链式影响：在临床预测建模中追踪时间与特征之间的相互依赖性
摘要
建模临床时间序列数据受限于捕捉特征之间潜在时变依赖性的挑战。现有方法常依赖黑箱机制或简单聚合，无法显式建模一个临床变量如何随时间通过其他变量传播其影响。我们提出链式影响（Chain-of-Influence, CoI），一种可解释的深度学习框架，构建特征相互作用的显式时序展开图。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.
引用
@article{arxiv.2510.09895,
title = {Chain-of-Influence: Tracing Interdependencies Across Time and Features in Clinical Predictive Modelings},
author = {Yubo Li and Rema Padman},
journal= {arXiv preprint arXiv:2510.09895},
year = {2026}
}