Entropy-Guided Graph Neural Networks for Cognitive Brain Disorder Detection from Speech Transcripts

Early and accurate diagnosis of brain diseases such as dementia, autism, and right hemisphere damage is crucial for effective intervention and treatment. Traditional diagnostic approaches often rely on clinical assessments, which can be time-consuming and subjective. In this study, we propose a methodology leveraging graph neural networks (GNNs) for brain disease classification based on speech transcripts or textual data. Our approach incorporates a subject similarity graph with an entropy-guided reinforcement regularisation mechanism to model complex linguistic patterns and capture inter-subject relationships, improving discrimination between neurological conditions. Experimental results demonstrate consistent performance gains over strong baseline models. The proposed framework provides a cost-effective and computationally efficient diagnostic tool with potential applicability to early-stage clinical decision support.