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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:

Detection of child depression using machine learning methods
Umme Marzia Haque, Enamul Kabir, Rasheda Khanam
PLoS ONE (2021) Vol. 16, Iss. 12, pp. e0261131-e0261131
Open Access | Times Cited: 69

Showing 1-25 of 69 citing articles:

Uncovering the symptom relationship between anxiety, depression, and internet addiction among left-behind children: A large-scale purposive sampling network analysis
Xi Shen, Xinqi Zhou, Haiping Liao, et al.
Journal of Psychiatric Research (2024) Vol. 171, pp. 43-51
Closed Access | Times Cited: 11
Predicting the depression in university students using stacking ensemble techniques over oversampling method
Alfredo Daza Vergaray, Juan Carlos Herrera, Juana Bobadilla Cornelio, et al.
Informatics in Medicine Unlocked (2023) Vol. 41, pp. 101295-101295
Open Access | Times Cited: 20
Predicting individual cases of major adolescent psychiatric conditions with artificial intelligence
Nina de Lacy, Michael J. Ramshaw, Elizabeth McCauley, et al.
Translational Psychiatry (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 17
Assessment and Prediction of Depression and Anxiety Risk Factors in Schoolchildren: Machine Learning Techniques Performance Analysis
Radwan Qasrawi, Stephanny Vicuna Polo, Diala Abu Al-Halawa, et al.
JMIR Formative Research (2022) Vol. 6, Iss. 8, pp. e32736-e32736
Open Access | Times Cited: 27
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: 5
Depression detection in social media comments data using machine learning algorithms
Zannatun Nayem Vasha, Bidyut Sharma, Israt Jahan Esha, et al.
Bulletin of Electrical Engineering and Informatics (2022) Vol. 12, Iss. 2, pp. 987-996
Open Access | Times Cited: 21
Efficacy of novel attention-based gated recurrent units transformer for depression detection using electroencephalogram signals
Neha Prerna Tigga, Shruti Garg
Health Information Science and Systems (2022) Vol. 11, Iss. 1
Closed Access | Times Cited: 20
AIDA: Artificial intelligence based depression assessment applied to Bangladeshi students
Rokeya Siddiqua, Nusrat Sharmin Islam, Jarba Farnaz Bolaka, et al.
Array (2023) Vol. 18, pp. 100291-100291
Open Access | Times Cited: 13
Machine Learning and Electroencephalogram Signal based Diagnosis of Depression
Adil O. Khadidos, Khaled H. Alyoubi, Shalini Mahato, et al.
Neuroscience Letters (2023) Vol. 809, pp. 137313-137313
Closed Access | Times Cited: 12
Machine learning prediction of mental health strategy selection in school aged children using neurocognitive data
Richard Lamb, Jonah B. Firestone, Amanda Kavner, et al.
Computers in Human Behavior (2024) Vol. 156, pp. 108197-108197
Closed 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
A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning–Based Approach
Md. Sabbir Ahmed, Nova Ahmed
JMIR Formative Research (2023) Vol. 7, pp. e28848-e28848
Open Access | Times Cited: 11
Early detection of paediatric and adolescent obsessive–compulsive, separation anxiety and attention deficit hyperactivity disorder using machine learning algorithms
Umme Marzia Haque, Enamul Kabir, Rasheda Khanam
Health Information Science and Systems (2023) Vol. 11, Iss. 1
Open Access | Times Cited: 11
Machine Learning Approach for Text Pattern Diagnosis of Mental Disorders in Online Health Consultation
Safitri Juanita, Anisah Hasratniwati Daeli, Mohammad Syafrullah, et al.
(2025)
Closed Access
A Review of Machine Learning-Based Assessment of Depression
Zhao Wang, Ziyi Cai, Shuya Dong, et al.
Communications in computer and information science (2025), pp. 266-290
Closed Access
A machine learning approach for text pattern diagnosis in mental health consultations
Safitri Juanita, Anisah Hasratniwati Daeli, Mohammad Syafrullah, et al.
Decision Analytics Journal (2025), pp. 100572-100572
Open Access
Predicting children's emotional and behavioral difficulties at age five using pregnancy and newborn risk factors: Evidence from the UK Household Longitudinal Study
Xuejing Zong, Li Y, Can Liu, et al.
Journal of Affective Disorders (2025)
Open Access
Longitudinal Prediction of Mental Health Outcomes in Vulnerable Youth using Machine Learning
Esmeralda Ruiz Pujadas, Covadonga M. Díaz‐Caneja, Dejan Stevanović, et al.
(2025)
Closed Access
Exploring Early Learning Challenges in Children Utilizing Statistical and Explainable Machine Learning
Mithila Akter Mim, Mst Rokeya Khatun, Muhammad Minoar Hossain, et al.
Algorithms (2025) Vol. 18, Iss. 1, pp. 20-20
Open Access
Predicting Depression in Healthy Young Adults: A Machine Learning Approach Using Longitudinal Neuroimaging Data
Ailing Zhang, Haobo Zhang
NeuroImage (2025), pp. 121285-121285
Open Access
Associated Patterns and Predicting Model of Life Trauma, Depression, and Suicide Using Ensemble Machine Learning
Saifon Aekwarangkoon, Putthiporn Thanathamathee
Emerging Science Journal (2022) Vol. 6, Iss. 4, pp. 679-693
Open Access | Times Cited: 13
Early warning model of adolescent mental health based on big data and machine learning
Ziyi Zhang
Soft Computing (2023) Vol. 28, Iss. 1, pp. 811-828
Closed Access | Times Cited: 8
Using machine learning to develop a five-item short form of the children’s depression inventory
Shumei Lin, Chengwei Wang, Xiuyu Jiang, et al.
BMC Public Health (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 2
Predicting risk of overweight or obesity in Chinese preschool-aged children using artificial intelligence techniques
Qiong Wang, Min Yang, Bo Pang, et al.
Endocrine (2022) Vol. 77, Iss. 1, pp. 63-72
Closed Access | Times Cited: 11
Using random forest to identify correlates of depression symptoms among adolescents
Mahmood Reza Gohari, Amanda Doggett, Karen A. Patte, et al.
Social Psychiatry and Psychiatric Epidemiology (2024)
Closed Access | Times Cited: 2
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