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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:

Machine learning applications in tobacco research: a scoping review
Rui Fu, Anasua Kundu, Nicholas Mitsakakis, et al.
Tobacco Control (2021) Vol. 32, Iss. 1, pp. 99-109
Closed Access | Times Cited: 40

Showing 1-25 of 40 citing articles:

Understanding e-cigarette content and promotion on YouTube through machine learning
Grace Kong, Alex Sebastian Schott, Juhan Lee, et al.
Tobacco Control (2022) Vol. 32, Iss. 6, pp. 739-746
Open Access | Times Cited: 28
Scoping review of guidance on cessation interventions for electronic cigarettes and dual electronic and combustible cigarettes use
Anasua Kundu, Erika Kouzoukas, Laurie Zawertailo, et al.
CMAJ Open (2023) Vol. 11, Iss. 2, pp. E336-E344
Open Access | Times Cited: 18
A multimodal deep learning architecture for smoking detection with a small data approach
Róbert Lakatos, Péter Pollner, András Hajdú, et al.
Frontiers in Artificial Intelligence (2024) Vol. 7
Open Access | Times Cited: 6
Using the Statistical Machine Learning Models ARIMA and SARIMA to Measure the Impact of Covid-19 on Official Provincial Sales of Cigarettes in Spain
Andoni Andueza, Miguel Ángel Del Arco-Osuna, Bernat Fornés, et al.
International Journal of Interactive Multimedia and Artificial Intelligence (2023) Vol. 8, Iss. 1, pp. 73-73
Open Access | Times Cited: 15
Scalable Surveillance of E-Cigarette Products on Instagram and TikTok Using Computer Vision
Julia Vassey, Chris J. Kennedy, Ho-Chun Herbert Chang, et al.
Nicotine & Tobacco Research (2023) Vol. 26, Iss. 5, pp. 552-560
Open Access | Times Cited: 13
Large Language Models’ Accuracy in Emulating Human Experts’ Evaluation of Public Sentiments about Heated Tobacco Products on Social Media: Evaluation Study
Kwanho Kim, Soojong Kim
Journal of Medical Internet Research (2025) Vol. 27, pp. e63631-e63631
Open Access
Artificial intelligence to improve cardiovascular population health
Benjamin Meder, Folkert W. Asselbergs, Euan A. Ashley
European Heart Journal (2025)
Open Access
Data mining and feature selection in machine learning analysis of cancer information seeking among U.S. adults (Preprint)
Ying Liu, Kesheng Wang
(2025)
Closed Access
Predictors of smoking cessation outcomes identified by machine learning: A systematic review
Warren K. Bickel, Devin C. Tomlinson, William H. Craft, et al.
Addiction Neuroscience (2023) Vol. 6, pp. 100068-100068
Open Access | Times Cited: 9
Predictors of perceived success in quitting smoking by vaping: A machine learning approach
Rui Fu, Robert Schwartz, Nicholas Mitsakakis, et al.
PLoS ONE (2022) Vol. 17, Iss. 1, pp. e0262407-e0262407
Open Access | Times Cited: 15
Examining Tobacco-Related Social Media Research in Government Policy Documents: Systematic Review
Trista Beard, Scott Donaldson, Jennifer B. Unger, et al.
Nicotine & Tobacco Research (2023) Vol. 26, Iss. 4, pp. 421-426
Open Access | Times Cited: 8
Examining the Peer-Reviewed Literature on Tobacco-Related Social Media Data: Scoping Review
Scott Donaldson, Allison Dormanesh, Anuja Majmundar, et al.
Nicotine & Tobacco Research (2023) Vol. 26, Iss. 4, pp. 413-420
Closed Access | Times Cited: 7
A machine learning approach to predict e-cigarette use and dependence among Ontario youth
Jiamin Shi, Rui Fu, Hayley A. Hamilton, et al.
Health Promotion and Chronic Disease Prevention in Canada (2022) Vol. 42, Iss. 1, pp. 21-28
Open Access | Times Cited: 12
A Machine Learning Approach to Identify Predictors of Frequent Vaping and Vulnerable Californian Youth Subgroups
Rui Fu, Jiamin Shi, Michael Chaiton, et al.
Nicotine & Tobacco Research (2021) Vol. 24, Iss. 7, pp. 1028-1036
Open Access | Times Cited: 14
“We Want to See Youth That Would Be Better People Than Us”: A Case Report on Addressing Adolescent Substance Use in Rural South Africa
Ifeolu David, Lisa Wegner, Wilson Majee
International Journal of Environmental Research and Public Health (2023) Vol. 20, Iss. 4, pp. 3493-3493
Open Access | Times Cited: 5
An Empirical Analysis of Diffusion, Autoencoders, and Adversarial Deep Learning Models for Predicting Dementia Using High-Fidelity MRI
Pranshav Gajjar, Manav Garg, Shivani Desai, et al.
IEEE Access (2024), pp. 1-1
Open Access | Times Cited: 1
Table 2 Fallacy in Descriptive Epidemiology: Bringing Machine Learning to the Table
Christoffer Dharma, Rui Fu, Michael Chaiton
International Journal of Environmental Research and Public Health (2023) Vol. 20, Iss. 13, pp. 6194-6194
Open Access | Times Cited: 3
Educating Substance Use Treatment Center Providers on Tobacco Use Treatments Is Associated with Increased Provision of Counseling and Medication to Patients Who Use Tobacco
Brian J. Carter, Ammar D. Siddiqi, Tzu-An Chen, et al.
International Journal of Environmental Research and Public Health (2023) Vol. 20, Iss. 5, pp. 4013-4013
Open Access | Times Cited: 2
Are the Relevant Risk Factors Being Adequately Captured in Empirical Studies of Smoking Initiation? A Machine Learning Analysis Based on the Population Assessment of Tobacco and Health Study
Thuy T. T. Le, Mona Issabakhsh, Yameng Li, et al.
Nicotine & Tobacco Research (2023) Vol. 25, Iss. 8, pp. 1481-1488
Open Access | Times Cited: 2
A Machine Learning Approach Reveals Distinct Predictors of Vaping Dependence for Adolescent Daily and Non-Daily Vapers in the COVID-19 Era
Ishmeet Singh, Varna Valavil Punnapuzha, Nicholas Mitsakakis, et al.
Healthcare (2023) Vol. 11, Iss. 10, pp. 1465-1465
Open Access | Times Cited: 2
Correlates of wanting to seek help for mental health and substance use concerns by sexual and gender minority young adults during the COVID-19 pandemic: A machine learning analysis
Anasua Kundu, Rui Fu, Daniel Grace, et al.
PLoS ONE (2022) Vol. 17, Iss. 11, pp. e0277438-e0277438
Open Access | Times Cited: 3
Deep learning-based intelligent control of moisture at the exit of blade charging process in cigarette production
Jinsheng Rui, Dongchen Qiu, Shicong Hou, et al.
Applied Mathematics and Nonlinear Sciences (2024) Vol. 9, Iss. 1
Open Access
Machine Learning Analysis to Identify Factors Associated with Requesting Tobacco Cessation Services Among Users of an Online Self-Diagnostic Questionnaire in Mexico.
Norberto Francisco Hernández-Llanes, Ricardo Sánchez-Domínguez, Sofía Álvarez Reza, et al.
Research Square (Research Square) (2024)
Open Access
Futuristic Blockchain Based Computer Vision Technique for Environmentally Informed Smoking Cessation: A Revolutionary Approach to Predictive Modeling
Usama Arshad, Sajid Anwar, Babar Shah, et al.
Lecture notes in networks and systems (2024), pp. 113-126
Closed Access
Population Health Management: Leveraging it for Better Patient Outcome’s
Ankur Tak
International Journal of Health Sciences (2024) Vol. 7, Iss. 2, pp. 53-67
Open Access
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