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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:

COVID-19 diagnosis by routine blood tests using machine learning
Matjaž Kukar, Gregor Gunčar, Tomaž Vovko, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 156

Showing 1-25 of 156 citing articles:

Applications of artificial intelligence in battling against covid-19: A literature review
Mohammad-H. Tayarani N.
Chaos Solitons & Fractals (2020) Vol. 142, pp. 110338-110338
Open Access | Times Cited: 195
Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review
Norah Alballa, Isra Al-Turaiki
Informatics in Medicine Unlocked (2021) Vol. 24, pp. 100564-100564
Open Access | Times Cited: 193
Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests
Federico Cabitza, Andrea Campagner, Davide Ferrari, et al.
Clinical Chemistry and Laboratory Medicine (CCLM) (2020) Vol. 59, Iss. 2, pp. 421-431
Open Access | Times Cited: 145
Time series prediction of COVID-19 transmission in America using LSTM and XGBoost algorithms
Junling Luo, Zhong-Liang Zhang, Yao Fu, et al.
Results in Physics (2021) Vol. 27, pp. 104462-104462
Open Access | Times Cited: 110
Automatic COVID-19 prediction using explainable machine learning techniques
Sanzida Solayman, Sk. Azmiara Aumi, Chand Sultana Mery, et al.
International Journal of Cognitive Computing in Engineering (2023) Vol. 4, pp. 36-46
Open Access | Times Cited: 57
Developing Deep LSTMs With Later Temporal Attention for Predicting COVID-19 Severity, Clinical Outcome, and Antibody Level by Screening Serological Indicators Over Time
Jiaxin Cai, Yang Li, Baichen Liu, et al.
IEEE Journal of Biomedical and Health Informatics (2024) Vol. 28, Iss. 7, pp. 4204-4215
Closed Access | Times Cited: 19
Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review
Buket Baddal, Ferdiye Taner, Dilber Uzun Ozsahin
Diagnostics (2024) Vol. 14, Iss. 5, pp. 484-484
Open Access | Times Cited: 16
Applying Machine Learning Sampling Techniques to Address Data Imbalance in a Chilean COVID-19 Symptoms and Comorbidities Dataset
Pablo Ormeño-Arriagada, Gastón Márquez, David Araya, et al.
Applied Sciences (2025) Vol. 15, Iss. 3, pp. 1132-1132
Open Access | Times Cited: 1
Ensemble learning model for diagnosing COVID-19 from routine blood tests
Maryam AlJame, Imtiaz Ahmad, Ayyub Imtiaz, et al.
Informatics in Medicine Unlocked (2020) Vol. 21, pp. 100449-100449
Open Access | Times Cited: 127
Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs
Marcos Antônio Alves, Giulia Zanon de Castro, Bruno Alberto Soares Oliveira, et al.
Computers in Biology and Medicine (2021) Vol. 132, pp. 104335-104335
Open Access | Times Cited: 98
Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review
Ezekwesiri Michael Nwanosike, Barbara R. Conway, Hamid A. Merchant, et al.
International Journal of Medical Informatics (2021) Vol. 159, pp. 104679-104679
Open Access | Times Cited: 79
Accurate detection of Covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy
Nehal A. Mansour, Ahmed I. Saleh, Mahmoud Badawy, et al.
Journal of Ambient Intelligence and Humanized Computing (2021) Vol. 13, Iss. 1, pp. 41-73
Open Access | Times Cited: 59
Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2
Kaifu Gao, Rui Wang, Jiahui Chen, et al.
Chemical Reviews (2022) Vol. 122, Iss. 13, pp. 11287-11368
Open Access | Times Cited: 58
COVID-19 Detection Empowered with Machine Learning and Deep Learning Techniques: A Systematic Review
Amir Rehman, Muhammad Azhar Iqbal, Huanlai Xing, et al.
Applied Sciences (2021) Vol. 11, Iss. 8, pp. 3414-3414
Open Access | Times Cited: 58
A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest
Mehrdad Rostami, Mourad Oussalah
Informatics in Medicine Unlocked (2022) Vol. 30, pp. 100941-100941
Open Access | Times Cited: 53
Clinical and Laboratory Approach to Diagnose COVID-19 Using Machine Learning
Krishnaraj Chadaga, Chinmay Chakraborty, Srikanth Prabhu, et al.
Interdisciplinary Sciences Computational Life Sciences (2022) Vol. 14, Iss. 2, pp. 452-470
Open Access | Times Cited: 49
Real-world evaluation of rapid and laboratory-free COVID-19 triage for emergency care: external validation and pilot deployment of artificial intelligence driven screening
Andrew A. S. Soltan, Jenny Yang, Ravi Pattanshetty, et al.
The Lancet Digital Health (2022) Vol. 4, Iss. 4, pp. e266-e278
Open Access | Times Cited: 40
Diagnosing COVID-19 using artificial intelligence: a comprehensive review
Varada Vivek Khanna, Krishnaraj Chadaga, Niranjana Sampathila, et al.
Network Modeling Analysis in Health Informatics and Bioinformatics (2022) Vol. 11, Iss. 1
Open Access | Times Cited: 39
Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application
Andrei Velichko, Mehmet Tahir Huyut, Maksim Belyaev, et al.
Sensors (2022) Vol. 22, Iss. 20, pp. 7886-7886
Open Access | Times Cited: 37
Effect of ferritin, INR, and D-dimer immunological parameters levels as predictors of COVID-19 mortality: A strong prediction with the decision trees
Mehmet Tahir Huyut, Zübeyir Huyut
Heliyon (2023) Vol. 9, Iss. 3, pp. e14015-e14015
Open Access | Times Cited: 22
NSGA-II as feature selection technique and AdaBoost classifier for COVID-19 prediction using patient’s symptoms
Makram Soui, Nesrine Mansouri, Raed Alhamad, et al.
Nonlinear Dynamics (2021) Vol. 106, Iss. 2, pp. 1453-1475
Open Access | Times Cited: 46
COVID-19 diagnosis from routine blood tests using artificial intelligence techniques
Samin Babaei Rikan, Amir Sorayaie Azar, Ali Ghafari, et al.
Biomedical Signal Processing and Control (2021) Vol. 72, pp. 103263-103263
Open Access | Times Cited: 46
An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples
Olusola Abayomi‐Alli, Robertas Damaševičius, Rytis Maskeliūnas, et al.
Sensors (2022) Vol. 22, Iss. 6, pp. 2224-2224
Open Access | Times Cited: 33
Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network
Mehmet Tahir Huyut, Andrei Velichko
Sensors (2022) Vol. 22, Iss. 13, pp. 4820-4820
Open Access | Times Cited: 32
Analyzing the Effect of Filtering and Feature-Extraction Techniques in a Machine Learning Model for Identification of Infectious Disease Using Radiography Imaging
Jawad Rasheed
Symmetry (2022) Vol. 14, Iss. 7, pp. 1398-1398
Open Access | Times Cited: 32
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