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

Artificial Intelligence (1374) Machine Learning (1467) Public (e)Health, Digital Epidemiology and Public Health Informatics (725) Depression and Mood Disorders; Suicide Prevention (1364)

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 (11) Tweetations (3) Metrics

Journals

  1. Hu Y, Yang T, Zhang J, Wang X, Cui X, Chen N, Zhou J, Jiang F, Zhu J, Zou J. Dynamic Prediction of Mechanical Thrombectomy Outcome for Acute Ischemic Stroke Patients Using Machine Learning. Brain Sciences 2022;12(7):938 View
  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
  5. Hosseinzadeh Kasani P, Lee J, Park C, Yun C, Jang J, Lee S. Evaluation of nutritional status and clinical depression classification using an explainable machine learning method. Frontiers in Nutrition 2023;10 View
  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 2024 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