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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:

Stroke risk prediction using machine learning: a prospective cohort study of 0.5 million Chinese adults
Matthew Chun, Robert Clarke, Benjamin J. Cairns, et al.
Journal of the American Medical Informatics Association (2021) Vol. 28, Iss. 8, pp. 1719-1727
Open Access | Times Cited: 50

Showing 1-25 of 50 citing articles:

A comparative analysis of machine learning classifiers for stroke prediction: A predictive analytics approach
Nitish Biswas, Khandaker Mohammad Mohi Uddin, Sarreha Tasmin Rikta, et al.
Healthcare Analytics (2022) Vol. 2, pp. 100116-100116
Open Access | Times Cited: 69
Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review
Yue Cai, Yuqing Cai, Liying Tang, et al.
BMC Medicine (2024) Vol. 22, Iss. 1
Open Access | Times Cited: 23
Machine-learning versus traditional approaches for atherosclerotic cardiovascular risk prognostication in primary prevention cohorts: a systematic review and meta-analysis
Weber Liu, Liliana Laranjo, Harry Klimis, et al.
European Heart Journal - Quality of Care and Clinical Outcomes (2023)
Open Access | Times Cited: 33
Innovations in Stroke Identification: A Machine Learning-Based Diagnostic Model Using Neuroimages
Muhammad Asim Saleem, Ashir Javeed, Wasan Akarathanawat, et al.
IEEE Access (2024) Vol. 12, pp. 35754-35764
Open Access | Times Cited: 9
Machine Learning and the Conundrum of Stroke Risk Prediction
Yaacoub Chahine, Matthew J Magoon, Bahetihazi Maidu, et al.
Arrhythmia & Electrophysiology Review (2023) Vol. 12
Open Access | Times Cited: 18
Comparison of ischemic stroke diagnosis models based on machine learning
Wan-Xia Yang, Fangfang Wang, Yunyan Pan, et al.
Frontiers in Neurology (2022) Vol. 13
Open Access | Times Cited: 22
A Systematic Review of Artificial Intelligence Models for Time-to-Event Outcome Applied in Cardiovascular Disease Risk Prediction
Achamyeleh Birhanu Teshale, Htet Lin Htun, Mor Vered, et al.
Journal of Medical Systems (2024) Vol. 48, Iss. 1
Open Access | Times Cited: 4
Deep Learning-Based Quantitative Assessment of Multimodal Features using Lenet Model
T Devi, Ritu Aggarwal, R. Swathiramya, et al.
Journal of Machine and Computing (2025), pp. 374-385
Closed Access
Quotient Complex (QC)-Based Machine Learning for 2D Hybrid Perovskite Design
Chuan-Shen Hu, Rishikanta Mayengbam, Kelin Xia, et al.
Journal of Chemical Information and Modeling (2025)
Closed Access
Machine learning to predict stroke risk from routine hospital data: A systematic review
William Heseltine-Carp, Megan Courtman, Daniel Browning, et al.
International Journal of Medical Informatics (2025), pp. 105811-105811
Open Access
Urban and rural disparities in stroke prediction using machine learning among Chinese older adults
Jingjing Zhu, Luotao Lin, Lei Si, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Utility of single versus sequential measurements of risk factors for prediction of stroke in Chinese adults
Matthew Chun, Robert Clarke, Tingting Zhu, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 23
Important Risk Factors in Patients with Nonvalvular Atrial Fibrillation Taking Dabigatran Using Integrated Machine Learning Scheme—A Post Hoc Analysis
Yung-Chuan Huang, Yu‐Chen Cheng, Mao-Jhen Jhou, et al.
Journal of Personalized Medicine (2022) Vol. 12, Iss. 5, pp. 756-756
Open Access | Times Cited: 17
Machine learning algorithms to predict major adverse cardiovascular events in patients with diabetes
Tadesse Melaku Abegaz, Ahmead Baljoon, Oluwaseun Kilanko, et al.
Computers in Biology and Medicine (2023) Vol. 164, pp. 107289-107289
Closed Access | Times Cited: 10
Predicting graft and patient outcomes following kidney transplantation using interpretable machine learning models
Achille Salaün, Simon Knight, L Wingfield, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 3
Analysis and Prediction of Stroke using Machine Learning Algorithms
V. JalajaJayalakshmi, V. Geetha, Muhammad Ijaz
2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA) (2021), pp. 1-5
Closed Access | Times Cited: 18
Stroke mortality prediction based on ensemble learning and the combination of structured and textual data
Rui Huang, Jundong Liu, Tsz Kin Wan, et al.
Computers in Biology and Medicine (2022) Vol. 155, pp. 106176-106176
Closed Access | Times Cited: 12
Predicting acute and long-term mortality in a cohort of pulmonary embolism patients using machine learning
Wahbi K. El‐Bouri, Alexander Sanders, Gregory Y.H. Lip
European Journal of Internal Medicine (2023) Vol. 118, pp. 42-48
Open Access | Times Cited: 7
Recent advancement in integrating artificial intelligence and information technology with real‐world data for clinical decision‐making in China: A scoping review
Xiwen Liao, Chen Yao, Jun Zhang, et al.
Journal of Evidence-Based Medicine (2023) Vol. 16, Iss. 4, pp. 534-546
Open Access | Times Cited: 7
Stroke risk prediction models: A systematic review and meta-analysis
Osahon Jeffery Asowata, Akinkunmi Paul Okekunle, Muideen T Olaiya, et al.
Journal of the Neurological Sciences (2024) Vol. 460, pp. 122997-122997
Closed Access | Times Cited: 2
Geometric data analysis-based machine learning for two-dimensional perovskite design
Chuan-Shen Hu, Rishikanta Mayengbam, Min-Chun Wu, et al.
Communications Materials (2024) Vol. 5, Iss. 1
Open Access | Times Cited: 2
The most efficient machine learning algorithms in stroke prediction: A systematic review
Farkhondeh Asadi, Milad Rahimi, Amir Hossein Daeechini, et al.
Health Science Reports (2024) Vol. 7, Iss. 10
Open Access | Times Cited: 2
Predicting stroke occurrences: a stacked machine learning approach with feature selection and data preprocessing
Pritam Chakraborty, Anjan Bandyopadhyay, Preeti Ranjan Sahu, et al.
BMC Bioinformatics (2024) Vol. 25, Iss. 1
Open Access | Times Cited: 2
Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning methods
Yuan‐Horng Yan, Ting‐Bin Chen, Chun‐Pai Yang, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 9
Big Data in Stroke: How to Use Big Data to Make the Next Management Decision
Brian Liu, Yuan Luo, Andrew M. Naidech
Neurotherapeutics (2023) Vol. 20, Iss. 3, pp. 744-757
Closed Access | Times Cited: 5
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