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.

翻译：生存分析通过建模时间-事件数据，在估计患者未来事件发生可能性方面发挥着关键作用，尤其在需要对死亡、疾病复发等结局进行预测的医疗场景中至关重要。然而，由于存在删失数据（即部分数据点的时间-事件信息缺失），该分析面临挑战。但若能恰当建模中纳入删失时间，删失数据可提供有价值的洞见。本文提出SurvCORN方法，利用条件序数排序网络直接预测生存曲线。此外，我们引入SurvMAE评估指标，用于衡量模型在估计时间-事件结局时的预测准确性。通过对两个真实癌症数据集的实证评估，我们证明SurvCORN在提升个体时间-事件预测精度的同时，能保持患者结局间的准确排序关系。本研究将序数回归的最新进展拓展至生存分析领域，为医疗场景中的精准预后提供了重要见解。