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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:

Predicting Six-Month Re-Admission Risk in Heart Failure Patients Using Multiple Machine Learning Methods: A Study Based on the Chinese Heart Failure Population Database
Shiyu Chen, Weiwei Hu, Yuhui Yang, et al.
Journal of Clinical Medicine (2023) Vol. 12, Iss. 3, pp. 870-870
Open Access | Times Cited: 18

Showing 18 citing articles:

Machine learning–based 30-day readmission prediction models for patients with heart failure: a systematic review
M Yu, Youn‐Jung Son
European Journal of Cardiovascular Nursing (2024) Vol. 23, Iss. 7, pp. 711-719
Closed Access | Times Cited: 7
Heart disease prediction using machine learning, deep Learning and optimization techniques-A semantic review
Girish S. Bhavekar, Agam Das Goswami, Chafle Pratiksha Vasantrao, et al.
Multimedia Tools and Applications (2024) Vol. 83, Iss. 39, pp. 86895-86922
Closed Access | Times Cited: 5
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
Effect of early readmission on subsequent hospital admissions within 1 year in patients with heart failure: A retrospective cohort study
Meng-yao Liang, Qingqing Yang, Yi Lu, et al.
Medicine (2025) Vol. 104, Iss. 7, pp. e41567-e41567
Open Access
A Comparative Analysis of Hyper-Parameter Optimization Methods for Predicting Heart Failure Outcomes
Qisthi Alhazmi Hidayaturrohman, Eisuke Hanada
Applied Sciences (2025) Vol. 15, Iss. 6, pp. 3393-3393
Open 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
SHAP based predictive modeling for 1 year all-cause readmission risk in elderly heart failure patients: feature selection and model interpretation
Hao Luo, Congyu Xiang, Lang Zeng, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 4
Heart Failure Emergency Readmission Prediction Using Stacking Machine Learning Model
Md. Sohanur Rahman, Hasib Rahman, Johayra Prithula, et al.
Diagnostics (2023) Vol. 13, Iss. 11, pp. 1948-1948
Open Access | Times Cited: 10
Predicting 1 year readmission for heart failure: A comparative study of machine learning and the LACE index
Xuewu Song, Yitong Tong, Feng Xian, et al.
ESC Heart Failure (2024) Vol. 11, Iss. 5, pp. 2648-2660
Open Access | Times Cited: 2
The Efficacy of Machine Learning Models for Predicting the Prognosis of Heart Failure: A Systematic Review and Meta-Analysis
Zhaohui Xu, Yinqin Hu, Xinyi Shao, et al.
Cardiology (2024), pp. 1-19
Closed Access | Times Cited: 1
Impact of Data Pre-Processing Techniques on XGBoost Model Performance for Predicting All-Cause Readmission and Mortality Among Patients with Heart Failure
Qisthi Alhazmi Hidayaturrohman, Eisuke Hanada
BioMedInformatics (2024) Vol. 4, Iss. 4, pp. 2201-2212
Open Access | Times Cited: 1
Optimal Tree Depth in Decision Tree Classifiers for Predicting Heart Failure Mortality
Tsehay Admassu Assegie, Ahmed Elaraby
Healthcraft Frontiers (2023) Vol. 1, Iss. 1, pp. 58-66
Open Access | Times Cited: 3
Predicting heart failure prognosis using deep learning based on FT-transformer
Geun-Ho Kim, Min-Uk Yang, Geun-Hyeong Kim, et al.
(2023), pp. 932-936
Closed Access | Times Cited: 2
Enhancing Clinical Decision-Making: Predicting Readmission Risk in Heart Failure Patients with Machine Learning (Preprint)
Xiangkui Jiang, Bingquan Wang
(2024)
Closed Access
Established machine learning models to predict readmission for elderly patients with ischemic heart disease
Xuewu Song, Feng Xian, Changyu Zhu, et al.
Kardiologia Polska (2024) Vol. 82, Iss. 9, pp. 861-869
Open Access
Enhancing Clinical Decision-Making: Predicting Readmission Risk in Heart Failure Patients with Machine Learning (Preprint)
Xiangkui Jiang, Bingquan Wang
JMIR Medical Informatics (2024) Vol. 12, pp. e58812-e58812
Open Access
A Review of Evaluation Approaches for Explainable AI With Applications in Cardiology
Ahmed Salih, Ilaria Boscolo Galazzo, Polyxeni Gkontra, et al.
(2023)
Open Access | Times Cited: 1
A Review of Evaluation Approaches for Explainable AI With Applications in Cardiology
Ahmed Salih, Ilaria Boscolo Galazzo, Polyxeni Gkontra, et al.
(2023)
Open Access
Analysis of the Larissa Heart Failure Risk Score: Predictive Value in 9207 Patients Hospitalized for Heart Failure from a Single Center
Αndrew Xanthopoulos, John Skoularigis, Αlexandros Briasoulis, et al.
Journal of Personalized Medicine (2023) Vol. 13, Iss. 12, pp. 1721-1721
Open Access
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