提升深度Cox模型的生存概率校准度，不损失预测能力。
Isotonic Survival Regression: Calibrated Survival Distributions from Deep Cox Models
- 用等距回归对深度Cox模型输出进行事后校准
- 在真实临床数据上显著改善生存概率校准效果
- 适合需要可靠生存概率的医疗决策场景
时间到事件数据广泛存在于生命科学和工程领域，但通常伴随删失现象，给标准机器学习方法带来挑战。深度Cox模型因其能优雅处理删失并适用于临床文本、基因序列和病理图像等非结构化数据而受到青睐。然而，其预测的生存概率常严重校准不足，限制了实际应用。本文提出一种新颖的后处理校准方法，利用等距回归对深度Cox模型的生存概率进行优化，不损害其判别能力。理论分析表明该方法具有双重稳健性和渐近校准性。在合成数据与真实临床数据上的实验验证了其有效性。
原文摘要 · Abstract (English)
Time-to-event data is widespread across the life sciences and engineering, but it is typically encountered together with censoring, which complicates the application of standard machine learning methods. Deep Cox models have emerged as a popular method for analyzing time-to-event data because they gracefully handle censoring and can be used with unstructured data such as clinical text reports, genomic sequences, and pathology images. However, their predicted survival probabilities are often poorly calibrated, thus limiting their practical utility. In this paper, we propose a novel post hoc calibration method for Deep Cox models that uses isotonic regression to refine predicted survival probabilities without affecting discriminative power. We establish favorable theoretical guarantees, including a double-robustness property and asymptotic calibration. Experiments on synthetic and real-world clinical data demonstrate the empirical effectiveness of our method.
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