Lisboa Paulo J G, Etchells Terence A, Jarman Ian H, Arsene Corneliu T C, Aung M S Hane, Eleuteri Antonio, Taktak Azzam F G, Ambrogi Federico, Boracchi Patrizia, Biganzoli Elia
School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool L33AF, UK.
IEEE Trans Neural Netw. 2009 Sep;20(9):1403-16. doi: 10.1109/TNN.2009.2023654. Epub 2009 Jul 21.
Time-to-event analysis is important in a wide range of applications from clinical prognosis to risk modeling for credit scoring and insurance. In risk modeling, it is sometimes required to make a simultaneous assessment of the hazard arising from two or more mutually exclusive factors. This paper applies to an existing neural network model for competing risks (PLANNCR), a Bayesian regularization with the standard approximation of the evidence to implement automatic relevance determination (PLANNCR-ARD). The theoretical framework for the model is described and its application is illustrated with reference to local and distal recurrence of breast cancer, using the data set of Veronesi (1995).
生存时间分析在从临床预后到信用评分和保险风险建模等广泛应用中都很重要。在风险建模中，有时需要同时评估两个或多个相互排斥因素产生的风险。本文将贝叶斯正则化与证据的标准近似相结合以实现自动相关性确定（PLANNCR-ARD），应用于现有的竞争风险神经网络模型（PLANNCR）。描述了该模型的理论框架，并使用韦罗内西（1995年）的数据集，以乳腺癌局部和远处复发为例说明了其应用。
