Optimizing Mental Health Diagnostics with Hybrid Deep Learning and Multimodal Data Fusion

Traditional means of mental health diagnosis practice rely on assessment results that are slow and variable in their nature. In this research work, we study how deep learning models, trained with multimodal data fusion methods, enhance diagnostic precision and operational efficiency, with added personalization outcomes. It builds a robust framework from various data sources such as clinical note text along with voice patterns and facial expressions that are incorporated into the machine by means of physiological signal data. We propose a method that unifies Convolutional Neural Networks (CNNs) and Transformer–based architecture with Recurrent Neural Networks (RNNs) for exploiting information from different modality. The feature and decision level data fusion used by this method, in turn, minimises the diagnosis error to its maximum. Experimental testing shows that our model outperforms conventional diagnostic procedures in terms of superior diagnostic quality with less incorrectly marked results. Explainable techniques can be applied to improve doctor understanding, increasing trust and transparency in the process of AI medical evaluation. The framework presents an ongoing assessment framework in terms of a real time evaluation, suitable enough to apply to detect mental health condition early stages as well as continued monitoring. Hybrid deep learning in conjunction with data fusion of multimodal data researchers show hybrid deep learning combined with multimodal data fusion, which tasks diagnosing mental health condition systems via data driven and scalable methods providing objective outcomes. The authors will expand the model and investigate the clinical implementation of bigger datasets.