Mental Health Evaluation using Multimodal Deep Learning with Attention Mechanisms

The latest advances in multimodal machine learning and deep learning have greatly impacted mental health assessment, especially depression and bipolar disorder diagnosis. This paper examines the application of multimodal inputs such as text, audio, visual, and physiology in interpreting and identifying mental health states. Based on datasets like E-DAIC, which have textual and facial features merged, the new approach utilizes attention mechanisms, deep neural networks, transfer learning, and data augmentation to increase diagnostic accuracy. The research compares three approaches: text features, face features, and their fusion and concludes that merging text information with the analysis of speech quality provides the best outcomes. The suggested multimodal model had a mean absolute error of 2.85 and a root mean square error of 4.02, better than all the unimodal baselines. Though promising, the application of multimodal learning to mental health is still hampered by issues such as data alignment, ethics, and scarcity of large-scale labeled datasets. This study emphasizes the need for choosing modality-specific data when developing effective systems for the assessment of mental health.