提出新模型GRAFT，分离生存分析中的排序与校准，提升预测准确性。
GRAFT: Decoupling Ranking and Calibration for Survival Analysis
- 用线性AFT加残差网络，结合随机门控自动选特征。
- 在公开数据集上排名和校准均优于现有方法，噪声下仍稳定。
- 适合需要高精度生存预测的医学研究者使用。
生存分析受删失数据、高维特征和非线性交互影响，传统模型虽可解释且校准好，但受限于线性或预定义函数形式；深度学习模型灵活且判别力强，但生存估计常欠校准。为此，我们提出GRAFT（门控残差加速失效时间模型），一种新型AFT模型，将预后排序与生存校准解耦。其混合架构融合线性AFT与非线性残差神经网络，并引入随机门控实现自动特征选择。通过局部Kaplan-Meier估计器进行随机条件插补，优化可微的C指数对齐排序损失；校准的生存估计则在训练后简单获得。在公开基准测试中，GRAFT在判别性和校准性上均优于基线，在高噪声环境中仍保持鲁棒与稀疏性。
原文摘要 · Abstract (English)
Survival analysis is complicated by censored data, high-dimensional features, and non-linear interactions. Classical models offer interpretability and superior calibration but are restricted to linear or predefined functional forms, while deep learning models are flexible and achieve strong discriminative performance, but tend to produce poorly calibrated survival estimates. To address this trade-off, we propose GRAFT (Gated Residual Accelerated Failure Time), a novel AFT model that decouples prognostic ranking from survival calibration. GRAFT's hybrid architecture combines a linear AFT model with a non-linear residual neural network, and it also integrates stochastic gates for automatic feature selection. The model is trained by optimizing a differentiable, C-index-aligned ranking loss using stochastic conditional imputation from local Kaplan-Meier estimators, while calibrated survival estimates are obtained through simple post-training calibration. In public benchmarks, GRAFT outperforms baselines in discrimination and calibration, while remaining robust and sparse in high-noise settings.
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