AI-Based Mental Health Assessment for Adolescents Using Their Daily Digital Activities

Abstract

Adolescents and their parents hesitate to acknowledge mental health issues until symptoms severely worsen, making timely treatment challenging. Moreover, infrequent psychiatric consultations often fail to adjust treatments to the dynamic nature of mental health states. To address these issues, our paper proposes an AI-based mental health assessment framework for adolescent mental health through non-invasively collected data from daily digital activities on their mobile devices, including tablets and smartphones. For this, we collect fifteen different types of passive sensor data across three primary categories of activities: studying, smartphone using, and metaverse gaming. Additionally, each adolescent completes self-survey reports on eight different disorders which are used as labels. Then, feature extraction is conducted based on this dataset, which yields 1,523 features that could function as potential digital biomarkers of mental health conditions in adolescents. Utilizing these features, our algorithm named CAMP: Customizable Automated Machine learning Process incorporates simulated annealing for feature selection. This approach enables the construction of AI models for mental health assessment that are finely tuned to domain specific strategies. Our experiments show that our proposed framework can significantly improve models' performance.

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