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

Digital Biomarkers and Digital Phenotyping (191) Clinical Information and Decision Making (1407) Decision Support for Health Professionals (1185) Machine Learning (1465) Ecological Momentary Assessment (EMA) (309) Depression and Mood Disorders; Suicide Prevention (1363)

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 Kim1, 2 Author Orcid Image ;   SungHee Lee3 Author Orcid Image ;   SangEun Lee4 Author Orcid Image ;   Soyun Hong1 Author Orcid Image ;   HeeJae Kang3 Author Orcid Image ;   Namhee Kim1 Author Orcid Image
Article Authors Cited by (74) Tweetations (8) Metrics

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Books/Policy Documents

  1. Verhagen S, van Os J, Delespaul P. Mental Health in a Digital World. View
  2. Tumanov K, Spanakis G. Explainable AI Within the Digital Transformation and Cyber Physical Systems. View
  3. Devi D, Naresh R, Kumar C, Senthilkumar S, Jovin A. Technological Tools for Predicting Pregnancy Complications. View