论文标题
深层生存机器：具有竞争风险的审查数据的完全参数生存回归和代表性学习
Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks
论文作者
论文摘要
我们描述了一种新的方法，以完全参数方式估算审查数据的事实预测问题的相对风险。我们的方法不需要按照Cox-Prortation危害模型的要求对基础生存分布的恒定比例危害做出强有力的假设。通过共同学习输入协变量的深层非线性表示，我们通过对具有不同级别审查水平的多个现实世界数据集进行广泛的实验来估算生存风险时，证明了我们的方法的好处。我们在竞争风险方案中进一步证明了我们的模型的优势。据我们所知，这是第一项涉及在审查存在下具有竞争风险的生存时间完全参数估计的工作。
We describe a new approach to estimating relative risks in time-to-event prediction problems with censored data in a fully parametric manner. Our approach does not require making strong assumptions of constant proportional hazard of the underlying survival distribution, as required by the Cox-proportional hazard model. By jointly learning deep nonlinear representations of the input covariates, we demonstrate the benefits of our approach when used to estimate survival risks through extensive experimentation on multiple real world datasets with different levels of censoring. We further demonstrate advantages of our model in the competing risks scenario. To the best of our knowledge, this is the first work involving fully parametric estimation of survival times with competing risks in the presence of censoring.
