论文标题
深内核生存分析和特定主题的生存时间预测间隔
Deep Kernel Survival Analysis and Subject-Specific Survival Time Prediction Intervals
论文作者
论文摘要
内核生存分析方法使用有关哪些训练对象与测试受试者最相似的信息预测特定主体的生存曲线和时间。这些最相似的培训科目可以作为预测证据。内核函数给出了任何两个主题的相似性。在本文中，我们提出了第一个学习内核在内核生存分析中使用的神经网络框架。我们还展示了如何使用内核函数来构建对类似于测试主题的个体有效的生存时间估计的预测间隔。这些预测间隔可以使用任何内核功能，例如使用我们的神经内核学习框架或使用随机生存森林学习的函数。我们的实验表明，我们的神经内核存活估计量具有各种现有生存分析方法的竞争力，并且我们的预测间隔也可以帮助比较不同方法的不确定性，即使对于不使用核的估计器也是如此。特别是，这些预测间隔宽度可以用作生存分析方法的新绩效指标。
Kernel survival analysis methods predict subject-specific survival curves and times using information about which training subjects are most similar to a test subject. These most similar training subjects could serve as forecast evidence. How similar any two subjects are is given by the kernel function. In this paper, we present the first neural network framework that learns which kernel functions to use in kernel survival analysis. We also show how to use kernel functions to construct prediction intervals of survival time estimates that are statistically valid for individuals similar to a test subject. These prediction intervals can use any kernel function, such as ones learned using our neural kernel learning framework or using random survival forests. Our experiments show that our neural kernel survival estimators are competitive with a variety of existing survival analysis methods, and that our prediction intervals can help compare different methods' uncertainties, even for estimators that do not use kernels. In particular, these prediction interval widths can be used as a new performance metric for survival analysis methods.
