There has been increasing interest in modeling survival data using deep learning methods in medical research. In this paper, we proposed a Bayesian hierarchical deep neural networks model for modeling and prediction of survival data. Compared with previously studied methods, the new proposal can provide not only point estimate of survival probability but also quantification of the corresponding uncertainty, which can be of crucial importance in predictive modeling and subsequent decision making. The favorable statistical properties of point and uncertainty estimates were demonstrated by simulation studies and real data analysis. The Python code implementing the proposed approach was provided.
翻译:对利用医学研究中的深层学习方法建立生存数据模型的兴趣日益浓厚。在本文件中,我们提出了一个贝耶斯河岸级深神经网络模型,用于建立和预测生存数据模型和预测生存数据。与以前研究过的方法相比,新提案不仅可以提供生存概率的点估计,还可以提供相应的不确定性的量化,这对于预测模型和随后的决策至关重要。点和不确定性估计数的有利统计特性通过模拟研究和真实数据分析得到证明。提供了实施拟议方法的Python代码。