Modern health data science applications leverage abundant molecular and
electronic health data, providing opportunities for machine learning to build
statistical models to support clinical practice. Time-to-event analysis, also
called survival analysis, stands as one of the most representative examples of
such statistical models. We present a deep-network-based approach that
leverages adversarial learning to address a key challenge in modern
time-to-event modeling: nonparametric estimation of event-time distributions.
We also introduce a principled cost function to exploit information from
censored events (events that occur subsequent to the observation window).
Unlike most time-to-event models, we focus on the estimation of time-to-event
distributions, rather than time ordering. We validate our model on both
benchmark and real datasets, demonstrating that the proposed formulation yields
significant performance gains relative to a parametric alternative, which we
also propose.

该论文提出了基于深度网络和对抗学习的非参数事件时间分布估计方法，通过有效利用丰富的健康分子数据和电子健康数据，为现代健康数据科学的应用提供了重要支持，并在基准和真实数据集上得到了显著的性能提高。