In this paper, a family of neural network-based survival models is presented. The models are specified based on piecewise definitions of the hazard function and the density function on a partitioning of the time; both constant and linear piecewise definitions are presented, resulting in a family of four models. The models can be seen as an extension of the commonly used discrete-time and piecewise exponential models and thereby add flexibility to this set of standard models. Using a simulated dataset the models are shown to perform well compared to the highly expressive, state-of-the-art energy-based model, while only requiring a fraction of the computation time.

翻译：本文提出了一类基于神经网络的生存模型。这些模型基于时间划分上的风险函数和密度函数的逐段定义进行构建；本文同时给出了常数逐段和线性逐段两种定义方式，由此形成包含四个模型的模型族。该模型族可视为常用离散时间模型和逐段指数模型的推广，从而为此类标准模型增添了灵活性。通过模拟数据集的实验表明，与具有高度表达能力的最先进能量基模型相比，这些模型在性能上表现良好，而计算时间仅需其极小部分。