文章基本信息

标题：BDNNSurv: Bayesian Deep Neural Networks for Survival Analysis Using Pseudo Value
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作者：Dai Feng ; Lili Zhao
期刊名称：Journal of Data Science
印刷版ISSN：1680-743X
电子版ISSN：1683-8602
出版年度：2021
卷号：19
期号：6
页码：542-554
DOI：10.6339/21-JDS1018
语种：English
出版社：Tingmao Publish Company
摘要：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.