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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:

Application of machine learning in predicting the risk of postpartum depression: A systematic review
Minhui Zhong, Han Zhang, Chan Yu, et al.
Journal of Affective Disorders (2022) Vol. 318, pp. 364-379
Closed Access | Times Cited: 31

Showing 1-25 of 31 citing articles:

Unveiling the prevalence and risk factors of early stage postpartum depression: a hybrid deep learning approach
Umesh Kumar Lilhore, Surjeet Dalal, Neetu Faujdar, et al.
Multimedia Tools and Applications (2024) Vol. 83, Iss. 26, pp. 68281-68315
Closed Access | Times Cited: 19
Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model
Umesh Kumar Lilhore, Surjeet Dalal, Neeraj Varshney, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 18
A random forest algorithm-based prediction model for moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia
Gaoxiang Shi, G. Liu, Qichao Gao, et al.
BMC Anesthesiology (2023) Vol. 23, Iss. 1
Open Access | Times Cited: 10
Consumer opinion on the use of machine learning in healthcare settings: A qualitative systematic review
Jacqueline H. Stephens, Celine Northcott, Brianna Poirier, et al.
Digital Health (2025) Vol. 11
Open Access
Development of an explainable machine learning model for predicting depression in adolescent girls with non-suicidal self-injury: A cross-sectional multicenter study
Ben Niu, Mengjie Wan, Yongjie Zhou
Journal of Affective Disorders (2025)
Closed Access
DMN network and neurocognitive changes associated with dissociative symptoms in major depressive disorder: a research protocol
Asli Ercan Dogan, Herdem Aslan Genç, Sinem Balaç, et al.
Frontiers in Psychiatry (2025) Vol. 16
Open Access
Machine learning approaches forpredicting postpartum depression risk leveraging XGBoost and CatBoost algorithms
P.M. Rameshkumar, J. Mohanraj, K. Elango, et al.
AIP conference proceedings (2025) Vol. 3279, pp. 020089-020089
Closed Access
Text-Based Depression Prediction on Social Media Using Machine Learning: Systematic Review and Meta-Analysis
Doreen Phiri, Frank Makowa, Vivi Leona Amelia, et al.
Journal of Medical Internet Research (2025) Vol. 27, pp. e59002-e59002
Open Access
Machine learning models to predict posttraumatic stress injuries in a sample of firefighters: A proof of concept
Filippo Rapisarda, Marc J. Lanovaz, Stéphane Guay, et al.
International Journal of Mental Health (2025), pp. 1-21
Closed Access
Targeted Research and Treatment Implications in Women With Depression
Marie E. Gaine, Kathleen M. Jagodnik, Ritika Baweja, et al.
FOCUS The Journal of Lifelong Learning in Psychiatry (2025) Vol. 23, Iss. 2, pp. 141-155
Closed Access
The NNDC Road Map for Depression Care and Focused Areas of Research

FOCUS The Journal of Lifelong Learning in Psychiatry (2025) Vol. 23, Iss. 2, pp. 217-218
Closed Access
Artificial Intelligence for Women and Child Healthcare: Is AI Able to Change the Beginning of a New Story? A Perspective
Patrícia Takako Endo
Health Science Reports (2025) Vol. 8, Iss. 5
Open Access
Postpartum depression in Northeastern China: a cross-sectional study 6 weeks after giving birth
Xudong Huang, Lifeng Zhang, ChenYang Zhang, et al.
Frontiers in Public Health (2025) Vol. 13
Open Access
Predicting Patients Requiring Treatment for Depression in the Postpartum Period Using Common Electronic Medical Record Data Available Antepartum
Colin Wakefield, Martin G. Frasch
AJPM Focus (2023) Vol. 2, Iss. 3, pp. 100100-100100
Open Access | Times Cited: 9
Predicting first time depression onset in pregnancy: applying machine learning methods to patient-reported data
Tamar Krishnamurti, Samantha N. Rodriguez, Bryan Wilder, et al.
Archives of Women s Mental Health (2024) Vol. 27, Iss. 6, pp. 1019-1031
Open Access | Times Cited: 3
SVM-Based Model Combining Patients’ Reported Outcomes and Lymphocyte Phenotypes of Depression in Systemic Lupus Erythematosus
Chen Dong, Nengjie Yang, Rui Zhao, et al.
Biomolecules (2023) Vol. 13, Iss. 5, pp. 723-723
Open Access | Times Cited: 7
Challenges and Opportunities for Data Science in Women's Health
Todd L. Edwards, Catherine A. Greene, Jacqueline A. Piekos, et al.
Annual Review of Biomedical Data Science (2023) Vol. 6, Iss. 1, pp. 23-45
Open Access | Times Cited: 4
Major Depressive Disorder Prediction Based on Sleep-Wake Disorders Symptoms in US Adolescents: A Machine Learning Approach from National Sleep Research Resource
Jingsong Luo, Yuxin Chen, Yanmin Tao, et al.
Psychology Research and Behavior Management (2024) Vol. Volume 17, pp. 691-703
Open Access | Times Cited: 1
Decision tree learning for predicting chronic postpartum depression in the Japan Environment and Children's Study
Kenta Matsumura, Kei Hamazaki, Haruka Kasamatsu, et al.
Journal of Affective Disorders (2024) Vol. 369, pp. 643-652
Open Access | Times Cited: 1
Performance Comparison of Randomized and Non-Randomized Learning Algorithms based Recommender Systems
Maryam Nadeem, Mohammed Wasid, Mohammad Nadeem, et al.
INTERNATIONAL JOURNAL OF NEXT-GENERATION COMPUTING (2022)
Open Access | Times Cited: 6
Triangular fuzzy numbers-based MADM for selecting pregnant mothers at risk of stunting
Wiwien Hadikurniawati, Kristoko Dwi Hartomo, Irwan Sembiring, et al.
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) (2023) Vol. 7, Iss. 3, pp. 579-585
Open Access | Times Cited: 3
Emotion regulation in bipolar disorder type-I: multivariate analysis of fMRI data
Fumika Kondo, Jocelyne C. Whitehead, Fernando Corbalán, et al.
International Journal of Bipolar Disorders (2023) Vol. 11, Iss. 1
Open Access | Times Cited: 1
Optimal risk and diagnosis assessment strategies in perinatal depression: A machine learning approach from the life-ON study cohort
Armando D’Agostino, Corrado Garbazza, Daniele Malpetti, et al.
Psychiatry Research (2023) Vol. 332, pp. 115687-115687
Open Access | Times Cited: 1
The Future of Prediction Modeling in Clinical Practice for Obstetrics and Gynecology
Digna R. Velez Edwards, Todd L. Edwards
Obstetrics and Gynecology (2024) Vol. 143, Iss. 3, pp. 355-357
Closed Access
Machine learning approach for early prediction of postpartum depression
S M Morris, Dipika Rawat
Elsevier eBooks (2024), pp. 163-172
Closed Access
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