Published on in Vol 28 (2026)

This is a member publication of Imperial College London (Jisc)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/72501, first published .
Digital Phenotyping for Adolescent Mental Health: Feasibility Study Using Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data

Digital Phenotyping for Adolescent Mental Health: Feasibility Study Using Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data

Digital Phenotyping for Adolescent Mental Health: Feasibility Study Using Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data

Balasundaram Kadirvelu   1 * , PhD ;   Teresa Bellido Bel   2 * , MD ;   Aglaia Freccero   2 , MSc ;   Martina Di Simplico   2 , PhD, MD ;   Dasha Nicholls   2 , MD ;   A Aldo Faisal   1, 3 , PhD

1 Brain & Behaviour Lab, Department of Computing and Department of Bioengineering, Imperial College London, London, United Kingdom

2 Division of Psychiatry, Department of Brain Sciences, Imperial College London, London, England, United Kingdom

3 Chair in Digital Health & Data Science, Faculty of Life Sciences, University of Bayreuth, Bayreuth, Bavaria, Germany

*these authors contributed equally

Corresponding Author:

  • A Aldo Faisal, PhD
  • Brain & Behaviour Lab, Department of Computing and Department of Bioengineering
  • Imperial College London
  • Royal School of Mines
  • London SW72AZ
  • United Kingdom
  • Phone: 44 20 7594 6373
  • Email: a.faisal@imperial.ac.uk