Survival regression aims to predict the time when an event of interest will take place, typically a death or a failure. A fully parametric method [18] is proposed to estimate the survival function as a mixture of individual parametric distributions in the presence of censoring. In this paper, We present a novel method to predict the survival time by better clustering the survival data and combine primitive distributions. We propose two variants of variational auto-encoder (VAE), discrete and continuous, to generate the latent variables for clustering input covariates. The model is trained end to end by jointly optimizing the VAE loss and regression loss. Thorough experiments on dataset SUPPORT and FLCHAIN show that our method can effectively improve the clustering result and reach competitive scores with previous methods. We demonstrate the superior result of our model prediction in the long-term. Our code is available at https://github.com/qinzzz/auton-survival-785.

翻译：生存回归旨在预测目标事件发生的时间，通常为死亡或故障。本文提出一种全参数化方法 [18]，在存在删失的情况下，将生存函数估计为个体参数分布的混合。我们提出了一种新方法，通过更好地对生存数据进行聚类并组合基础分布来预测生存时间。我们提出了两种变分自编码器（VAE）变体——离散型与连续型——用于生成输入协变量的聚类潜变量。模型通过联合优化VAE损失与回归损失进行端到端训练。在SUPPORT和FLCHAIN数据集上的充分实验表明，我们的方法能有效改进聚类结果，并与先前方法达到具有竞争力的得分。我们展示了模型在长期预测中的优越性能。代码开源于 https://github.com/qinzzz/auton-survival-785。