gnn-transformer-fusion

You can predict disease progression by applying a GNN-Transformer fusion pipeline to longitudinal brain connectivity data. This approach uses cross-modal attention to jointly encode brain graphs and tabular clinical data for temporal forecasting.

Multi-modal fusion handles these distinct data structures by using cross-modal attention to jointly encode non-Euclidean brain graphs and Euclidean tabular data, creating an integrated representation for end-to-end predictive modeling.

Yes, you can use a time-aware Transformer-based fusion layer to capture temporal dynamics across multiple clinical visits. This enables longitudinal predictions for cognitive trajectories and treatment response.

The best way to fuse these data types is an end-to-end pipeline utilizing cross-modal attention. This architecture simultaneously processes brain connectivity graphs and clinical demographics to generate time-aware longitudinal predictions.

Yes, this approach specifically supports forecasting cognitive trajectories. By fusing longitudinal brain connectivity with clinical data through time-aware transformers, it models temporal dynamics across multiple patient visits.

The primary limitation is the requirement for longitudinal multi-visit data to effectively train the time-aware transformer layer. Without multiple temporal data points, the cross-modal attention mechanism cannot capture dynamic progression.