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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:

<p>Machine Learning Methods to Evaluate the Depression Status of Chinese Recruits: A Diagnostic Study</p>
Mengxue Zhao, Zhengzhi Feng
Neuropsychiatric Disease and Treatment (2020) Vol. Volume 16, pp. 2743-2752
Open Access | Times Cited: 11

Showing 11 citing articles:

Machine Learning Algorithms for Depression: Diagnosis, Insights, and Research Directions
Shumaila Aleem, Noor ul Huda, Rashid Amin, et al.
Electronics (2022) Vol. 11, Iss. 7, pp. 1111-1111
Open Access | Times Cited: 82
Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review
Roberto Tornero-Costa, Antonio Martínez-Millana, Natasha Azzopardi‐Muscat, et al.
JMIR Mental Health (2023) Vol. 10, pp. e42045-e42045
Open Access | Times Cited: 54
Unravelling the complexities of depression with medical intelligence: exploring the interplay of genetics, hormones, and brain function
Md Belal Bin Heyat, Faijan Akhtar, Farwa Munir, et al.
Complex & Intelligent Systems (2024) Vol. 10, Iss. 4, pp. 5883-5915
Open Access | Times Cited: 19
Intelligent Internet of Medical Things for Depression: Current Advancements, Challenges, and Trends
Md Belal Bin Heyat, Deepak Adhikari, Faijan Akhtar, et al.
International Journal of Intelligent Systems (2025) Vol. 2025, Iss. 1
Open Access | Times Cited: 1
Machine learning-based predictive modeling of depression in hypertensive populations
Chi‐Young Lee, Heewon Kim
PLoS ONE (2022) Vol. 17, Iss. 7, pp. e0272330-e0272330
Open Access | Times Cited: 31
Personality traits as predictors of depression across the lifespan
Zhen Yang, Allison Li, Chloe Roske, et al.
Journal of Affective Disorders (2024) Vol. 356, pp. 274-283
Open Access | Times Cited: 5
Effectiveness of artificial intelligence in detecting and managing depressive disorders: Systematic review
Yoonseo Park, Sewon Park, Munjae Lee
Journal of Affective Disorders (2024) Vol. 361, pp. 445-456
Closed Access | Times Cited: 3
A Review of Overfitting Solutions in Smart Depression Detection Models
Gaurav Gupta, Dilip Kumar Sharma
2022 9th International Conference on Computing for Sustainable Global Development (INDIACom) (2022), pp. 145-151
Closed Access | Times Cited: 15
Forecasting Mental Stress Using Machine Learning Algorithms
ME Hossain, Abdulwahab Alazeb, Naif Almudawi, et al.
Computers, materials & continua/Computers, materials & continua (Print) (2022) Vol. 72, Iss. 3, pp. 4945-4966
Open Access | Times Cited: 6
Hybrid Model for Analysis of Social Media Posts for Identification of Depression and Measuring Its Severity
Jay Nanavati, Unnati Patel
(2023), pp. 1-5
Closed Access | Times Cited: 2
Identification of depressive symptoms: A cause-and-effect based machine learning study
Tiantian Wang, Zijian Zhang, Xilin Zhang, et al.
Chinese Medical Journal (2024) Vol. 137, Iss. 10, pp. 1258-1260
Open Access
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