Deep Kernel Survival Analysis and Subject-Specific Survival Time Prediction Intervals
This work addresses the need for more accurate and interpretable survival predictions in fields like healthcare, offering a novel way to quantify uncertainty, though it builds incrementally on kernel-based methods.
The paper introduces a neural network framework for learning kernel functions in survival analysis, enabling subject-specific survival time prediction intervals that are statistically valid for similar individuals. Experiments show the method is competitive with existing approaches and provides a new metric for comparing uncertainties across different survival analysis methods.
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.