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

Development and Evaluation of Research Methods, Instruments and Tools (511) Ubiquitous Health (uHealth) (125) Wearable Devices and Sensors (903) mHealth for Data Collection and Research (981) Machine Learning (1501) Depression and Mood Disorders; Suicide Prevention (1392) Diagnostic Tools in Mental Health (248)

Published on in Vol 6, No 6 (2022): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/35807, first published .
Predicting Depression in Adolescents Using Mobile and Wearable Sensors: Multimodal Machine Learning–Based Exploratory Study

Predicting Depression in Adolescents Using Mobile and Wearable Sensors: Multimodal Machine Learning–Based Exploratory Study

Predicting Depression in Adolescents Using Mobile and Wearable Sensors: Multimodal Machine Learning–Based Exploratory Study

Authors of this article:

Tahsin Mullick1 Author Orcid Image ;   Ana Radovic2 Author Orcid Image ;   Sam Shaaban3 Author Orcid Image ;   Afsaneh Doryab1 Author Orcid Image
Article Authors Cited by (31) Tweetations (4) Metrics

Journals

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  22. Shvetcov A, Funke Kupper J, Zheng W, Slade A, Han J, Whitton A, Spoelma M, Hoon L, Mouzakis K, Vasa R, Gupta S, Venkatesh S, Newby J, Christensen H. Passive sensing data predicts stress in university students: a supervised machine learning method for digital phenotyping. Frontiers in Psychiatry 2024;15 View
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Books/Policy Documents

  1. Avasthi S, Sanwal T, Prakash A, Tripathi S. Multimodal Biometric and Machine Learning Technologies. View