用Transformer处理纵向数据，提升生存分析准确性和校准度
TraCeR: Transformer-Based Competing Risk Analysis with Longitudinal Covariates
- 基于因子化自注意力机制，建模时间序列医疗数据的动态变化
- 在多个真实数据集上显著优于现有方法，校准性能提升明显
- 适合医学预测场景，尤其关注长期随访与竞争风险问题
生存分析是建模生存时间数据的关键工具。近年来基于深度学习的方法减少了比例风险、线性关系等假设，但对纵向协变量的建模仍存在挑战，多数工作仅关注横断面特征，且评估侧重区分能力而忽视校准性。本文提出TraCeR，一种基于Transformer的生存分析框架，可有效整合纵向协变量。其基于因子化自注意力结构，从一系列测量值中估计风险函数，无需假设数据生成过程即可自然捕捉时序协变量交互。该框架天然支持删失数据和竞争事件。在多个真实世界数据集上的实验表明，TraCeR显著优于当前最优方法，且评估不仅涵盖区分度，还系统评估了模型校准性，弥补了文献中的关键缺失。
原文摘要 · Abstract (English)
Survival analysis is a critical tool for modeling time-to-event data. Recent deep learning-based models have reduced various modeling assumptions including proportional hazard and linearity. However, a persistent challenge remains in incorporating longitudinal covariates, with prior work largely focusing on cross-sectional features, and in assessing calibration of these models, with research primarily focusing on discrimination during evaluation. We introduce TraCeR, a transformer-based survival analysis framework for incorporating longitudinal covariates. Based on a factorized self-attention architecture, TraCeR estimates the hazard function from a sequence of measurements, naturally capturing temporal covariate interactions without assumptions about the underlying data-generating process. The framework is inherently designed to handle censored data and competing events. Experiments on multiple real-world datasets demonstrate that TraCeR achieves substantial and statistically significant performance improvements over state-of-the-art methods. Furthermore, our evaluation extends beyond discrimination metrics and assesses model calibration, addressing a key oversight in literature.
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