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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Prediction of incident atrial fibrillation in post-stroke patients using machine learning: a French nationwide study
Arnaud Bisson, Yassine Lemrini, Wahbi K. El‐Bouri, et al.
Clinical Research in Cardiology (2022) Vol. 112, Iss. 6, pp. 815-823
Closed Access | Times Cited: 12

Showing 12 citing articles:

Prediction of atrial fibrillation and stroke using machine learning models in UK Biobank
Areti Papadopoulou, Daniel Harding, Greg Slabaugh, et al.
Heliyon (2024) Vol. 10, Iss. 7, pp. e28034-e28034
Open Access | Times Cited: 6
The Efficacy of Artificial Intelligence in the Detection and Management of Atrial Fibrillation
Apurva Popat, Sweta Yadav, Jacob Obholz, et al.
Cureus (2025)
Open Access
Clinical applications of artificial intelligence and machine learning in neurocardiology: a comprehensive review
Jade Basem, Racheed Mani, Scott Sun, et al.
Frontiers in Cardiovascular Medicine (2025) Vol. 12
Open Access
The role of artificial intelligence in optimizing management of atrial fibrillation in acute ischemic stroke
Bill Goh, Sonu Bhaskar
Annals of the New York Academy of Sciences (2024) Vol. 1541, Iss. 1, pp. 24-36
Open Access | Times Cited: 3
Evaluating Machine Learning Models for Stroke Prognosis and Prediction in Atrial Fibrillation Patients: A Comprehensive Meta-Analysis
Bill Goh, Sonu Bhaskar
Diagnostics (2024) Vol. 14, Iss. 21, pp. 2391-2391
Open Access | Times Cited: 2
2024 ACC Expert Consensus Decision Pathway on Practical Approaches for Arrhythmia Monitoring After Stroke
Michael Spooner, Steven R. Messé, Seemant Chaturvedi, et al.
Journal of the American College of Cardiology (2024)
Closed Access | Times Cited: 1
Prediction of early death after atrial fibrillation diagnosis using a machine learning approach: A French nationwide cohort study
Arnaud Bisson, Yassine Lemrini, Giulio Francesco Romiti, et al.
American Heart Journal (2023) Vol. 265, pp. 191-202
Closed Access | Times Cited: 3
Multi-Modality Machine Learning Models to Predict Stroke and Atrial Fibrillation in Patients with Heart Failure
Jiandong Zhou, Lakshmi Murugappan, Lei Lü, et al.
medRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 2
Machine learning for stroke in heart failure with reduced ejection fraction but without atrial fibrillation: A post‐hoc analysis of the WARCEF trial
Hironori Ishiguchi, Yang Chen, Bi Huang, et al.
European Journal of Clinical Investigation (2024)
Open Access
A decision-making approach under uncertainty based on ensemble learning model with multimodal data and its application in medical diagnosis
Xixuan Zhao, Bingzhen Sun, Xiaoli Chu, et al.
Expert Systems with Applications (2024), pp. 125983-125983
Closed Access
Performance of risk models to predict mortality risk for patients with heart failure: evaluation in an integrated health system
Faraz S. Ahmad, Ted Ling Hu, Eric Adler, et al.
Clinical Research in Cardiology (2024) Vol. 113, Iss. 9, pp. 1343-1354
Open Access
Research Progress of Neural Network in Electrocardiographic Diagnosis of Atrial Fibrillation
文海 石
Advances in Clinical Medicine (2023) Vol. 13, Iss. 04, pp. 6712-6721
Closed Access
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