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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.

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Showing 1-25 of 102 citing articles:

Evaluation of Tree-Based Ensemble Machine Learning Models in Predicting Stock Price Direction of Movement
Ernest Kwame Ampomah, Zhiguang Qin, Gabriel Nyame
Information (2020) Vol. 11, Iss. 6, pp. 332-332
Open Access | Times Cited: 192
A Review of Machine Learning and Deep Learning Approaches on Mental Health Diagnosis
Ngumimi Karen Iyortsuun, Soo-Hyung Kim, Min Jhon, et al.
Healthcare (2023) Vol. 11, Iss. 3, pp. 285-285
Open Access | Times Cited: 123
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: 87
Depression Detection From Social Networks Data Based on Machine Learning and Deep Learning Techniques: An Interrogative Survey
Khan Md. Hasib, Md Rafiqul Islam, Shadman Sakib, et al.
IEEE Transactions on Computational Social Systems (2023) Vol. 10, Iss. 4, pp. 1568-1586
Closed Access | Times Cited: 58
A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation
Siavash Bolourani, Max Brenner, Ping Wang, et al.
Journal of Medical Internet Research (2021) Vol. 23, Iss. 2, pp. e24246-e24246
Open Access | Times Cited: 99
An insight into diagnosis of depression using machine learning techniques: a systematic review
Sweta Bhadra, Chandan Jyoti Kumar
Current Medical Research and Opinion (2022) Vol. 38, Iss. 5, pp. 749-771
Closed Access | Times Cited: 53
A lexicon-based approach to examine depression detection in social media: the case of Twitter and university community
Junyeop Cha, Seoyun Kim, Eunil Park
Humanities and Social Sciences Communications (2022) Vol. 9, Iss. 1
Open Access | Times Cited: 33
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: 32
Machine Learning, Deep Learning and Data Preprocessing Techniques for Detection, Prediction, and Monitoring of Stress and Stress-related Mental Disorders: A Scoping Review (Preprint)
Moein Razavi, Samira Ziyadidegan, Ahmadreza Mahmoudzadeh, et al.
JMIR Mental Health (2024) Vol. 11, pp. e53714-e53714
Open Access | Times Cited: 7
Predictive Analysis of Mental Health Conditions Using AdaBoost Algorithm
Elizabeth O. Ogunseye, Cecilia Ajowho Adenusi, Andrew Chinonso Nwanakwaugwu, et al.
ParadigmPlus (2022) Vol. 3, Iss. 2, pp. 11-26
Open Access | Times Cited: 27
Predicting Treatment Response in Schizophrenia With Magnetic Resonance Imaging and Polygenic Risk Score
Meng Wang, Ke Hu, Lingzhong Fan, et al.
Frontiers in Genetics (2022) Vol. 13
Open Access | Times Cited: 23
Random Forest with Sampling Techniques for Handling Imbalanced Prediction of University Student Depression
Siriporn Sawangarreerak, Putthiporn Thanathamathee
Information (2020) Vol. 11, Iss. 11, pp. 519-519
Open Access | Times Cited: 33
Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions
Thalia Richter, Barak Fishbain, Gal Richter‐Levin, et al.
Journal of Personalized Medicine (2021) Vol. 11, Iss. 10, pp. 957-957
Open Access | Times Cited: 31
Comorbidity and multimorbidity prediction of major chronic diseases using machine learning and network analytics
Shahadat Uddin, Shangzhou Wang, Haohui Lu, et al.
Expert Systems with Applications (2022) Vol. 205, pp. 117761-117761
Closed Access | Times Cited: 20
Machine Learning in ADHD and Depression Mental Health Diagnosis: A Survey
Christian Nash, Rajesh Nair, Syed Mohsen Naqvi
IEEE Access (2023) Vol. 11, pp. 86297-86317
Open Access | Times Cited: 12
Implications of resampling data to address the class imbalance problem (IRCIP): an evaluation of impact on performance between classification algorithms in medical data
Koen Welvaars, Jacobien H. F. Oosterhoff, Michel P. J. van den Bekerom, et al.
JAMIA Open (2023) Vol. 6, Iss. 2
Open Access | Times Cited: 11
Evaluating Machine Learning Stability in Predicting Depression and Anxiety Amidst Subjective Response Errors
Wai Lim Ku, Hua Min
Healthcare (2024) Vol. 12, Iss. 6, pp. 625-625
Open Access | Times Cited: 4
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: 4
Improving the diagnostic accuracy for major depressive disorder using machine learning algorithms integrating clinical and near-infrared spectroscopy data
Cyrus S. H. Ho, Yee Ling Chan, Trevor Wei Kiat Tan, et al.
Journal of Psychiatric Research (2022) Vol. 147, pp. 194-202
Closed Access | Times Cited: 18
Tryptophan metabolism-related gene CYP1B1 serves as a shared biomarker for both Parkinson’s disease and insomnia
Xinyu Li, Wen-Kai Yu, Jinghao Wu, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Explainable artificial intelligence systems for predicting mental health problems in autistics
El-Sayed Atlam, Mahmoud Rokaya, Mehedi Masud, et al.
Alexandria Engineering Journal (2025) Vol. 117, pp. 376-390
Open Access
Physiological, Psychological, and Functional Health Determinants of Depressive Symptoms Among the Elderly in India: Evaluation of Classification Performance of XGBoost Models
Aswathy PV, Abhishek Verma, Balasankar JM, et al.
Indian Journal of Psychological Medicine (2025)
Open Access
Mental health evaluation during internet blackouts: A case study of Bangladesh Quota Movement
Mohammad Ariful Islam Rafi, Tahidul Islam
ITM Web of Conferences (2025) Vol. 72, pp. 02004-02004
Open Access
Explainable Artificial Intelligence Models for Predicting Depression Based on Polysomnographic Phenotypes
Doljinsuren Enkhbayar, Jonathan Ko, Sejong Oh, et al.
Bioengineering (2025) Vol. 12, Iss. 2, pp. 186-186
Open Access
Bootstrap inference and machine learning reveal core differential plasma metabolic connectome signatures in major depressive disorder
Hongxin Pan, Yuyang Sha, Xiaobing Zhai, et al.
Journal of Affective Disorders (2025) Vol. 378, pp. 281-292
Closed Access
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