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

Pilot studies (ehealth) (321) Depression and Mood Disorders; Suicide Prevention (1683) Mental Health Issues in Adolescence (632) Wearable Devices and Sensors (1132) Machine Learning (2070)

Published on in Vol 9 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67964, first published .
Detection of Depressive Symptoms in College Students Using Multimodal Passive Sensing Data and Light Gradient Boosting Machine: Longitudinal Pilot Study

Detection of Depressive Symptoms in College Students Using Multimodal Passive Sensing Data and Light Gradient Boosting Machine: Longitudinal Pilot Study

Detection of Depressive Symptoms in College Students Using Multimodal Passive Sensing Data and Light Gradient Boosting Machine: Longitudinal Pilot Study

Authors of this article:

Jessica L Borelli1 Author Orcid Image ;   Yuning Wang2 Author Orcid Image ;   Frances Haofei Li1 Author Orcid Image ;   Lyric N Russo1 Author Orcid Image ;   Marta Tironi3 Author Orcid Image ;   Ken Yamashita1 Author Orcid Image ;   Elayne Zhou4 Author Orcid Image ;   Jocelyn Lai1 Author Orcid Image ;   Brenda Nguyen1 Author Orcid Image ;   Iman Azimi1 Author Orcid Image ;   Christopher Marcotullio1 Author Orcid Image ;   Sina Labbaf1 Author Orcid Image ;   Salar Jafarlou1 Author Orcid Image ;   Nikil Dutt1 Author Orcid Image ;   Amir Rahmani1 Author Orcid Image
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Journals

  1. Ba H, Du H, Cheng C, Zhang Y, Ruan L, Chen Z. Identification of Syndrome Types in Patients With Pancreatic Cancer From Free Text in Electronic Medical Records: Model Development and Validation. JMIR Formative Research 2025;9:e70602 View