Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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 Mortality in Intensive Care Unit Patients With Heart Failure Using an Interpretable Machine Learning Model: Retrospective Cohort Study
Jili Li, Siru Liu, Yundi Hu, et al.
Journal of Medical Internet Research (2022) Vol. 24, Iss. 8, pp. e38082-e38082
Open Access | Times Cited: 79

Showing 1-25 of 79 citing articles:

Interpretable machine learning model for new-onset atrial fibrillation prediction in critically ill patients: a multi-center study
Chengjian Guan, A. Gong, Yan Zhao, et al.
Critical Care (2024) Vol. 28, Iss. 1
Open Access | Times Cited: 13
Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease
Xunliang Li, Zhijuan Wang, Wenman Zhao, et al.
Renal Failure (2024) Vol. 46, Iss. 1
Open Access | Times Cited: 9
Prediction of acute kidney injury in intensive care unit patients based on interpretable machine learning
Li Zhang, Mingyu Li, Chengcheng Wang, et al.
Digital Health (2025) Vol. 11
Open Access | Times Cited: 1
An explainable multi-objective hybrid machine learning model for reducing heart failure mortality
F. M. Javed Mehedi Shamrat, Majdi Khalid, Thamir M. Qadah, et al.
PeerJ Computer Science (2025) Vol. 11, pp. e2682-e2682
Open Access | Times Cited: 1
Improving the precision prognostic evaluation of hospital outcome for heart failure: a multimodal deep learning model leveraging admission notes and clinical tabular data (Preprint)
Zhenyue Gao, Xiaoli Liu, Yu Kang, et al.
Journal of Medical Internet Research (2024) Vol. 26, pp. e54363-e54363
Open Access | Times Cited: 8
Interpretable machine learning framework to predict gout associated with dietary fiber and triglyceride-glucose index
Shunshun Cao, Yangyang Hu
Nutrition & Metabolism (2024) Vol. 21, Iss. 1
Open Access | Times Cited: 7
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
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: 6
Machine learning for predicting in-hospital mortality in elderly patients with heart failure combined with hypertension: a multicenter retrospective study
Xiaozhu Liu, Zulong Xie, Yang Zhang, et al.
Cardiovascular Diabetology (2024) Vol. 23, Iss. 1
Open Access | Times Cited: 6
Prediction model for spinal cord injury in spinal tuberculosis patients using multiple machine learning algorithms: a multicentric study
Sitan Feng, Shujiang Wang, Chong Liu, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 5
Prediction of In-Hospital Mortality for ICU Patients with Heart Failure
Jiahong Zhang, Hexin Li, Negin Ashrafi, et al.
medRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 5
Development and validation of machine learning models to predict MDRO colonization or infection on ICU admission by using electronic health record data
Yun Li, Yuan Yuan Cao, Min Wang, et al.
Antimicrobial Resistance and Infection Control (2024) Vol. 13, Iss. 1
Open Access | Times Cited: 5
Development and comparison of machine learning-based models for predicting heart failure after acute myocardial infarction
Xuewen Li, Chengming Shang, Changyan Xu, et al.
BMC Medical Informatics and Decision Making (2023) Vol. 23, Iss. 1
Open Access | Times Cited: 12
Predicting Colorectal Cancer Survival Using Time-to-Event Machine Learning: Retrospective Cohort Study
Xulin Yang, Hang Qiu, Liya Wang, et al.
Journal of Medical Internet Research (2023) Vol. 25, pp. e44417-e44417
Open Access | Times Cited: 11
Development and Validation of an Explainable Deep Learning Model to Predict In-Hospital Mortality for Patients With Acute Myocardial Infarction: Algorithm Development and Validation Study
Puguang Xie, H Wang, Jun Xiao, et al.
Journal of Medical Internet Research (2024) Vol. 26, pp. e49848-e49848
Open Access | Times Cited: 4
Auxiliary Diagnosis of Children with Attention-Deficit/Hyperactivity Disorder: An Eye-Tracking Study with Novel Digital Biomarkers (Preprint)
Zhongling Liu, Jinkai Li, Yuanyuan Zhang, et al.
JMIR mhealth and uhealth (2024)
Open Access | Times Cited: 4
Application of machine learning algorithms in an epidemiologic study of mortality
George O. Agogo, Henry Mwambi
Annals of Epidemiology (2025)
Closed Access
Enhancing stroke disease classification through machine learning models via a novel voting system by feature selection techniques
Mahade Hasan, Farhana Yasmin, Md. Mehedi Hassan, et al.
PLoS ONE (2025) Vol. 20, Iss. 1, pp. e0312914-e0312914
Open Access
Mesenteric and Anti-Mesenteric Border Sub-Regionalization Using MR Enterography: Advancing Fibrosis Evaluation in Crohn's Disease
Xuehua Li, Luyao Wu, Weicheng Zheng, et al.
(2025)
Closed Access
Peripheral blood microbiome signature and Mycobacterium tuberculosis-derived rsRNA as diagnostic biomarkers for tuberculosis in human
Wei Gu, Zhigang Huang, Yunfan Fan, et al.
Journal of Translational Medicine (2025) Vol. 23, Iss. 1
Open Access
Advances in rhabdomyolysis: A review of pathogenesis, diagnosis, and treatment
Bofan Yang, Duo Li, C. Liu, et al.
Chinese Journal of Traumatology (2025)
Open Access
Evaluation of a novel ensemble model for preoperative ovarian cancer diagnosis: Clinical factors, O-RADS, and deep learning radiomics
Yimin Wu, Lifang Fan, Haixin Shao, et al.
Translational Oncology (2025) Vol. 54, pp. 102335-102335
Closed Access
Evaluation of factors associated with adult skeletal fluorosis in coal-burning type of endemic fluorosis and initial screening model based on machine learning in Guizhou, Southwest China
H Y Shi, You Meng, X. Li, et al.
Ecotoxicology and Environmental Safety (2025) Vol. 293, pp. 118018-118018
Open Access
Identification of DYRK2 and TRIM32 as keloids programmed cell death-related biomarkers: insights from bioinformatics and machine learning in multiple cohorts
Xi Yang, Yang Yao, Mingjian Zhao, et al.
Computer Methods in Biomechanics & Biomedical Engineering (2025), pp. 1-15
Closed Access
Interpretive machine learning predicts short-term mortality risk in elderly sepsis patients
Xingyu Zhu, Zefei Jiang, Li Xiao, et al.
Frontiers in Physiology (2025) Vol. 16
Open Access
Page 1 - Next Page

Scroll to top