用排序网络直接预测生存曲线，提升医疗预后准确性
SurvCORN: Survival Analysis with Conditional Ordinal Ranking Neural Network
- 基于条件序数排序网络建模生存数据，直接输出生存曲线
- 在两个癌症数据集上实现更准确的事件时间预测与排序
- 适合关注医疗预后、生存分析的临床研究者与算法开发者
生存分析在医疗领域对患者未来事件（如死亡、疾病复发）的发生概率估计至关重要，需建模生存时间数据。然而，由于存在删失数据（部分患者未发生事件），传统方法面临挑战。本文提出SurvCORN，一种利用条件序数排序神经网络直接预测生存曲线的新方法，并引入新评估指标SurvMAE以衡量模型对事件时间预测的准确性。在两个真实世界癌症数据集上的实证表明，SurvCORN不仅能保持患者间事件顺序的正确性，还显著提升了个体事件时间的预测精度。该工作将序数回归进展拓展至生存分析，为精准医疗预后提供新思路。
原文摘要 · Abstract (English)
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.
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