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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:

Explainable machine-learning predictions for the prevention of hypoxaemia during surgery
Scott Lundberg, Bala G. Nair, Monica S. Vavilala, et al.
Nature Biomedical Engineering (2018) Vol. 2, Iss. 10, pp. 749-760
Open Access | Times Cited: 1486

Showing 26-50 of 1486 citing articles:

Deep Learning for Medical Anomaly Detection – A Survey
Tharindu Fernando, Harshala Gammulle, Simon Denman, et al.
ACM Computing Surveys (2021) Vol. 54, Iss. 7, pp. 1-37
Open Access | Times Cited: 208
A Systematic Review of Explainable Artificial Intelligence in Terms of Different Application Domains and Tasks
Mir Riyanul Islam, Mobyen Uddin Ahmed, Shaibal Barua, et al.
Applied Sciences (2022) Vol. 12, Iss. 3, pp. 1353-1353
Open Access | Times Cited: 206
Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications
Bing Xue, Dingwen Li, Chenyang Lu, et al.
JAMA Network Open (2021) Vol. 4, Iss. 3, pp. e212240-e212240
Open Access | Times Cited: 203
Algorithmic fairness in artificial intelligence for medicine and healthcare
Richard J. Chen, Judy J. Wang, Drew F. K. Williamson, et al.
Nature Biomedical Engineering (2023) Vol. 7, Iss. 6, pp. 719-742
Open Access | Times Cited: 189
Interpretable prediction of 3-year all-cause mortality in patients with heart failure caused by coronary heart disease based on machine learning and SHAP
Ke Wang, Jing Tian, Chu Zheng, et al.
Computers in Biology and Medicine (2021) Vol. 137, pp. 104813-104813
Open Access | Times Cited: 172
Deep Learning for Diabetes: A Systematic Review
Taiyu Zhu, Kezhi Li, Pau Herrero, et al.
IEEE Journal of Biomedical and Health Informatics (2020) Vol. 25, Iss. 7, pp. 2744-2757
Open Access | Times Cited: 169
Revealing Drivers of Haze Pollution by Explainable Machine Learning
Linlu Hou, Qili Dai, Congbo Song, et al.
Environmental Science & Technology Letters (2022) Vol. 9, Iss. 2, pp. 112-119
Closed Access | Times Cited: 157
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use
Sana Tonekaboni, Shalmali Joshi, Melissa D. McCradden, et al.
arXiv (Cornell University) (2019)
Open Access | Times Cited: 156
Developing future human-centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges
Kashif Ahmad, Majdi Maabreh, Mohamed Ghaly, et al.
Computer Science Review (2021) Vol. 43, pp. 100452-100452
Closed Access | Times Cited: 155
Artificial intelligence for precision oncology: beyond patient stratification
Francisco Azuaje
npj Precision Oncology (2019) Vol. 3, Iss. 1
Open Access | Times Cited: 150
Physician understanding, explainability, and trust in a hypothetical machine learning risk calculator
William K. Diprose, Nicholas Buist, Ning Hua, et al.
Journal of the American Medical Informatics Association (2019) Vol. 27, Iss. 4, pp. 592-600
Open Access | Times Cited: 147
Toward a unified framework for interpreting machine-learning models in neuroimaging
Lada Kohoutová, Juyeon Heo, Sungmin Cha, et al.
Nature Protocols (2020) Vol. 15, Iss. 4, pp. 1399-1435
Open Access | Times Cited: 144
Improving performance of deep learning models with axiomatic attribution priors and expected gradients
Gabriel Erion, Joseph D. Janizek, Pascal Sturmfels, et al.
Nature Machine Intelligence (2021) Vol. 3, Iss. 7, pp. 620-631
Open Access | Times Cited: 141
Prediction of concrete strengths enabled by missing data imputation and interpretable machine learning
Gideon A. Lyngdoh, Mohd Zaki, N. M. Anoop Krishnan, et al.
Cement and Concrete Composites (2022) Vol. 128, pp. 104414-104414
Open Access | Times Cited: 124
Deep learning the collisional cross sections of the peptide universe from a million experimental values
Florian Meier, Niklas Köhler, Andreas‐David Brunner, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 114
Machine Learning for Early Lung Cancer Identification Using Routine Clinical and Laboratory Data
Michael K. Gould, Brian Huang, Martin C. Tammemägi, et al.
American Journal of Respiratory and Critical Care Medicine (2021) Vol. 204, Iss. 4, pp. 445-453
Closed Access | Times Cited: 108
Forecasting plastic waste generation and interventions for environmental hazard mitigation
Yee Van Fan, Peng Jiang, Raymond R. Tan, et al.
Journal of Hazardous Materials (2021) Vol. 424, pp. 127330-127330
Closed Access | Times Cited: 107
Meteorology-driven variability of air pollution (PM<sub>1</sub>) revealed with explainable machine learning
Roland Stirnberg, Jan Čermák, Simone Kotthaus, et al.
Atmospheric chemistry and physics (2021) Vol. 21, Iss. 5, pp. 3919-3948
Open Access | Times Cited: 106
Machine learning models to accelerate the design of polymeric long-acting injectables
Pauric Bannigan, Zeqing Bao, Riley J. Hickman, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 106
Implementing machine learning in medicine
Amol A. Verma, Joshua Murray, Russell Greiner, et al.
Canadian Medical Association Journal (2021) Vol. 193, Iss. 34, pp. E1351-E1357
Open Access | Times Cited: 103
Evaluation guidelines for machine learning tools in the chemical sciences
Andreas Bender, Nadine Schneider, Marwin Segler, et al.
Nature Reviews Chemistry (2022) Vol. 6, Iss. 6, pp. 428-442
Closed Access | Times Cited: 103
An interpretable probabilistic model for short-term solar power forecasting using natural gradient boosting
Georgios Mitrentsis, Hendrik Lens
Applied Energy (2022) Vol. 309, pp. 118473-118473
Open Access | Times Cited: 101
A deep learning system for predicting time to progression of diabetic retinopathy
Ling Dai, Bin Sheng, Ting‐Li Chen, et al.
Nature Medicine (2024)
Open Access | Times Cited: 93
Artificial Intelligence in Critical Care Medicine
Joo Heung Yoon, Michael R. Pinsky, Gilles Clermont
Critical Care (2022) Vol. 26, Iss. 1
Open Access | Times Cited: 92
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

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