AI 中文总结
针对从纵向临床数据理解疾病轨迹的难题，提出以时间图建模多变量疾病轨迹，用对比图神经网络学习表示，借结构感知随机游走引导对比学习，实现相似疾病进展模式患者的稳健聚类并揭示数据潜在结构。
AI 中文摘要
从纵向临床数据理解疾病轨迹具有挑战性。本文提出一个对比表示学习框架，将多变量疾病轨迹建模为时间图，用对比图神经网络学习表示。节点代表患者随时间的观察，边捕捉轨迹间的时间连续性和结构相似性。结构感知随机游走引导对比学习生成嵌入，能对疾病进展模式相似的患者进行稳健聚类并揭示纵向数据中的潜在结构。
英文摘要
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.
