Leveraging Multimodal Machine Learning for Predictive Diagnostics of Adolescent Mental Health Disorders

Abstract

Adolescent mental health disorders present significant public health challenges due to their increasing prevalence and the complexity of early diagnosis. We introduce a novel multimodal machine learning framework that integrates data from social media, wearable devices, academic records, and peer interactions to predict early signs of mental health disorders. This approach uses advanced techniques for data fusion and achieves high diagnostic accuracy, outperforming traditional methods. Extensive validation shows strong performance across multiple metrics. Future work will enhance real-time diagnostic capabilities and model robustness. This framework holds promise for improving early detection and intervention in adolescent mental health.