes studies comparing ML algorithms with conventional statistical models for predicting cardiovascular disease and, in particular, different stroke subtypes. Another avenue of research explored is ML as a means of enriching multiscale computational modelling, which holds great promise for revealing thrombogenesis mechanisms. Overall, ML offers a new approach to stroke risk stratification that accounts for subtle physiologic variants between patients, potentially leading to more reliable and personalised predictions than standard regression-based statistical associations.
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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.