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

Digital Biomarkers and Digital Phenotyping (75) Clinical Information and Decision Making (405) Decision Support for Health Professionals (348) Machine Learning (465) Ecological Momentary Assessment (EMA) (154) Depression and Mood Disorders; Suicide Prevention (651)

Published on in Vol 7, No 10 (2019): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/14149, first published .
Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone

Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone

Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone

Authors of this article:

Heejung Kim 1, 2 Author Orcid Image ;   SungHee Lee 3 Author Orcid Image ;   SangEun Lee 4 Author Orcid Image ;   Soyun Hong 2 Author Orcid Image ;   HeeJae Kang 3 Author Orcid Image ;   Namhee Kim 2 Author Orcid Image
Article Authors Cited by (12) Tweetations (8) Metrics
Heejung Kim 1, 2, GNP-BC, RN, PhD;  SungHee Lee 3, PhD;  SangEun Lee 4, PhD;  Soyun Hong 2, RN, MSN;  HeeJae Kang 3, BS;  Namhee Kim 2, RN, MPH

1 Mo-Im Kim Nursing Research Institute, Yonsei University , Seoul , KR

2 College of Nursing, Yonsei University , Seoul , KR

3 BRFrame Inc , Seoul , KR

4 Health-IT Acceleration Platform Technology Innovation Center, College of Medicine, Yonsei University Health System, Seoul , KR

Corresponding Author:

  • Heejung Kim, GNP-BC, RN, PhD
  • College of Nursing
  • Yonsei University
  • Room #603
  • 50-1 Yonsei-ro, Seodaemun-gu
  • Seoul
  • KR
  • Phone: 82 2 2228 3273
  • Email: hkim80@yuhs.ac