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

Sang Min Nam   1 * , MD, PhD ;   Thomas A Peterson   2 * , PhD ;   Kyoung Yul Seo   3 , MD, PhD ;   Hyun Wook Han   4 , MD, PhD ;   Jee In Kang   5 , MD, PhD

1 Department of Ophthalmology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea

2 UCSF REACH Informatics Core, Department of Orthopaedic Surgery, Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, United States

3 Department of Ophthalmology, Institute of Vision Research, Eye and Ear Hospital, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea

4 Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam, Republic of Korea

5 Department of Psychiatry, Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea

*these authors contributed equally

Corresponding Author:

  • Jee In Kang, MD, PhD
  • Department of Psychiatry
  • Institute of Behavioral Science in Medicine
  • Yonsei University College of Medicine
  • 50-1 Yonsei-ro, Seodaemun-gu
  • Seoul, 03722
  • Republic of Korea
  • Phone: 82 2-2228-1620
  • Fax: 82 2-313-0891
  • Email: jeeinkang@yuhs.ac