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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

Exploratory analyses were conducted as a bottom-up approach where all potential passive sensing features were included in the same model to see which features emerged as most predictive. The pursuit of these 3 aims combined to allow us to examine the utility of predicting depressive symptoms within a sample of college students using the Light GBM method, contributing to the knowledge base regarding ML and depression detection.

Jessica L Borelli, Yuning Wang, Frances Haofei Li, Lyric N Russo, Marta Tironi, Ken Yamashita, Elayne Zhou, Jocelyn Lai, Brenda Nguyen, Iman Azimi, Christopher Marcotullio, Sina Labbaf, Salar Jafarlou, Nikil Dutt, Amir Rahmani

JMIR Form Res 2025;9:e67964