There is currently great interest in applying neural networks to prediction
tasks in medicine. It is important for predictive models to be able to use
survival data, where each patient has a known follow-up time and
event/censoring indicator. This avoids information loss when training the model
and enables generation of predicted survival curves. In this paper, we describe
a discrete-time survival model that is designed to be used with neural
networks, which we refer to as Nnet-survival. The model is trained with the
maximum likelihood method using minibatch stochastic gradient descent (SGD).
The use of SGD enables rapid convergence and application to large datasets that
do not fit in memory. The model is flexible, so that the baseline hazard rate
and the effect of the input data on hazard probability can vary with follow-up
time. It has been implemented in the Keras deep learning framework, and source
code for the model and several examples is available online. We demonstrate the
performance of the model on both simulated and real data and compare it to
existing models Cox-nnet and Deepsurv.

本文介绍了一种名为 Nnet-survival 的离散时间生存模型，适用于神经网络在医学预测任务中的应用。该模型由 Keras 深度学习框架实现，具有灵活性，能够根据随访时间的不同变化基线风险和输入数据对危险概率的影响。在模拟和实际数据集上的表现优于 Cox-nnet 和 Deepsurv 等已有模型。