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

Journal: Journal of medical Internet research
PMID: 30994461

Abstract

BACKGROUND: Virtually, all organisms on Earth have their own circadian rhythm, and humans are no exception. Circadian rhythms are associated with various human states, especially mood disorders, and disturbance of the circadian rhythm is known to be very closely related. Attempts have also been made to derive clinical implications associated with mood disorders using the vast amounts of digital log that is acquired by digital technologies develop and using computational analysis techniques.

Authors

  • Chul-Hyun Cho
    Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Republic of Korea.
  • Taek Lee
    Sungshin University, Department of Convergence Security Engineering, Seoul, Republic of Korea.
  • Min-Gwan Kim
    Korea University College of Informatics, Department of Computer Science and Engineering, Seoul, Republic of Korea.
  • Hoh Peter In
    Korea University College of Informatics, Department of Computer Science and Engineering, Seoul, Republic of Korea.
  • Leen Kim
    Korea University College of Medicine, Department of Psychiatry, Seoul, Republic of Korea.
  • Heon-Jeong Lee
    Department of Psychiatry, Korea University College of Medicine, Seoul, Republic of Korea.

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