This paper is in the following e-collection/theme issue:

Artificial Intelligence (1676) Machine Learning (1694) Public (e)Health, Digital Epidemiology and Public Health Informatics (847) Depression and Mood Disorders; Suicide Prevention (1499)

Published on in Vol 23, No 6 (2021): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27344, first published .
Discovery of Depression-Associated Factors From a Nationwide Population-Based Survey: Epidemiological Study Using Machine Learning and Network Analysis

Discovery of Depression-Associated Factors From a Nationwide Population-Based Survey: Epidemiological Study Using Machine Learning and Network Analysis

Discovery of Depression-Associated Factors From a Nationwide Population-Based Survey: Epidemiological Study Using Machine Learning and Network Analysis

Authors of this article:

Sang Min Nam1 Author Orcid Image ;   Thomas A Peterson2 Author Orcid Image ;   Kyoung Yul Seo3 Author Orcid Image ;   Hyun Wook Han4 Author Orcid Image ;   Jee In Kang5 Author Orcid Image
Article Authors Cited by (14) Tweetations (3) Metrics

Journals

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  2. Majcherek D, Kowalski A, Lewandowska M. Lifestyle, Demographic and Socio-Economic Determinants of Mental Health Disorders of Employees in the European Countries. International Journal of Environmental Research and Public Health 2022;19(19):11913 View
  3. Zainal N, Newman M. Prospective network analysis of proinflammatory proteins, lipid markers, and depression components in midlife community women. Psychological Medicine 2023;53(11):5267 View
  4. Liu X, Ji X, Weng X, Zhang Y. Artificial intelligence ecosystem for computational psychiatry: Ideas to practice. World Journal of Meta-Analysis 2023;11(4):79 View
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  6. Muehlensiepen F, Petit P, Knitza J, Welcker M, Vuillerme N. Prediction of the acceptance of telemedicine among rheumatic patients: a machine learning-powered secondary analysis of German survey data. Rheumatology International 2024;44(3):523 View
  7. Bhak Y, Ahn T, Peterson T, Han H, Nam S. Machine Learning Models for Low Back Pain Detection and Factor Identification: Insights From a 6-Year Nationwide Survey. The Journal of Pain 2024;25(8):104497 View
  8. Iovoli F, Hall M, Nenadic I, Straube B, Alexander N, Jamalabadi H, Jansen A, Stein F, Brosch K, Thomas-Odenthal F, Usemann P, Teutenberg L, Wroblewski A, Pfarr J, Thiel K, Flinkenflügel K, Meinert S, Grotegerd D, Hahn T, Goltermann J, Gruber M, Repple J, Enneking V, Winter A, Dannlowski U, Kircher T, Rubel J. Exploring the complex interrelation between depressive symptoms, risk, and protective factors: A comprehensive network approach. Journal of Affective Disorders 2024;355:12 View
  9. Shapiro R, Muenzel E, Nicholson R, Zagar A, L. Reed M, Buse D, Hutchinson S, Ashina S, Pearlman E, Lipton R. Factors and Reasons Associated with Hesitating to Seek Care for Migraine: Results of the OVERCOME (US) Study. Neurology and Therapy 2025;14(1):135 View
  10. Todd E, Orr R, Gamage E, West E, Jabeen T, McGuinness A, George V, Phuong-Nguyen K, Voglsanger L, Jennings L, Radovic L, Angwenyi L, Taylor S, Khosravi A, Jacka F, Dawson S. Lifestyle factors and other predictors of common mental disorders in diagnostic machine learning studies: A systematic review. Computers in Biology and Medicine 2025;185:109521 View
  11. Chen Z, Liu H, Zhang Y, Xing F, Jiang J, Xiang Z, Duan X. Identifying major depressive disorder among US adults living alone using stacked ensemble machine learning algorithms. Frontiers in Public Health 2025;13 View
  12. Liu X, Chen M, Ji Y, Chen H, Lin Y, Xiao Z, Guan Q, Ou W, Wang Y, Xiao Q, Huang X, Zhang J, Huang Y, Yu Q, Jiang M. Identifying depression with mixed features: the potential value of eye-tracking features. Frontiers in Neurology 2025;16 View

Books/Policy Documents

  1. Magboo V, Magboo M. Well-Being in the Information Society: When the Mind Breaks. View
  2. Yun J, Kim Y. Recent Advances and Challenges in the Treatment of Major Depressive Disorder. View