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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:

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: 87

Showing 1-25 of 87 citing articles:

Investigation on explainable machine learning models to predict chronic kidney diseases
Samit Kumar Ghosh, Ahsan H. Khandoker
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 15
Prediction of Sepsis Mortality in ICU Patients Using Machine Learning Methods
Jiayi Gao, Yu‐Ying Lu, Negin Ashrafi, et al.
medRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 11
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: 10
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: 8
Application of interpretable machine learning for early prediction of prognosis in acute kidney injury
Chang Hu, Qing Tan, Qinran Zhang, et al.
Computational and Structural Biotechnology Journal (2022) Vol. 20, pp. 2861-2870
Open Access | Times Cited: 28
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
Prediction of sepsis mortality in ICU patients using machine learning methods
Jiayi Gao, Yu‐Ying Lu, Negin Ashrafi, et al.
BMC Medical Informatics and Decision Making (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 7
Explainable artificial intelligence (XAI) for predicting the need for intubation in methanol-poisoned patients: a study comparing deep and machine learning models
Khadijeh Moulaei, Mohammad Reza Afrash, Mohammad Parvin, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
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
Machine learning-based prediction of in-ICU mortality in pneumonia patients
Eun‐Tae Jeon, Hyo Jin Lee, Tae Yun Park, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 15
Explainable machine learning in outcome prediction of high-grade aneurysmal subarachnoid hemorrhage
Lei Shu, Hua Yan, Yanze Wu, et al.
Aging (2024), pp. 4654-4669
Open Access | Times Cited: 5
Prediction of mortality in intensive care unit with short-term heart rate variability: Machine learning-based analysis of the MIMIC-III database
Lexin Huang, Zixuan Dou, Fang Fang, et al.
Computers in Biology and Medicine (2025) Vol. 186, pp. 109635-109635
Closed Access
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
SOAR-ML: Synthetic Optimization and Augmentation for Robust Machine Learning in Oral Cancer Prediction
Akhil Chintalapati, Aparajita Senapati, Siddharth Pal, et al.
Communications in computer and information science (2025), pp. 174-187
Closed Access
Development and Application of an Early Prediction Model for Risk of Bloodstream Infection based on Real-world Study
Xiefei Hu, Shenshen Zhi, Li Yang, et al.
Research Square (Research Square) (2025)
Closed Access
Development and validation of a multivariable Prediction Model for Pre-diabetes and Diabetes using Easily Obtainable Clinical Data
Alan L. Hutchison, Mary E. Rinella, Raghavendra G. Mirmira, et al.
medRxiv (Cold Spring Harbor Laboratory) (2025)
Closed Access
An interpretable machine learning model based on computed tomography radiomics for predicting programmed death ligand 1 expression status in gastric cancer
Lei Dai, Jinxue Yin, Xin Xin, et al.
Cancer Imaging (2025) Vol. 25, Iss. 1
Open Access
Prediction Model of Mortality Risk of Sepsis Patients Based on Stacking Algorithm
文锦 李
Modeling and Simulation (2025) Vol. 14, Iss. 03, pp. 261-269
Closed Access
Early Sepsis Prediction Method Based on Improved LF-Transformer
晓乐 冯
Artificial Intelligence and Robotics Research (2025) Vol. 14, Iss. 02, pp. 332-340
Closed Access
Wrangling Real-World Data: Optimizing Clinical Research Through Factor Selection with LASSO Regression
Kerry A. Howard, Wes Anderson, Jagdeep T. Podichetty, et al.
International Journal of Environmental Research and Public Health (2025) Vol. 22, Iss. 4, pp. 464-464
Open Access
Dynamic, Interpretable, Machine Learning–Based Outcome Prediction as a New Emerging Opportunity in Acute Ischemic Stroke Patient Care: A Proof‐of‐Concept Study
Ivan Petrović, Sava Njegovan, Olivera Tomašević, et al.
Stroke Research and Treatment (2025) Vol. 2025, Iss. 1
Open Access
Harness machine learning for multiple prognoses prediction in sepsis patients: evidence from the MIMIC-IV database
Suzhen Zhang, Hong Ding, Yiming Shen, et al.
BMC Medical Informatics and Decision Making (2025) Vol. 25, Iss. 1
Open Access
Application of an interpretable machine learning method to predict the risk of death during hospitalization in patients with acute myocardial infarction combined with diabetes mellitus
Zhijun Bu, Siyu Bai, Chan Yang, et al.
Acta Cardiologica (2025), pp. 1-18
Closed Access
The Association of Elevated Depression Levels and Life’s Essential 8 on Cardiovascular Health With Predicted Machine Learning Models and Interpretations: Evidence From NHANES 2007–2018
Ze Wu, Pao Xu, Yali Zhai, et al.
Depression and Anxiety (2025) Vol. 2025, Iss. 1
Open Access
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
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