Deep neural networks for survival prediction outper-form classical approaches in discrimination, which is the ordering of patients according to their time-of-event. Conversely, classical approaches like the Cox Proportional Hazards model display much better calibration, the correct temporal prediction of events of the underlying distribution. Especially in the medical domain, where it is critical to predict the survival of a single patient, both discrimination and calibration are important performance metrics. Here we present Discrete Calibrated Survival (DCS), a novel deep neural network for discriminated and calibrated survival prediction that outperforms competing survival models in discrimination on three medical datasets, while achieving best calibration among all discrete time models. The enhanced performance of DCS can be attributed to two novel features, the variable temporal output node spacing and the novel loss term that optimizes the use of uncensored and censored patient data. We believe that DCS is an important step towards clinical application of deep-learning-based survival prediction with state-of-the-art discrimination and good calibration.

翻译：相反,Cox成比例危险模型等典型方法显示得更好,对基本分布事件进行正确的时间预测。 特别是在医疗领域,对单一患者的生存进行预测至关重要,歧视和校准都是重要的性能衡量标准。 我们在这里展示了分辨校准生存(DCS),这是一个歧视与校准生存预测的新颖的深神经网络,在三个医疗数据集的歧视中优于相互竞争的生存模型,同时在所有离散时间模型中实现最佳校准。DCS的增强性能可归因于两个新的特征,即可变时输出节距和新的损失术语,即优化使用未经检查和审查的病人数据。我们认为DCS是临床应用基于深学习的生存预测的重要一步,并带有最先进的歧视和良好的校准。