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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:

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: 31

Showing 1-25 of 31 citing articles:

The enlightening role of explainable artificial intelligence in medical & healthcare domains: A systematic literature review
Subhan Ali, Filza Akhlaq, Ali Shariq Imran, et al.
Computers in Biology and Medicine (2023) Vol. 166, pp. 107555-107555
Open Access | Times Cited: 119
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: 18
A Survey on Medical Explainable AI (XAI): Recent Progress, Explainability Approach, Human Interaction and Scoring System
Ruey‐Kai Sheu, Mayuresh Sunil Pardeshi
Sensors (2022) Vol. 22, Iss. 20, pp. 8068-8068
Open Access | Times Cited: 69
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
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
Machine learning algorithms assist early evaluation of enteral nutrition in ICU patients
Ya-Xi Wang, Xun-Liang Li, Linghui Zhang, et al.
Frontiers in Nutrition (2023) Vol. 10
Open Access | Times Cited: 14
Explainable artificial intelligence model for mortality risk prediction in the intensive care unit: a derivation and validation study
Chang Hu, Chao Gao, Tianlong Li, et al.
Postgraduate Medical Journal (2024) Vol. 100, Iss. 1182, pp. 219-227
Closed Access | Times Cited: 5
Artificial intelligence in early detection and prediction of pediatric/neonatal acute kidney injury: current status and future directions
Rupesh Raina, Arwa Nada, Raghav Shah, et al.
Pediatric Nephrology (2023) Vol. 39, Iss. 8, pp. 2309-2324
Closed Access | Times Cited: 12
Association between the triglyceride glucose index and length of hospital stay in patients with heart failure and type 2 diabetes in the intensive care unit: a retrospective cohort study
Kai Zhang, Yu Han, Gao Yu, et al.
Frontiers in Endocrinology (2024) Vol. 15
Open Access | Times Cited: 4
Interpretable machine learning model for early prediction of delirium in elderly patients following intensive care unit admission: a derivation and validation study
Dayu Tang, Chengyong Ma, Yu Xu
Frontiers in Medicine (2024) Vol. 11
Open Access | Times Cited: 4
The future of artificial intelligence in clinical nutrition
Pierre Singer, Eyal Robinson, Orit Raphaeli
Current Opinion in Clinical Nutrition & Metabolic Care (2023) Vol. 27, Iss. 2, pp. 200-206
Closed Access | Times Cited: 10
Novel Systems Based on Artificial Intelligence and Numerical Algorithms for Predicting Laboratory Results: A Comparative Study of Original Automatic Prediction Model with Advances in the Field
Dawid Pawuś, Tomasz Porażko, Szczepan Paszkiel
Lecture notes in networks and systems (2025), pp. 113-131
Closed Access
An interpretable machine learning-assisted diagnostic model for Kawasaki disease in children
Mengyu Duan, Zhimin Geng, Lichao Gao, et al.
Scientific Reports (2025) Vol. 15, 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
Expert system supporting automatic risk classification and management in idiopathic membranous nephropathy based on rule sets and machine learning
Dawid Pawuś, Szczepan Paszkiel, Tomasz Porażko
Biomedical Signal Processing and Control (2025) Vol. 109, pp. 107989-107989
Closed Access
Interpretable machine learning model for predicting acute kidney injury in critically ill patients
Xunliang Li, Peng Wang, Yuke Zhu, et al.
BMC Medical Informatics and Decision Making (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 3
Automation and Decision Support in the Area of Nephrology Using Numerical Algorithms, Artificial Intelligence, and Expert Approach: Review of the Current State of Knowledge
Dawid Pawuś, Tomasz Porażko, Szczepan Paszkiel
IEEE Access (2024) Vol. 12, pp. 86043-86066
Open Access | Times Cited: 3
Bibliometric and visual analysis of machine learning-based research in acute kidney injury worldwide
Xiang Yu, Rilige Wu, Yuwei Ji, et al.
Frontiers in Public Health (2023) Vol. 11
Open Access | Times Cited: 6
The Possible Effect of Dietary Fiber Intake on the Metabolic Patterns of Dyslipidemia Subjects: Cross-Sectional Research Using Nontargeted Metabolomics
Youngmin Han, Kyunghye Jang, Unchong Kim, et al.
Journal of Nutrition (2023) Vol. 153, Iss. 9, pp. 2552-2560
Closed Access | Times Cited: 6
Detection of potential drug-drug interactions for risk of acute kidney injury: a population-based case-control study using interpretable machine-learning models
Hayato Akimoto, T. Hayakawa, Takuya Nagashima, et al.
Frontiers in Pharmacology (2023) Vol. 14
Open Access | Times Cited: 5
A pattern mixture model with long short-term memory network for acute kidney injury prediction
Fathima Begum M, Subhashini Narayan
Journal of King Saud University - Computer and Information Sciences (2023) Vol. 35, Iss. 4, pp. 172-182
Open Access | Times Cited: 3
Prediction of acute kidney injury in patients with liver cirrhosis using machine learning models: evidence from the MIMIC-III and MIMIC-IV
Jia Tian, Rui Cui, Huinan Song, et al.
International Urology and Nephrology (2023) Vol. 56, Iss. 1, pp. 237-247
Closed Access | Times Cited: 3
Gastrointestinal failure, big data and intensive care
Pierre Singer, Eyal Robinson, Orit Raphaeli
Current Opinion in Clinical Nutrition & Metabolic Care (2023) Vol. 26, Iss. 5, pp. 476-481
Closed Access | Times Cited: 3
Predictive modeling of perioperative blood transfusion in lumbar posterior interbody fusion using machine learning
Fang-Fang Lang, Liying Liu, Shaowei Wang
Frontiers in Physiology (2023) Vol. 14
Open Access | Times Cited: 3
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