Advancing Mental Health Diagnostics Through the Fusion of Multimodal Data with CNN and LSTM Techniques

Abstract

Multimodal assessment of mental health disorders is an important problem to solve since millions of people suffer from disorders while approaches to detect them are often time-consuming and non-objective. This work aims to develop advanced diagnostic approaches by using the additional modalities like speech, facial expression, and physiological data to include the complex and multiple signals of psychopathology. In this paper, synthetic spatial features are extracted using the Convolutional Neural Networks (CNNs) while the temporal features are adopted by utilizing the Long Short-Term Memory (LSTM) network to solve both spatial and sequential data problems. When integrating these models, we obtain a solid diagnostic system that offers more profound analysis of diverse mental health conditions. The findings also corroborate that the fusion model has higher accuracy and reliability than single modal and single technique-based approaches in different mental health conditions. What is more it not only refining diagnostics but also creates new opportunities for early detection and individual approach in immediate treatment. The work underlines how AI improves and speeds up mental health diagnostic opportunities and underlines the necessity of using a multi-modal approach for the assessment and management of patients’ conditions.