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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 Prediction of Hospital Mortality in Patients With Postoperative Sepsis
Ren-qi Yao, Xin Jin, Guowei Wang, et al.
Frontiers in Medicine (2020) Vol. 7
Open Access | Times Cited: 58

Showing 1-25 of 58 citing articles:

Interpretable Machine Learning for Early Prediction of Prognosis in Sepsis: A Discovery and Validation Study
Chang Hu, Lu Li, Weipeng Huang, et al.
Infectious Diseases and Therapy (2022) Vol. 11, Iss. 3, pp. 1117-1132
Open Access | Times Cited: 91
Machine learning in perioperative medicine: a systematic review
Valentina Bellini, Marina Valente, Giorgia Bertorelli, et al.
Journal of Anesthesia Analgesia and Critical Care (2022) Vol. 2, Iss. 1
Open Access | Times Cited: 43
Predicting sepsis in-hospital mortality with machine learning: a multi-center study using clinical and inflammatory biomarkers
Guyu Zhang, Fei Shao, Yuan Wei, et al.
European journal of medical research (2024) Vol. 29, Iss. 1
Open Access | Times Cited: 13
Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study
Sang Won Park, Na Young Yeo, Seonguk Kang, et al.
Journal of Korean Medical Science (2024) Vol. 39, Iss. 5
Open Access | Times Cited: 11
An interpretable machine learning model for predicting in-hospital mortality in ICU patients with ventilator-associated pneumonia
Jian‐Jun Wei, Heshan Cao, Mingling Peng, et al.
PLoS ONE (2025) Vol. 20, Iss. 1, pp. e0316526-e0316526
Open Access | Times Cited: 1
Construction and verification of a nomogram model for the risk of death in sepsis patients
Yanjie Yang, Huiling Zhao, Ling Ge, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 1
Machine Learning Prediction Models for Mechanically Ventilated Patients: Analyses of the MIMIC-III Database
Yibing Zhu, Jin Zhang, Guowei Wang, et al.
Frontiers in Medicine (2021) Vol. 8
Open Access | Times Cited: 55
Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review
Mahanazuddin Syed, Shorabuddin Syed, Kevin W. Sexton, et al.
Informatics (2021) Vol. 8, Iss. 1, pp. 16-16
Open Access | Times Cited: 47
Sepsis prediction in intensive care unit based on genetic feature optimization and stacked deep ensemble learning
Nora El-Rashidy, Tamer Abuhmed, Louai Alarabi, et al.
Neural Computing and Applications (2021) Vol. 34, Iss. 5, pp. 3603-3632
Closed Access | Times Cited: 43
Deep learning-based prediction of in-hospital mortality for sepsis
Yong Li, Liu Zhenzhou
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 7
A predictive model for postoperative adverse outcomes following surgical treatment of acute type A aortic dissection based on machine learning
Linfeng Xie, Yu‐ling Xie, Qingsong Wu, et al.
Journal of Clinical Hypertension (2024) Vol. 26, Iss. 3, pp. 251-261
Open Access | Times Cited: 6
Explainable Machine-Learning Model for Prediction of In-Hospital Mortality in Septic Patients Requiring Intensive Care Unit Readmission
Chang Hu, Lu Li, Yiming Li, et al.
Infectious Diseases and Therapy (2022) Vol. 11, Iss. 4, pp. 1695-1713
Open Access | Times Cited: 24
MGP-AttTCN: An interpretable machine learning model for the prediction of sepsis
Margherita Rosnati, Vincent Fortuin
PLoS ONE (2021) Vol. 16, Iss. 5, pp. e0251248-e0251248
Open Access | Times Cited: 32
Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis
Yan Zhang, Weiwei Xu, Ping Yang, et al.
BMC Medical Informatics and Decision Making (2023) Vol. 23, Iss. 1
Open Access | Times Cited: 12
Developing an Interpretable Machine Learning Model to Predict in-Hospital Mortality in Sepsis Patients: A Retrospective Temporal Validation Study
Shuhe Li, Ruoxu Dou, Xiaodong Song, et al.
Journal of Clinical Medicine (2023) Vol. 12, Iss. 3, pp. 915-915
Open Access | Times Cited: 11
Machine learning prognosis model based on patient-reported outcomes for chronic heart failure patients after discharge
Jing Tian, Jingjing Yan, Gangfei Han, et al.
Health and Quality of Life Outcomes (2023) Vol. 21, Iss. 1
Open Access | Times Cited: 11
Predicting Agitation-Sedation Levels in Intensive Care Unit Patients: Development of an Ensemble Model
Pei-Yu Dai, Pei-Yi Lin, Ruey‐Kai Sheu, et al.
JMIR Medical Informatics (2025) Vol. 13, pp. e63601-e63601
Open Access
Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis
Zhixuan Zeng, Shuo Yao, Jianfei Zheng, et al.
BioData Mining (2021) Vol. 14, Iss. 1
Open Access | Times Cited: 25
Machine Learning Methods for Predicting Long-Term Mortality in Patients After Cardiac Surgery
Yue Yu, Chi Peng, Zhiyuan Zhang, et al.
Frontiers in Cardiovascular Medicine (2022) Vol. 9
Open Access | Times Cited: 17
Exploring a global interpretation mechanism for deep learning networks when predicting sepsis
Ethan A. T. Strickler, Joshua Thomas, Johnson P. Thomas, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 9
A time-incorporated SOFA score-based machine learning model for predicting mortality in critically ill patients: A multicenter, real-world study
Yang Liu, Kun Gao, Hongbin Deng, et al.
International Journal of Medical Informatics (2022) Vol. 163, pp. 104776-104776
Closed Access | Times Cited: 15
Application of machine learning tools: Potential and useful approach for the prediction of type 2 diabetes mellitus based on the gut microbiome profile
Xiao-chun Ge, Aimin Zhang, Lihui Li, et al.
Experimental and Therapeutic Medicine (2022) Vol. 23, Iss. 4
Open Access | Times Cited: 14
Prediction of Sepsis in COVID-19 Using Laboratory Indicators
Guoxing Tang, Ying Luo, Feng Lu, et al.
Frontiers in Cellular and Infection Microbiology (2021) Vol. 10
Open Access | Times Cited: 18
Development and internal validation of a machine-learning-developed model for predicting 1-year mortality after fragility hip fracture
Nitchanant Kitcharanant, Pojchong Chotiyarnwong, Thiraphat Tanphiriyakun, et al.
BMC Geriatrics (2022) Vol. 22, Iss. 1
Open Access | Times Cited: 13
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