论文标题
使用神经网络估算条件混合物Weibull分布与右审核数据进行估算
Estimation of conditional mixture Weibull distribution with right-censored data using neural network for time-to-event analysis
论文作者
论文摘要
在本文中，我们考虑使用右审核数据的生存分析，这是预测维护和健康领域中的常见情况。我们根据有条件地将两参数Weibull分布的估计估算到特征。为了实现这一结果，我们描述了一个神经网络体系结构和相关的损失功能，这些功能考虑了右审核数据。我们将方法扩展到了两参数Weibull分布的有限混合物。我们首先验证我们的模型能够精确估算合成数据集上条件Weibull分布的正确参数。在两个现实单词数据集（Apabric and Seer）上的数值实验中，我们的模型优于最新方法。我们还证明我们的方法可以考虑任何生存时间范围。
In this paper, we consider survival analysis with right-censored data which is a common situation in predictive maintenance and health field. We propose a model based on the estimation of two-parameter Weibull distribution conditionally to the features. To achieve this result, we describe a neural network architecture and the associated loss functions that takes into account the right-censored data. We extend the approach to a finite mixture of two-parameter Weibull distributions. We first validate that our model is able to precisely estimate the right parameters of the conditional Weibull distribution on synthetic datasets. In numerical experiments on two real-word datasets (METABRIC and SEER), our model outperforms the state-of-the-art methods. We also demonstrate that our approach can consider any survival time horizon.
