Advanced Deep Learning for Multi-Modal Mental Health Disorder Detection: A Framework Synthesizing Behavioural, Textual, and Physiological Data

Abstract

Mental health disorders are a significant and escalating global health concern. But their diagnosis is mostly based on subjective clinical evaluations and symptoms that they report themselves. Social stigma, delayed reporting, and limited access to mental health professionals can all have an effect on these methods. This frequently results in delayed or inaccurate diagnoses. This paper introduces a comprehensive multimodal deep learning framework for identifying mental health disorders through the simultaneous analysis of textual, behavioral, and physiological data. We use transformer-based language models to figure out what text means and what it means in context. Convolutional neural networks help us learn important things from physiological signals. Deep neural networks model signs of behavior and speech. We use explainable artificial intelligence methods to make clinical settings more open and trustworthy. These give us a clear idea of what the model thinks will happen. Experimental analysis demonstrates that the proposed multimodal fusion approach surpasses single-modality baselines on various evaluation metrics. This indicates its capacity for dependable and credible mental health evaluation.