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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:

Using machine learning approach to predict depression and anxiety among patients with epilepsy in China: A cross-sectional study
Zihan Wei, Xinpei Wang, Lei Ren, et al.
Journal of Affective Disorders (2023) Vol. 336, pp. 1-8
Closed Access | Times Cited: 17

Showing 17 citing articles:

Identification of a novel disulfidptosis-related gene signature in osteoarthritis using bioinformatics analysis and experimental validation
Mingjie Wei, Xiangwen Shi, Wenbao Tang, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 1
Using machine learning to predict depression among middle-aged and elderly population in China and conducting empirical analysis
Zhe Wang, Ni Jia
PLoS ONE (2025) Vol. 20, Iss. 3, pp. e0319232-e0319232
Open Access
Machine learning algorithms to predict depression in older adults in China: a cross-sectional study
Yongping Song, Chen Lin, Haoqiang Liu, et al.
Frontiers in Public Health (2025) Vol. 12
Open Access
Microstate-based brain network dynamics distinguishing temporal lobe epilepsy patients: A machine learning approach
Zihan Wei, Xinpei Wang, Chao Liu, et al.
NeuroImage (2024) Vol. 296, pp. 120683-120683
Open Access | Times Cited: 2
Using machine learning models to identify the risk of depression in middle-aged and older adults with frequent and infrequent nicotine use: A cross-sectional study
Yuran Qiu, Xiangru Zhu, Xu Ma
Journal of Affective Disorders (2024) Vol. 367, pp. 554-561
Closed Access | Times Cited: 2
Machine learning algorithms to predict mild cognitive impairment in older adults in China: A cross-sectional study
Yanliqing Song, Quan Yuan, Haoqiang Liu, et al.
Journal of Affective Disorders (2024)
Closed Access | Times Cited: 2
Federated learning-based prediction of depression among adolescents across multiple districts in China
Yalan Kuang, Liao Xiao, Zekun Jiang, et al.
Journal of Affective Disorders (2024) Vol. 369, pp. 625-632
Closed Access | Times Cited: 2
Evaluating predictive artificial intelligence approaches used in mobile health platforms to forecast mental health symptoms among youth: a systematic review
Jamin Patel, Chih-Ching Hung, Tarun Reddy Katapally
Psychiatry Research (2024) Vol. 343, pp. 116277-116277
Open Access | Times Cited: 2
Anxiety in young people: Analysis from a machine learning model
Marcela Tabares Tabares, Consuelo Vélez Álvarez, Joshua Bernal Salcedo, et al.
Acta Psychologica (2024) Vol. 248, pp. 104410-104410
Open Access | Times Cited: 1
Felt stigma and associated factors in children and adolescents with epilepsy: a multicenter cross-sectional study in China
Jing Zhao, Shuangzi Li, Ni Zhang, et al.
Frontiers in Neurology (2024) Vol. 15
Open Access | Times Cited: 1
Applying contrastive pre-training for depression and anxiety risk prediction in type 2 diabetes patients based on heterogeneous electronic health records: a primary healthcare case study
Wei Feng, Honghan Wu, Hui Ma, et al.
Journal of the American Medical Informatics Association (2023) Vol. 31, Iss. 2, pp. 445-455
Open Access | Times Cited: 3
Fear of disease in patients with epilepsy – a network analysis
Xiaoxiao Yin, Shan Niu, Qun Yu, et al.
Frontiers in Neurology (2024) Vol. 15
Open Access
Accuracy of machine learning in detecting pediatric epileptic seizures: a systematic review and meta-analysis (Preprint)
Zhuan Zou, Bin Chen, Dongqiong Xiao, et al.
Journal of Medical Internet Research (2024) Vol. 26, pp. e55986-e55986
Open Access
Anxiety in Young People: Analysis from a Machine Learning Model
Marcela Tabares Tabares, Consuelo Vélez Álvarez, Joshua Bernal Salcedo, et al.
(2024)
Closed Access
Development and validation of a machine learning model for prediction of comorbid major depression disorder among narcolepsy type 1
Yuanhang Pan, Xinbo Zhang, Xinyu Wen, et al.
Sleep Medicine (2024) Vol. 119, pp. 556-564
Closed Access
Identification of depression predictors from standard health surveys using machine learning
Ali Akbar Jamali, Corinne Berger, Raymond J. Spiteri
Current Research in Behavioral Sciences (2024) Vol. 7, pp. 100157-100157
Open Access
An explainable predictive model for anxiety symptoms risk among Chinese older adults with abdominal obesity using a machine learning and SHapley Additive exPlanations approach
Tengfei Niu, Shiwei Cao, Jingyu Cheng, et al.
Frontiers in Psychiatry (2024) Vol. 15
Open Access
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