Survival Analysis provides critical insights for partially incomplete time-to-event data in various domains. It is also an important example of probabilistic machine learning. The probabilistic nature of the predictions can be exploited by using (proper) scoring rules in the model fitting process instead of likelihood-based optimization. Our proposal does so in a generic manner and can be used for a variety of model classes. We establish different parametric and non-parametric sub-frameworks that allow different degrees of flexibility. Incorporated into neural networks, it leads to a computationally efficient and scalable optimization routine, yielding state-of-the-art predictive performance. Finally, we show that using our framework, we can recover various parametric models and demonstrate that optimization works equally well when compared to likelihood-based methods.

翻译：生存分析为各领域部分不完整事件发生时间数据提供了关键性见解，同时也是概率机器学习的重要范例。基于预测的概率特性，可在模型拟合过程中采用（适当）评分规则替代基于似然的优化方法。本文以统一方式提出该方案，适用于多种模型类别。我们建立了不同参数化与非参数化子框架，允许不同程度的灵活性。当融入神经网络时，该方案可形成计算高效且可扩展的优化流程，实现领先的预测性能。最后，我们证明通过该框架能够恢复多种参数模型，并展示其优化效果与基于似然的方法同样出色。