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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:

Phenomapping for the Identification of Hypertensive Patients with the Myocardial Substrate for Heart Failure with Preserved Ejection Fraction
Daniel H. Katz, Rahul C. Deo, Frank G. Aguilar, et al.
Journal of Cardiovascular Translational Research (2017) Vol. 10, Iss. 3, pp. 275-284
Closed Access | Times Cited: 74

Showing 1-25 of 74 citing articles:

Artificial Intelligence in Cardiology
Kipp W. Johnson, Jessica Torres Soto, Benjamin S. Glicksberg, et al.
Journal of the American College of Cardiology (2018) Vol. 71, Iss. 23, pp. 2668-2679
Open Access | Times Cited: 925
Artificial Intelligence in Cardiovascular Imaging
Damini Dey, Piotr J. Slomka, Paul Leeson, et al.
Journal of the American College of Cardiology (2019) Vol. 73, Iss. 11, pp. 1317-1335
Open Access | Times Cited: 498
Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone
Davide Chicco, Giuseppe Jurman
BMC Medical Informatics and Decision Making (2020) Vol. 20, Iss. 1
Open Access | Times Cited: 475
Machine Learning and the Future of Cardiovascular Care
Giorgio Quer, Ramy Arnaout, Michael Henne, et al.
Journal of the American College of Cardiology (2021) Vol. 77, Iss. 3, pp. 300-313
Open Access | Times Cited: 265
Phenomapping of patients with heart failure with preserved ejection fraction using machine learning‐based unsupervised cluster analysis
Matthew W. Segar, Kershaw V. Patel, Colby Ayers, et al.
European Journal of Heart Failure (2019) Vol. 22, Iss. 1, pp. 148-158
Open Access | Times Cited: 211
Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association
Kate Hanneman, David Playford, Damini Dey, et al.
Circulation (2024) Vol. 149, Iss. 6
Open Access | Times Cited: 18
Implementing personalized pathways for cancer follow‐up care in the United States: Proceedings from an American Cancer Society–American Society of Clinical Oncology summit
Catherine M. Alfano, Deborah K. Mayer, Smita Bhatia, et al.
CA A Cancer Journal for Clinicians (2019) Vol. 69, Iss. 3, pp. 234-247
Open Access | Times Cited: 82
Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility
Amitava Banerjee, Suliang Chen, Ghazaleh Fatemifar, et al.
BMC Medicine (2021) Vol. 19, Iss. 1
Open Access | Times Cited: 60
Hypertension and heart failure with preserved ejection fraction: position paper by the European Society of Hypertension
Alexandros Kasiakogias, Enrico Agabiti Rosei, Miguel Camafort, et al.
Journal of Hypertension (2021) Vol. 39, Iss. 8, pp. 1522-1545
Open Access | Times Cited: 58
Artificial Intelligence in Cardiology—A Narrative Review of Current Status
George Koulaouzidis, Tomasz Jadczyk, Dimitris K. Iakovidis, et al.
Journal of Clinical Medicine (2022) Vol. 11, Iss. 13, pp. 3910-3910
Open Access | Times Cited: 54
A cardiologist’s guide to machine learning in cardiovascular disease prognosis prediction
Karl‐Patrik Kresoja, Matthias Unterhuber, Rolf Wachter, et al.
Basic Research in Cardiology (2023) Vol. 118, Iss. 1
Open Access | Times Cited: 40
Artificial intelligence and heart failure: A state‐of‐the‐art review
Muhammad Shahzeb Khan, Muhammad Sameer Arshad, Stephen J. Greene, et al.
European Journal of Heart Failure (2023) Vol. 25, Iss. 9, pp. 1507-1525
Closed Access | Times Cited: 35
Long-term outcomes of phenoclusters in severe tricuspid regurgitation
Vishal N. Rao, Anna Giczewska, Karen Chiswell, et al.
European Heart Journal (2023) Vol. 44, Iss. 21, pp. 1910-1923
Open Access | Times Cited: 27
Precision Medicine for Heart Failure with Preserved Ejection Fraction: An Overview
Sanjiv J. Shah
Journal of Cardiovascular Translational Research (2017) Vol. 10, Iss. 3, pp. 233-244
Open Access | Times Cited: 78
Artificial Intelligence in Cardiovascular Medicine
Karthik Seetharam, Sirish Shrestha, Partho P. Sengupta
Current Treatment Options in Cardiovascular Medicine (2019) Vol. 21, Iss. 5
Open Access | Times Cited: 66
Myocardial Mechanics in Patients With Normal LVEF and Diastolic Dysfunction
Christopher Bianco, Peter Farjo, Yasir Abdul Ghaffar, et al.
JACC. Cardiovascular imaging (2019) Vol. 13, Iss. 1, pp. 258-271
Open Access | Times Cited: 62
AI-Assisted Echocardiographic Prescreening of Heart Failure With Preserved Ejection Fraction on the Basis of Intrabeat Dynamics
Yu-An Chiou, Chung‐Lieh Hung, SHIEN‐FONG LIN
JACC. Cardiovascular imaging (2021) Vol. 14, Iss. 11, pp. 2091-2104
Open Access | Times Cited: 46
Successfully implemented artificial intelligence and machine learning applications in cardiology: State-of-the-art review
Jef Van den Eynde, Mark Lachmann, Karl‐Ludwig Laugwitz, et al.
Trends in Cardiovascular Medicine (2022) Vol. 33, Iss. 5, pp. 265-271
Closed Access | Times Cited: 31
Clinical Phenotypes of Heart Failure With Preserved Ejection Fraction to Select Preclinical Animal Models
Willem B. van Ham, Elise L. Kessler, Marish I.F.J. Oerlemans, et al.
JACC Basic to Translational Science (2022) Vol. 7, Iss. 8, pp. 844-857
Open Access | Times Cited: 29
Strengths and Opportunities of Network Medicine in Cardiovascular Diseases
Giuditta Benincasa, Raffaele Marfella, Nunzia Della Mura, et al.
Circulation Journal (2019) Vol. 84, Iss. 2, pp. 144-152
Open Access | Times Cited: 53
Unsupervised Cluster Analysis of Patients With Aortic Stenosis Reveals Distinct Population With Different Phenotypes and Outcomes
Soongu Kwak, Yunhwan Lee, Taehoon Ko, et al.
Circulation Cardiovascular Imaging (2020) Vol. 13, Iss. 5
Open Access | Times Cited: 46
Machine Intelligence in Cardiovascular Medicine
D. Douglas Miller
Cardiology in Review (2020) Vol. 28, Iss. 2, pp. 53-64
Closed Access | Times Cited: 45
A Primer on the Present State and Future Prospects for Machine Learning and Artificial Intelligence Applications in Cardiology
Cedric Manlhiot, Jef Van den Eynde, Shelby Kutty, et al.
Canadian Journal of Cardiology (2021) Vol. 38, Iss. 2, pp. 169-184
Open Access | Times Cited: 36
A phenomapping-derived tool to personalize the selection of anatomical vs. functional testing in evaluating chest pain (ASSIST)
Evangelos K. Oikonomou, David van Dijk, Helen Parise, et al.
European Heart Journal (2021) Vol. 42, Iss. 26, pp. 2536-2548
Open Access | Times Cited: 34
Predicting in‐hospital mortality among patients admitted with a diagnosis of heart failure: a machine learning approach
Zina Jawadi, Rosemary He, Pratyaksh K. Srivastava, et al.
ESC Heart Failure (2024) Vol. 11, Iss. 5, pp. 2490-2498
Open Access | Times Cited: 6
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