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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:

Analyzing and predicting the risk of death in stroke patients using machine learning
Enzhao Zhu, Zhihao Chen, Pu Ai, et al.
Frontiers in Neurology (2023) Vol. 14
Open Access | Times Cited: 22

Showing 22 citing articles:

Heterogeneous treatment effects of Cerebrolysin as an early add-on to reperfusion therapy: post hoc analysis of the CEREHETIS trial
Mikhail N. Kalinin, Д. Р. Хасанова
Frontiers in Pharmacology (2024) Vol. 14
Open Access | Times Cited: 5
Personalized surgical recommendations and quantitative therapeutic insights for patients with metastatic breast cancer: Insights from deep learning
Enzhao Zhu, Linmei Zhang, Jiayi Wang, et al.
Cancer Innovation (2024) Vol. 3, Iss. 3
Open Access | Times Cited: 4
Predicting stroke risk: An effective stroke prediction model based on neural networks
Aakanshi Gupta, Nidhi Mishra, Nishtha Jatana, et al.
Journal of Neurorestoratology (2024), pp. 100156-100156
Open Access | Times Cited: 4
Machine Learning-Based 28-Day Mortality Prediction Model for Elderly Neurocritically Ill Patients
Jia Yuan, Jiong Xiong, Jinfeng Yang, et al.
Computer Methods and Programs in Biomedicine (2025) Vol. 260, pp. 108589-108589
Open Access
Identifying Neurological Disorders using Deep Learning with Biomedical Imaging Techniques
Kingshuk Das Bakshi, Sonia Rani, S. Ramya, et al.
SSRN Electronic Journal (2025)
Closed Access
Risk of bias assessment of post-stroke mortality machine learning predictive models: Systematic review.
Nicole Maria Radley, Ian Soh, Abdelrahman M. Saad, et al.
Journal of Stroke and Cerebrovascular Diseases (2025), pp. 108291-108291
Open Access
Individualized Analysis of Nipple‐Sparing Mastectomy Versus Modified Radical Mastectomy Using Deep Learning
Enzhao Zhu, Linmei Zhang, Pu Ai, et al.
Cancer Innovation (2025) Vol. 4, Iss. 3
Open Access
Machine learning-based prognostication of mortality in stroke patients
Ahmad A. Abujaber, Ibrahem Albalkhi, Yahia Imam, et al.
Heliyon (2024) Vol. 10, Iss. 7, pp. e28869-e28869
Open Access | Times Cited: 4
Predicting mortality in brain stroke patients using neural networks: outcomes analysis in a longitudinal study
Nasrin Someeh, Mani Mirfeizi, Mohammad Asghari Jafarabadi, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 10
Optimizing adjuvant treatment options for patients with glioblastoma
Enzhao Zhu, Jiayi Wang, Weizhong Shi, et al.
Frontiers in Neurology (2024) Vol. 15
Open Access | Times Cited: 2
Utilizing machine learning to facilitate the early diagnosis of posterior circulation stroke
Ahmad A. Abujaber, Yahia Imam, Ibrahem Albalkhi, et al.
BMC Neurology (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 2
Determining individual suitability for neoadjuvant systemic therapy in breast cancer patients through deep learning
Enzhao Zhu, Linmei Zhang, Yixian Liu, et al.
Clinical & Translational Oncology (2024) Vol. 26, Iss. 10, pp. 2584-2593
Closed Access | Times Cited: 1
Machine learning-based prediction of one-year mortality in ischemic stroke patients
Ahmad A. Abujaber, Said Yaseen, Yahia Imam, et al.
Oxford Open Neuroscience (2024) Vol. 3
Open Access | Times Cited: 1
Interpretation and Analysis of Machine Learning Models for Brain Stroke Prediction
Ritesh Kumari, Hitendra Garg
2021 5th International Conference on Information Systems and Computer Networks (ISCON) (2023), pp. 1-6
Closed Access | Times Cited: 3
Optimizing Stroke Mortality Prediction: A Comprehensive Study on Risk Factors Analysis and Hyperparameter Tuning Techniques
Imam Tahyudin, Ades Tikaningsih, Puji Lestari, et al.
TEM Journal (2024), pp. 705-717
Open Access
Personalized chemotherapy selection for patients with triple-negative breast cancer using deep learning
Xinyi Yang, Reshetov Iogr Vladmirovich, Poltavskaya Maria Georgievna, et al.
Frontiers in Medicine (2024) Vol. 11
Open Access
Predicting stroke severity of patients using interpretable machine learning algorithms
Amir Sorayaie Azar, Tahereh Samimi, Ghanbar Tavassoli, et al.
European journal of medical research (2024) Vol. 29, Iss. 1
Open Access
Tailoring nonsurgical therapy for elderly patients with head and neck squamous cell carcinoma: A deep learning-based approach
Yang Li, Qinyu Xiao, Haiqi Chen, et al.
Medicine (2024) Vol. 103, Iss. 37, pp. e39659-e39659
Open Access
CONTEXTUAL AND CLINICAL FACTORS AS EXPLAINERS OF STROKE SEVERITY, RESIDUAL MOTOR IMPAIRMENTS, AND FUNCTIONAL INDEPENDENCE DURING HOSPITALIZATION
Kênia Kiefer Parreiras de Menezes, Aline Alvim Scianni, Patrick Roberto Avelino, et al.
Journal of Stroke and Cerebrovascular Diseases (2024) Vol. 34, Iss. 1, pp. 108154-108154
Open Access
Prediction of stroke-associated hospital-acquired pneumonia: Machine learning approach
Ahmad A. Abujaber, Said Yaseen, Abdulqadir J. Nashwan, et al.
Journal of Stroke and Cerebrovascular Diseases (2024) Vol. 34, Iss. 2, pp. 108200-108200
Open Access
Deep learning to optimize radiotherapy decisions for elderly patients with early-stage breast cancer: a novel approach for personalized treatment
Guangliang Yang, Haiqi Chen, Jinbo Yue
American Journal of Cancer Research (2024) Vol. 14, Iss. 12, pp. 5885-5896
Closed Access
A Novel Approach to Predict Brain Stroke using KNN in Machine Learning
M. Vasumathi Devi, S Harshitha, K. Lakshmi Ramya, et al.
(2023), pp. 1-6
Closed Access
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