Gated Multi-Graph Fusion via Graph Attention Networks for Alzheimer's Disease Detection
For researchers in AD detection, this method improves accuracy by modeling linguistic deviations and clinical heterogeneity, though it is an incremental improvement over existing graph-based approaches.
The paper proposes a Multi-View Gated Graph Attention Network for Alzheimer's Disease detection from spontaneous speech, achieving 90.00% accuracy on the ADReSSo dataset by fusing semantic, dependency, and co-occurrence graphs with an adaptive gating mechanism.
Spontaneous speech is a vital non-invasive biomarker for Alzheimer's Disease (AD), yet many systems overlook non-linear structural disruptions and clinical heterogeneity in pathological language. We propose a Multi-View Gated Graph Attention Network that transcribes audio via Automatic Speech Recognition (ASR) to construct semantic, dependency, and co-occurrence graphs, characterizing speech through a "content-structure-flow" framework. Notably, the co-occurrence graph leverages Pointwise Mutual Information (PMI) from a normative corpus to quantify narrative logic and linguistic deviation. To address symptomatic diversity, an adaptive gated fusion mechanism dynamically integrates these views. Evaluated on the ADReSSo dataset, our model achieves 90.00% accuracy. Ablation results confirm that the PMI-based graph and heterogeneity-aware gating are essential for robust classification across diverse clinical populations. Our source code is publicly available at https://github.com/opeacc/AD.