Figure 3.

Attention-guided message passing for dynamic medical graphs. This figure illustrates the framework of DMGF, which employs an attention-guided message passing mechanism to enhance interpretability in medical interactions. The model utilizes local and global similarity computations to refine textual and visual representations, pooling these features through an adaptive focal loss function. A temporal graph convolutional module with attention-based weight assignments dynamically refines node interactions over time, incorporating gated recurrent units (GRUs) for modeling temporal dependencies. The process culminates in a readout function, aggregating node embeddings with learned importance weights to generate final predictions. Regularization techniques enforce temporal smoothness, ensuring stability and clinical relevance in predictions.