这篇论文用对比图神经网络给疾病轨迹建模，能更精细地聚类病程相似的患者，比传统方法好用。
论文提出一种对比表示学习框架，将多变量疾病轨迹建模为时间图。节点代表患者在不同时间的观察，边捕捉时间连续性和轨迹结构相似性。框架使用结构感知随机游走引导对比图神经网络，生成保留时间上下文的嵌入。这些表示能对具有相似疾病进展模式的患者进行鲁棒聚类，并揭示纵向数据中的潜在结构。实验在真实临床数据上验证了有效性。
Contrastive Representation Learning of Longitudinal Disease Trajectories on Temporal Graphs
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.