🤖 AI Summary
This work addresses the challenges of modeling complex temporal dynamics and patient heterogeneity in longitudinal clinical data by proposing a temporal graph-based contrastive graph neural network approach. The method constructs multivariate disease trajectories as temporal graphs and incorporates structure-aware random walks to guide contrastive learning, effectively preserving both temporal context and trajectory topology. By uniquely integrating structure-aware random walks with contrastive learning, the framework learns expressive embeddings of patient observation nodes, significantly enhancing robust clustering of patients with similar disease progression patterns and uncovering latent evolutionary structures inherent in longitudinal clinical data.
📝 Abstract
Understanding disease trajectories from longitudinal clinical data remains challenging due to complex temporal dynamics and heterogeneous patient cohorts. Here, we present a contrastive representation learning framework that models multivariate disease trajectories as temporal graphs and learns representations using contrastive graph neural networks. Nodes represent patient observations over time, while edges capture temporal continuity and structural similarity between trajectories. Structure-aware random walks guide contrastive learning to generate embeddings that preserve temporal context and trajectory topology. The resulting representations enable robust clustering of patients with similar disease progression patterns and reveal latent structure in longitudinal data.