用图神经网络学习疾病发展轨迹，揭示患者病情演变模式。
Contrastive Representation Learning of Longitudinal Disease Trajectories on Temporal Graphs
- 将病程数据建模为时间图，节点为时序观测，边表征时间连续性
- 通过结构感知随机游走增强对比学习，保留时间上下文与轨迹拓扑
- 可有效聚类相似病程患者，适合临床轨迹分析与个性化医疗研究
从纵向临床数据中理解疾病轨迹仍面临复杂的时间动态和异质患者群体的挑战。本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，利用对比图神经网络学习表示。节点代表随时间变化的患者观测，边捕捉轨迹间的时间连续性与结构相似性。结构感知随机游走引导对比学习，生成保留时间上下文与轨迹拓扑的嵌入表示。结果表示能实现对具有相似疾病进展模式患者的稳健聚类，并揭示纵向数据中的潜在结构。
原文摘要 · Abstract (English)
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.
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