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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:

Machine learning-based in-hospital mortality risk prediction tool for intensive care unit patients with heart failure
Zijun Chen, Tingming Li, Sheng Guo, et al.
Frontiers in Cardiovascular Medicine (2023) Vol. 10
Open Access | Times Cited: 14

Showing 14 citing articles:

Prioritizing Patient Selection in Clinical Trials: A Machine Learning Algorithm for Dynamic Prediction of In-Hospital Mortality for ICU Admitted Patients Using Repeated Measurement Data
Emma Pedarzani, Alberto Fogagnolo, Ileana Baldi, et al.
Journal of Clinical Medicine (2025) Vol. 14, Iss. 2, pp. 612-612
Open Access | Times Cited: 1
Association between red cell distribution width—albumin ratio and all-cause mortality in intensive care unit patients with heart failure
Ni Li, Junling Li, Kai Wang
Frontiers in Cardiovascular Medicine (2025) Vol. 12
Open Access | Times Cited: 1
Predicting hospital admissions, ICU utilization, and prolonged length of stay among febrile pediatric emergency department patients using incomplete and imbalanced electronic health record (EHR) data strategies
Tom Velez, Z. A. Ibrahim, Kanayo Duru, et al.
International Journal of Medical Informatics (2025), pp. 105905-105905
Closed Access
Evaluation of machine learning methods for prediction of heart failure mortality and readmission: meta-analysis
Hamed Hajishah, Danial Kazemi, Ehsan Safaee, et al.
BMC Cardiovascular Disorders (2025) Vol. 25, Iss. 1
Open Access
Independent prognostic importance of endothelial activation and stress index (EASIX) in critically ill patients with heart failure: modulating role of inflammation
Fang Yin, Kai Wang
Frontiers in Medicine (2025) Vol. 12
Open Access
Development, Validation, and Deployment of a Time-Dependent Machine Learning Model for Predicting One-Year Mortality Risk in Critically Ill Patients with Heart Failure
Jiuyi Wang, Qingxia Kang, Shiqi Tian, et al.
Bioengineering (2025) Vol. 12, Iss. 5, pp. 511-511
Open Access
Prediction of 28-day all-cause mortality in heart failure patients with Clostridioides difficile infection using machine learning models: evidence from the MIMIC-IV database
Caiping Shi, Qiong Jie, Hongsong Zhang, et al.
Cardiology (2024), pp. 1-1
Closed Access | Times Cited: 2
Machine learning in heart failure diagnosis, prediction, and prognosis: review
Muhammad Saqib, Prinka Perswani, A. Muneem, et al.
Annals of Medicine and Surgery (2024)
Open Access | Times Cited: 2
Patient Selection and End Point Definitions for Decongestion Studies in Acute Decompensated Heart Failure: Part 1
Gabriel Georges, Marat Fudim, Daniel Burkhoff, et al.
Journal of the Society for Cardiovascular Angiography & Interventions (2023) Vol. 2, Iss. 6, pp. 101060-101060
Open Access | Times Cited: 4
Predictive Analytics in Heart Failure Prediction: A Systematic Review
Qisthi Alhazmi Hidayaturrohman, Eisuke Hanada
(2024)
Open Access | Times Cited: 1
A machine learning-based predictive model for the in-hospital mortality of critically ill patients with atrial fibrillation
Yanting Luo, Ruimin Dong, Jinlai Liu, et al.
International Journal of Medical Informatics (2024) Vol. 191, pp. 105585-105585
Open Access | Times Cited: 1
Predictive Analytics in Heart Failure Risk, Readmission, and Mortality Prediction: A Review
Qisthi Alhazmi Hidayaturrohman, Eisuke Hanada
Cureus (2024)
Open Access | Times Cited: 1
Independent prognostic importance of the albumin-corrected anion gap in critically ill patients with congestive heart failure: a retrospective study from MIMIC-IV database
Ni Li, Junling Li, Kai Wang
BMC Cardiovascular Disorders (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 1
Predicting in-hospital mortality in patients with heart failure combined with atrial fibrillation using stacking ensemble model: an analysis of the medical information mart for intensive care IV (MIMIC-IV)
Panpan Chen, Jun-hua Sun, Yingjie Chu, et al.
BMC Medical Informatics and Decision Making (2024) Vol. 24, Iss. 1
Open Access
Predictive Value of Machine Learning for the Risk of In‐Hospital Death in Patients With Heart Failure: A Systematic Review and Meta‐Analysis
Liyuan Yan, Jinlong Zhang, Le Chen, et al.
Clinical Cardiology (2024) Vol. 48, Iss. 1
Open Access
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