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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:

A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy
Tim Smole, Bojan Žunkovič, Matej Pičulin, et al.
Computers in Biology and Medicine (2021) Vol. 135, pp. 104648-104648
Open Access | Times Cited: 43

Showing 1-25 of 43 citing articles:

Machine learning-based approach: global trends, research directions, and regulatory standpoints
Raffaele Pugliese, Stefano Regondi, Riccardo Marini
Data Science and Management (2021) Vol. 4, pp. 19-29
Open Access | Times Cited: 223
Artificial Intelligence-Based Methods for Precision Cardiovascular Medicine
Farida Mohsen, Balqees Al-Saadi, Nima Abdi, et al.
Journal of Personalized Medicine (2023) Vol. 13, Iss. 8, pp. 1268-1268
Open Access | Times Cited: 28
Efficient prediction of coronary artery disease using machine learning algorithms with feature selection techniques
Md. Mehedi Hassan, Sadika Zaman, Md. Mushfiqur Rahman, et al.
Computers & Electrical Engineering (2024) Vol. 115, pp. 109130-109130
Closed Access | Times Cited: 8
A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases
Alejandra Cuevas-Chávez, Yasmín Hernández, Javier Ortiz-Hernández, et al.
Healthcare (2023) Vol. 11, Iss. 16, pp. 2240-2240
Open Access | Times Cited: 20
Heart failure disease prediction and stratification with temporal electronic health records data using patient representation
Liang Ye, Chonghui Guo
Journal of Applied Biomedicine (2023) Vol. 43, Iss. 1, pp. 124-141
Closed Access | Times Cited: 19
Risk factor refinement and ensemble deep learning methods on prediction of heart failure using real healthcare records
Chunjie Zhou, Aihua Hou, Pengfei Dai, et al.
Information Sciences (2023) Vol. 637, pp. 118932-118932
Closed Access | Times Cited: 19
Development and validation of an integrated prognostic model for all-cause mortality in heart failure: a comprehensive analysis combining clinical, electrocardiographic, and echocardiographic parameters
Yahui Li, Jiayu Xu, Xuhui Liu, et al.
BMC Cardiovascular Disorders (2025) Vol. 25, Iss. 1
Open Access
Machine Learning-Based Discrimination of Cardiovascular Outcomes in Patients With Hypertrophic Cardiomyopathy
Tae‐Min Rhee, Yeon-Kyoung Ko, Hyung‐Kwan Kim, et al.
JACC Asia (2024) Vol. 4, Iss. 5, pp. 375-386
Open Access | Times Cited: 4
A review of evaluation approaches for explainable AI with applications in cardiology
Ahmed Salih, Ilaria Boscolo Galazzo, Polyxeni Gkontra, et al.
Artificial Intelligence Review (2024) Vol. 57, Iss. 9
Open Access | Times Cited: 4
Exploring the Current Status of Risk Stratification in Hypertrophic Cardiomyopathy: From Risk Models to Promising Techniques
Alexandros Kasiakogias, Christos Kaskoutis, C. Antoniou, et al.
Journal of Cardiovascular Development and Disease (2025) Vol. 12, Iss. 3, pp. 101-101
Open Access
Artificial Intelligence in Ventricular Arrhythmias and Sudden Death
Lauri Holmström, Zijun Zhang, David Ouyang, et al.
Arrhythmia & Electrophysiology Review (2023) Vol. 12
Open Access | Times Cited: 9
Hypertrophic Cardiomyopathy: Genetic Foundations, Outcomes, Interconnections, and Their Modifiers
Mila Glavaški, Lazar Velicki, Nataša Vučinić
Medicina (2023) Vol. 59, Iss. 8, pp. 1424-1424
Open Access | Times Cited: 9
Machine learning techniques for arrhythmic risk stratification: a review of the literature
Cheuk To Chung, George Bazoukis, Sharen Lee, et al.
International Journal of Arrhythmia (2022) Vol. 23, Iss. 1
Open Access | Times Cited: 13
Estimating the risk of sudden death in hypertrophic cardiomyopathy might be solved by artificial intelligence
Josef Veselka
International Journal of Cardiology (2024) Vol. 402, pp. 131842-131842
Closed Access | Times Cited: 2
Clinical applicability of artificial intelligence for patients with an inherited heart disease: A scoping review
Hidde Bleijendaal, Philip Merio Croon, Marinka D. Oudkerk Pool, et al.
Trends in Cardiovascular Medicine (2022) Vol. 33, Iss. 5, pp. 274-282
Open Access | Times Cited: 12
Prediction of Sudden Cardiac Arrest After Alcohol Septal Ablation for Hypertrophic Obstructive Cardiomyopathy: ASA-SCARRE Risk Score
Josef Veselka, Max Liebregts, Robert Cooper, et al.
The American Journal of Cardiology (2022) Vol. 184, pp. 120-126
Closed Access | Times Cited: 11
Radiomics Analysis Derived From LGE-MRI Predict Sudden Cardiac Death in Participants With Hypertrophic Cardiomyopathy
Jie Wang, Laura Bravo, Jinquan Zhang, et al.
Frontiers in Cardiovascular Medicine (2021) Vol. 8
Open Access | Times Cited: 11
Risk Stratification in Hypertrophic Cardiomyopathy
Monica Ahluwalia, Jacques Kpodonu, Emmanuel Agu
JACC Advances (2023) Vol. 2, Iss. 7, pp. 100562-100562
Open Access | Times Cited: 4
A machine learning approach to differentiate wide QRS tachycardia: distinguishing ventricular tachycardia from supraventricular tachycardia
Zhenzhen Li, Wei Zhao, Yangming Mao, et al.
Journal of Interventional Cardiac Electrophysiology (2024) Vol. 67, Iss. 6, pp. 1391-1398
Closed Access | Times Cited: 1
Applying Artificial Intelligence for Phenotyping of Inherited Arrhythmia Syndromes
Sophie Sigfstead, River Jiang, Robert Avram, et al.
Canadian Journal of Cardiology (2024) Vol. 40, Iss. 10, pp. 1841-1851
Closed Access | Times Cited: 1
Application of Isokinetic Dynamometry Data in Predicting Gait Deviation Index Using Machine Learning in Stroke Patients: A Cross-Sectional Study
Xiaolei Lu, Chenye Qiao, Hujun Wang, et al.
Sensors (2024) Vol. 24, Iss. 22, pp. 7258-7258
Open Access | Times Cited: 1
Artificial intelligence-driven intelligent learning models for identification and prediction of cardioneurological disorders: A comprehensive study
Shahadat Hussain, Shahnawaz Ahmad, Mohammed Wasid
Computers in Biology and Medicine (2024) Vol. 184, pp. 109342-109342
Closed Access | Times Cited: 1
Humans and machines in biomedical knowledge curation: hypertrophic cardiomyopathy molecular mechanisms’ representation
Mila Glavaški, Lazar Velicki
BioData Mining (2021) Vol. 14, Iss. 1
Open Access | Times Cited: 9
Machine learning for spatial stratification of progressive cardiovascular dysfunction in a murine model of type 2 diabetes mellitus
Andrya J. Durr, Anna Korol, Quincy A. Hathaway, et al.
PLoS ONE (2023) Vol. 18, Iss. 5, pp. e0285512-e0285512
Open Access | Times Cited: 3
Machine learning and physical based modeling for cardiac hypertrophy
Bogdan Miličević, Miljan Milošević, Vladimir Simić, et al.
Heliyon (2023) Vol. 9, Iss. 6, pp. e16724-e16724
Open Access | Times Cited: 3
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