Survival analysis plays a crucial role in estimating the likelihood of future events for patients by modeling time-to-event data, particularly in healthcare settings where predictions about outcomes such as death and disease recurrence are essential. However, this analysis poses challenges due to the presence of censored data, where time-to-event information is missing for certain data points. Yet, censored data can offer valuable insights, provided we appropriately incorporate the censoring time during modeling. In this paper, we propose SurvCORN, a novel method utilizing conditional ordinal ranking networks to predict survival curves directly. Additionally, we introduce SurvMAE, a metric designed to evaluate the accuracy of model predictions in estimating time-to-event outcomes. Through empirical evaluation on two real-world cancer datasets, we demonstrate SurvCORN's ability to maintain accurate ordering between patient outcomes while improving individual time-to-event predictions. Our contributions extend recent advancements in ordinal regression to survival analysis, offering valuable insights into accurate prognosis in healthcare settings.

本研究解决了生存分析中由于审查数据缺失导致的预测挑战，提出了一种新方法SurvCORN，利用条件有序排名网络直接预测生存曲线。实验结果表明，SurvCORN在保持患者结果准确排序的同时，显著提高了个体事件发生时间的预测精度，推动了有序回归在生存分析中的应用。

SurvCORN：基于条件有序排名神经网络的生存分析