这篇论文把临床流程做成图结构来预测生存时间，比传统方法更准，适合做医疗AI的朋友看看。
ChronoSurv是一个基于有向图的框架，用于头颈癌患者的多模态生存预测。它将患者诊疗过程建模为按诊断步骤对齐的临床轨迹，并通过分层拓扑整合细粒度、粗粒度和全局表示。在两个公开数据集上，ChronoSurv实现了优于现有方法的判别性能，且校准误差达到统计显著水平。消融实验验证了各组件对整体性能的贡献。
ChronoSurv: A Clinical Pathway-Guided Graph Framework for Multimodal Survival Analysis
Accurate survival prediction is essential for personalized treatment planning in head and neck cancer, yet remains challenging due to the heterogeneous and high-dimensional nature of multimodal clinical data. While deep survival models have improved predictive performance over classical statistical approaches, existing methods typically rely on static fusion strategies or temporally agnostic modeling, limiting their ability to capture structured clinical workflows. In this work, we propose ChronoSurv, a heterogeneous hierarchical directed graph framework for multimodal survival analysis. ChronoSurv represents patient care as a progression-aware clinical trajectory using directed graphs aligned with key diagnostic steps. A hierarchical topology incorporates fine-grained, coarse, and global representations, further supporting flexible adaptation to missing modalities, while heterogeneous message passing models complex and asymmetric relationships across modalities and clinical steps. Experimental results on two public datasets demonstrate that ChronoSurv achieves state-of-the-art discriminative performance while maintaining statistically reliable calibration. Comprehensive ablation studies further confirm the contribution of each architectural component, highlighting the potential of trajectory-aware graph modeling for multimodal survival prediction.