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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:

Prediction of depressive symptoms onset and long-term trajectories in home-based older adults using machine learning techniques
Shaowu Lin, Yafei Wu, Lingxiao He, et al.
Aging & Mental Health (2022) Vol. 27, Iss. 1, pp. 8-17
Closed Access | Times Cited: 18

Showing 18 citing articles:

Prediction of depressive symptoms severity based on sleep quality, anxiety, and gray matter volume: a generalizable machine learning approach across three datasets
Mahnaz Olfati, Fateme Samea, Shahrooz Faghihroohi, et al.
EBioMedicine (2024) Vol. 108, pp. 105313-105313
Open Access | Times Cited: 8
Impact of Artificial Intelligence in Nursing for Geriatric Clinical Care for Chronic Diseases: A Systematic Literature Review
Mahdieh Poodineh Moghadam, Zabih Allah Moghadam, Mohammad Reza Chalak Qazani, et al.
IEEE Access (2024) Vol. 12, pp. 122557-122587
Open Access | Times Cited: 4
Prediction of late-onset depression in the elderly Korean population using machine learning algorithms
Jong Wan Park, Chang Woo Ko, D. Lee, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Long-term trajectories of depressive symptoms and machine learning techniques for fall prediction in older adults: Evidence from the China Health and Retirement Longitudinal Study (CHARLS)
Xiaodong Chen, Shaowu Lin, Yixuan Zheng, et al.
Archives of Gerontology and Geriatrics (2023) Vol. 111, pp. 105012-105012
Closed Access | Times Cited: 9
Trajectories of depressive symptoms and their predictors in Chinese older population: Growth Mixture model
Yaofei Xie, Mengdi Ma, Wei Wang
BMC Geriatrics (2023) Vol. 23, Iss. 1
Open Access | Times Cited: 9
Long-term trajectories of depressive symptoms in deployed military personnel: A 10-year prospective study
Xandra Plas, Bastiaan Bruinsma, Caspar J. Van Lissa, et al.
Journal of Affective Disorders (2024) Vol. 354, pp. 702-711
Open Access | Times Cited: 3
Predicting future depressive episodes from resting-state fMRI with generative embedding
Herman Galioulline, Stefan Frässle, Samuel J. Harrison, et al.
NeuroImage (2023) Vol. 273, pp. 119986-119986
Open Access | Times Cited: 8
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
Prediction of depressive symptoms severity based on sleep quality, anxiety, and brain: a machine learning approach across three cohorts
Mahnaz Olfati, Fateme Samea, Shahrooz Faghihroohi, et al.
medRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 4
A case for the use of deep learning algorithms for individual and population level assessments of mental health disorders: Predicting depression among China's elderly
Yingjie Wang, Xuzhe Wang, Li Zhao, et al.
Journal of Affective Disorders (2024)
Open Access | Times Cited: 1
Using machine learning to predict the probability of incident 2-year depression in older adults with chronic diseases: a retrospective cohort study
Ying Zheng, Taotao Zhang, Shu Yang, et al.
BMC Psychiatry (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 1
A Multilevel Depression Detection from Twitter using Fine-Tuned RoBERTa
Afra Zaman, Syeda Sunjida Ferdous, Nazneen Akhter, et al.
(2023), pp. 280-284
Closed Access | Times Cited: 2
Longitudinal Associations Between Multiple Types of Adverse Childhood Experiences and Depression Trajectories in Middle-Aged and Older Chinese Adults: a Growth Mixture Model
Man‐Man Peng, Zurong Liang
International Journal of Mental Health and Addiction (2023)
Closed Access | Times Cited: 2
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
Long-term Trajectories of Depressive Symptoms in Deployed Military Personnel: A 10-year prospective study
Xandra Plas, Bastiaan Bruinsma, Caspar J. Van Lissa, et al.
medRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 1
Identifying the most crucial factors associated with depression based on interpretable machine learning: a case study from CHARLS
Rulin Li, Xueyan Wang, Lanjun Luo, et al.
Frontiers in Psychology (2024) Vol. 15
Open Access
Prediction of late-onset depression in the elderly Korean population using machine learning algorithms
Jong Wan Park, Chang Woo Ko, D. Lee, et al.
Research Square (Research Square) (2024)
Open Access
Association of TyG Index and TG/HDL-C Ratio with Trajectories of Depressive Symptoms: Evidence from China Health and Retirement Longitudinal Study
Tingting Guo, Qing Zou, Qi Wang, et al.
Nutrients (2024) Vol. 16, Iss. 24, pp. 4300-4300
Open Access
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