论文标题
深度：通过深度学习预测事件时间的预测
DeepCENT: Prediction of Censored Event Time via Deep Learning
论文作者
论文摘要
随着深度学习的快速发展，已经开发了许多计算方法来通过深度学习方法分析非线性和复杂的权利审查数据。但是，大多数方法着重于预测生存函数或危害功能，而不是预测事件的单个有价值时间。在本文中，我们提出了一种新颖的方法，即直接预测事件的个人时间。它利用深度学习框架具有创新的损失函数，将均方误差和一致性指数结合在一起。最重要的是，DeepCent可以处理竞争风险，其中一种事件排除了其他类型的事件被观察到。使用仿真研究评估了DeepCent的有效性和优势，并用三个公开可用的癌症数据集进行了说明。
With the rapid advances of deep learning, many computational methods have been developed to analyze nonlinear and complex right censored data via deep learning approaches. However, the majority of the methods focus on predicting survival function or hazard function rather than predicting a single valued time to an event. In this paper, we propose a novel method, DeepCENT, to directly predict the individual time to an event. It utilizes the deep learning framework with an innovative loss function that combines the mean square error and the concordance index. Most importantly, DeepCENT can handle competing risks, where one type of event precludes the other types of events from being observed. The validity and advantage of DeepCENT were evaluated using simulation studies and illustrated with three publicly available cancer data sets.
