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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:

Evaluation of nutritional status and clinical depression classification using an explainable machine learning method
Payam Hosseinzadeh Kasani, Jung Eun Lee, Chihyun Park, et al.
Frontiers in Nutrition (2023) Vol. 10
Open Access | Times Cited: 13

Showing 13 citing articles:

A Scoping Review of Artificial Intelligence for Precision Nutrition
Xizhi Wu, David Oniani, Zeman Shao, et al.
Advances in Nutrition (2025), pp. 100398-100398
Open Access | Times Cited: 1
A novel method for predicting debris flow hazard: a multi-strategy fusion approach based on the light gradient boosting machine framework
Tianlong Wang, Qi Ge, Tianxing Ma, et al.
Stochastic Environmental Research and Risk Assessment (2025)
Closed Access | Times Cited: 1
Machine Learning in Predicting Child Malnutrition: A Meta-Analysis of Demographic and Health Surveys Data
Bhagyajyothi Rao, Muhammed Rashid, Md. Gulzarul Hasan, et al.
International Journal of Environmental Research and Public Health (2025) Vol. 22, Iss. 3, pp. 449-449
Open Access
A comprehensive review of explainable AI for disease diagnosis
Al Amin Biswas
Array (2024) Vol. 22, pp. 100345-100345
Open Access | Times Cited: 3
Enhancing explainability in predicting mental health disorders using human–machine interaction
Inderpreet Kaur, Kamini, Jaskirat Kaur, et al.
Multimedia Tools and Applications (2024)
Closed Access | Times Cited: 2
Explainable AI and transformer models: Unraveling the nutritional influences on Alzheimer's disease mortality
Ziming Liu, Longjian Liu, R. Eric Heidel, et al.
Smart Health (2024) Vol. 32, pp. 100478-100478
Open Access | Times Cited: 2
Predicting Depression during the COVID-19 Pandemic Using Interpretable TabNet: A Case Study in South Korea
Hung Viet Nguyen, Haewon Byeon
Mathematics (2023) Vol. 11, Iss. 14, pp. 3145-3145
Open Access | Times Cited: 6
Global, Regional, and National Epidemiology of Depression in Working‐Age Individuals, 1990–2019
J Yang, L Zhang, Cheng-Hao Yang, et al.
Depression and Anxiety (2024) Vol. 2024, Iss. 1
Open Access | Times Cited: 1
Comparative effectiveness of explainable machine learning approaches for extrauterine growth restriction classification in preterm infants using longitudinal data
Kee Hyun Cho, Eun Sun Kim, Jong Wook Kim, et al.
Frontiers in Medicine (2023) Vol. 10
Open Access | Times Cited: 3
SymScore: Machine Learning Accuracy Meets Transparency in a Symbolic Regression-Based Clinical Score Generator
Olive R. Cawiding, Sieun Lee, Hyeontae Jo, et al.
medRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Lifestyle factors and other predictors of common mental disorders in diagnostic machine learning studies: A systematic review
Emma Todd, Rebecca Orr, Elizabeth Gamage, et al.
Computers in Biology and Medicine (2024) Vol. 185, pp. 109521-109521
Open Access
SymScore: Machine learning accuracy meets transparency in a symbolic regression-based clinical score generator
Olive R. Cawiding, Sieun Lee, Hyeontae Jo, et al.
Computers in Biology and Medicine (2024) Vol. 185, pp. 109589-109589
Open Access
Integration of PSO-based advanced supervised learning techniques for classification data mining to predict heart failure
Mesran Mesran, Remuz MB Kmurawak, Agus Perdana Windarto
TELKOMNIKA (Telecommunication Computing Electronics and Control) (2023) Vol. 22, Iss. 1, pp. 76-76
Open Access
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