Modeling clinical time-series data is hampered by the challenge of capturing latent, time-varying dependencies among features. State-of-the-art approaches often rely on black-box mechanisms or simple aggregation, failing to explicitly model how the influence of one clinical variable propagates through others over time. We propose $\textbf{Chain-of-Influence (CoI)}$, an interpretable deep learning framework that constructs an explicit, time-unfolded graph of feature interactions. CoI enables the tracing of influence pathways, providing a granular audit trail that shows how any feature at any time contributes to the final prediction, both directly and through its influence on other variables. We evaluate CoI on mortality and disease progression tasks using the MIMIC-IV dataset and a chronic kidney disease cohort. Our framework achieves state-of-the-art predictive performance (AUROC of 0.960 on CKD progression and 0.950 on ICU mortality), with deletion-based sensitivity analyses confirming that CoI's learned attributions faithfully reflect its decision process. Through case studies, we demonstrate that CoI uncovers clinically meaningful, patient-specific patterns of disease progression, offering enhanced transparency into the temporal and cross-feature dependencies that inform clinical decision-making.

翻译：临床时间序列数据的建模面临着一个挑战：如何捕捉特征之间潜在的、随时间变化的依赖性。现有先进方法通常依赖于黑箱机制或简单聚合，未能显式地建模一个临床变量如何随时间推移通过其他变量传播其影响。我们提出了$\textbf{影响链 (CoI)}$，这是一个可解释的深度学习框架，它构建了一个显式的、时间展开的特征交互图。CoI 能够追踪影响路径，提供一个细粒度的审计轨迹，展示任何时间点的任何特征如何直接或通过影响其他变量来对最终预测做出贡献。我们使用 MIMIC-IV 数据集和一个慢性肾脏病队列，在死亡率和疾病进展任务上评估 CoI。我们的框架实现了最先进的预测性能（在 CKD 进展任务上 AUROC 为 0.960，在 ICU 死亡率任务上为 0.950），基于删除的敏感性分析证实了 CoI 学习到的归因忠实地反映了其决策过程。通过案例研究，我们证明 CoI 能够揭示具有临床意义的、患者特定的疾病进展模式，为临床决策所依据的时间及跨特征依赖性提供了更强的透明度。