Continuous and Discrete Time Survival Analysis: Neural Network Approaches
作者： A. Eleuteri , M. S. H. Aung , A. F. G. Taktak , B. Damato , P. J. G. Lisboa
DOI: 10.1109/IEMBS.2007.4353568
关键词: Artificial neural network 、 Linear discriminant analysis 、 Pattern recognition (psychology) 、 Discrete time and continuous time 、 Survival rate 、 Statistics 、 Survival analysis 、 Bayesian inference 、 Calibration (statistics) 、 Computer science
摘要: In this paper we describe and compare two neural network models aimed at survival analysis modeling, based on formulations in continuous discrete time. Learning both is approached a Bayesian inference framework. We test the real problem, show that exhibit good discrimination calibration capabilities. The C index of varied from 0.8 (SE=0.093) year 1, to 0.75 (SE=0.034) 7 for time model; 0.81 (SE=0.07) (SE=0.033) model. For was (p<0.05) up years.
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