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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:

Clinical features of COVID-19 mortality: development and validation of a clinical prediction model
Arjun Singh Yadaw, Yan Chak Li, Sonali Bose, et al.
The Lancet Digital Health (2020) Vol. 2, Iss. 10, pp. e516-e525
Open Access | Times Cited: 277

Showing 26-50 of 277 citing articles:

Comparison of the First and Second Waves of the Coronavirus Disease 2019 Pandemic in Children and Adolescents in a Middle-Income Country: Clinical Impact Associated with Severe Acute Respiratory Syndrome Coronavirus 2 Gamma Lineage
Eduardo A. Oliveira, Ana Cristina Simões e Silva, Maria Christina L. Oliveira, et al.
The Journal of Pediatrics (2022) Vol. 244, pp. 178-185.e3
Open Access | Times Cited: 31
A comparison of machine learning algorithms in predicting COVID-19 prognostics
Serpil Üstebay, Abdurrahman Sarmış, Gülsüm Kübra Kaya, et al.
Internal and Emergency Medicine (2022) Vol. 18, Iss. 1, pp. 229-239
Open Access | Times Cited: 31
Application of Machine Learning to Predict COVID-19 Spread via an Optimized BPSO Model
Eman H. Alkhammash, Sara A. Assiri, Dalal Nemenqani, et al.
Biomimetics (2023) Vol. 8, Iss. 6, pp. 457-457
Open Access | Times Cited: 23
The PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19
Juan Torres‐Macho, Pablo Ryan, Jorge Valencia, et al.
Journal of Clinical Medicine (2020) Vol. 9, Iss. 10, pp. 3066-3066
Open Access | Times Cited: 45
Development of a Web-Based Ensemble Machine Learning Application to Select the Optimal Size of Posterior Chamber Phakic Intraocular Lens
Eun Min Kang, Ik Hee Ryu, Geunyoung Lee, et al.
Translational Vision Science & Technology (2021) Vol. 10, Iss. 6, pp. 5-5
Open Access | Times Cited: 35
Comparison of host endothelial, epithelial and inflammatory response in ICU patients with and without COVID-19: a prospective observational cohort study
Pavan K. Bhatraju, Eric D. Morrell, Leila R. Zelnick, et al.
Critical Care (2021) Vol. 25, Iss. 1
Open Access | Times Cited: 34
Prediction of in‐hospital mortality with machine learning for COVID‐19 patients treated with steroid and remdesivir
Toshiki Kuno, Yuki Sahashi, Shinpei Kawahito, et al.
Journal of Medical Virology (2021) Vol. 94, Iss. 3, pp. 958-964
Open Access | Times Cited: 34
Predicting in-Hospital Mortality of Patients with COVID-19 Using Machine Learning Techniques
Fabiana Tezza, Giulia Lorenzoni, Danila Azzolina, et al.
Journal of Personalized Medicine (2021) Vol. 11, Iss. 5, pp. 343-343
Open Access | Times Cited: 33
Prediction models for severe manifestations and mortality due to COVID‐19: A systematic review
Jamie Miller, Masafumi Tada, Michihiko Goto, et al.
Academic Emergency Medicine (2022) Vol. 29, Iss. 2, pp. 206-216
Open Access | Times Cited: 27
Comparing machine learning algorithms to predict COVID‑19 mortality using a dataset including chest computed tomography severity score data
Seyed Salman Zakariaee, Negar Naderi, Mahdi Ebrahimi, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 15
Effect of increasing doses of colchicine on the treatment of 333 COVID‐19 inpatients
Rumen Tiholov, A Lilov, Gergana Georgieva, et al.
Immunity Inflammation and Disease (2024) Vol. 12, Iss. 5
Open Access | Times Cited: 6
Explainable multimodal prediction of treatment-resistance in patients with depression leveraging brain morphometry and natural language processing
Dong Yun Lee, Narae Kim, ChulHyoung Park, et al.
Psychiatry Research (2024) Vol. 334, pp. 115817-115817
Open Access | Times Cited: 5
Early predictors of mortality for moderate to severely ill patients with Covid-19
Gökhan Aksel, Mehmet Muzaffer İslam, Abdullah Algın, et al.
The American Journal of Emergency Medicine (2020) Vol. 45, pp. 290-296
Open Access | Times Cited: 33
Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients
Irfan Ullah Khan, Nida Aslam, Malak Aljabri, et al.
International Journal of Environmental Research and Public Health (2021) Vol. 18, Iss. 12, pp. 6429-6429
Open Access | Times Cited: 31
A new COVID-19 intubation prediction strategy using an intelligent feature selection and K-NN method
Zahra Asghari Varzaneh, Azam Orooji, Leila Erfannia, et al.
Informatics in Medicine Unlocked (2021) Vol. 28, pp. 100825-100825
Open Access | Times Cited: 31
Predicting in-hospital mortality from Coronavirus Disease 2019: A simple validated app for clinical use
Bianca Magro, Valentina Zuccaro, Luca Novelli, et al.
PLoS ONE (2021) Vol. 16, Iss. 1, pp. e0245281-e0245281
Open Access | Times Cited: 30
Hypoxia reduces cell attachment of SARS-CoV-2 spike protein by modulating the expression of ACE2, neuropilin-1, syndecan-1 and cellular heparan sulfate
Endika Prieto‐Fernández, Leire Egia‐Mendikute, Laura Vila, et al.
Emerging Microbes & Infections (2021) Vol. 10, Iss. 1, pp. 1065-1076
Open Access | Times Cited: 29
Early Prediction of COVID-19 Ventilation Requirement and Mortality from Routinely Collected Baseline Chest Radiographs, Laboratory, and Clinical Data with Machine Learning
Abdulrhman Aljouie, Ahmed Almazroa, Yahya Bokhari, et al.
Journal of Multidisciplinary Healthcare (2021) Vol. Volume 14, pp. 2017-2033
Open Access | Times Cited: 29
eXtreme Gradient Boosting-based method to classify patients with COVID-19
Antonio Ramón, Ana María Torres, Javier Milara, et al.
Journal of Investigative Medicine (2022) Vol. 70, Iss. 7, pp. 1472-1480
Closed Access | Times Cited: 22
Application of machine learning models based on decision trees in classifying the factors affecting mortality of COVID-19 patients in Hamadan, Iran
Samad Moslehi, Niloofar Rabiei, Ali Reza Soltanian, et al.
BMC Medical Informatics and Decision Making (2022) Vol. 22, Iss. 1
Open Access | Times Cited: 22
Identification of Clinical Features Associated with Mortality in COVID-19 Patients
Rahimeh Eskandarian, Roohallah Alizadehsani, Mohaddeseh Behjati, et al.
Operations Research Forum (2023) Vol. 4, Iss. 1
Open Access | Times Cited: 13
Unraveling complex relationships between COVID-19 risk factors using machine learning based models for predicting mortality of hospitalized patients and identification of high-risk group: a large retrospective study
Mohammad Mehdi Banoei, Haniyeh Rafiepoor, Kazem Zendehdel, et al.
Frontiers in Medicine (2023) Vol. 10
Open Access | Times Cited: 12
Prediction of prognosis in COVID-19 patients using machine learning: A systematic review and meta-analysis
Ruiyao Chen, Jiayuan Chen, Sen Yang, et al.
International Journal of Medical Informatics (2023) Vol. 177, pp. 105151-105151
Closed Access | Times Cited: 12
Predicting nutritional status for women of childbearing age from their economic, health, and demographic features: A supervised machine learning approach
Md. Mohsan Khudri, Kang Keun Rhee, Mohammad Shabbir Hasan, et al.
PLoS ONE (2023) Vol. 18, Iss. 5, pp. e0277738-e0277738
Open Access | Times Cited: 11
Patient level dataset to study the effect of COVID-19 in people with Multiple Sclerosis
Hamza Khan, Lotte Geys, Peer Baneke, et al.
Scientific Data (2024) Vol. 11, Iss. 1
Open Access | Times Cited: 4

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