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

Mobile Health (mhealth) (2939) Digital Biomarkers and Digital Phenotyping (191) Wearable Devices and Sensors (888) Depression and Mood Disorders; Suicide Prevention (1363)

Published on in Vol 21, No 4 (2019): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11029, first published .
Mood Prediction of Patients With Mood Disorders by Machine Learning Using Passive Digital Phenotypes Based on the Circadian Rhythm: Prospective Observational Cohort Study

Mood Prediction of Patients With Mood Disorders by Machine Learning Using Passive Digital Phenotypes Based on the Circadian Rhythm: Prospective Observational Cohort Study

Mood Prediction of Patients With Mood Disorders by Machine Learning Using Passive Digital Phenotypes Based on the Circadian Rhythm: Prospective Observational Cohort Study

Authors of this article:

Chul-Hyun Cho1 Author Orcid Image ;   Taek Lee2 Author Orcid Image ;   Min-Gwan Kim3 Author Orcid Image ;   Hoh Peter In3 Author Orcid Image ;   Leen Kim1 Author Orcid Image ;   Heon-Jeong Lee1 Author Orcid Image
Article Authors Cited by (96) Tweetations (20) Metrics

Chul-Hyun Cho   1 * , MD, PhD ;   Taek Lee   2 * , PhD ;   Min-Gwan Kim   3 , MS ;   Hoh Peter In   3 , PhD ;   Leen Kim   1 , MD, PhD ;   Heon-Jeong Lee   1 , MD, PhD

1 Korea University College of Medicine, Department of Psychiatry, Seoul, Republic of Korea

2 Sungshin University, Department of Convergence Security Engineering, Seoul, Republic of Korea

3 Korea University College of Informatics, Department of Computer Science and Engineering, Seoul, Republic of Korea

*these authors contributed equally

Corresponding Author:

  • Heon-Jeong Lee, MD, PhD
  • Korea University College of Medicine
  • Department of Psychiatry
  • Anam Hospital, Korea University College of Medicine
  • Seoul
  • Republic of Korea
  • Phone: 82 029205815
  • Email: leehjeong@korea.ac.kr