most citedDigital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data

3 citations · 3 across the 1 of their papers we have counts for

collaborators

1 paper

cs.LG2025★ 3 cited

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

Balasundaram Kadirvelu, Teresa Bellido Bel, Aglaia Freccero +3

Background: Adolescents are particularly vulnerable to mental disorders, with over 75% of cases manifesting before the age of 25. Research indicates that only 18 to 34% of young pe…