A Clinical Decision Support System Prototype for Multimodal Youth Mental Health Monitoring

Abstract

Mental health problems among young people, including anxiety and depression, are a growing public health challenge, and emerging digital tools offer new opportunities for personalised care. This paper presents a prototype of a Clinical Decision Support System (CDSS) designed to support longitudinal multimodal monitoring of adolescents’ mental health using data from two mobile applications. We present the system’s multimodal data framework, which integrates Experience Sampling Method (ESM) self-reports, standardised Patient-Reported Outcome Measures (PROMs), behavioural cues from audio-visual diary recordings, and serious game–derived metrics. We demonstrate the practical feasibility of combining all these metrics within a single, explainable decision support platform providing clinicians with interactive visual analytics to explore data and interpret explainable predictions. A small-scale expert preliminary evaluation (N=10) highlighted the CDSS’ potential for mental health monitoring and informed clinical practice. Future work will leverage real-world data from an ongoing multisite pilot study to train and validate CDSS explainable predictive models.