Title :
Continuous and Discrete Time Survival Analysis: Neural Network Approaches
Author :
Eleuteri, A. ; Aung, M.S.H. ; Taktak, A.F.G. ; Damato, B. ; Lisboa, P.J.G.
Author_Institution :
R. Liverpool Univ. Hosp., Liverpool
Abstract :
In this paper we describe and compare two neural network models aimed at survival analysis modeling, based on formulations in continuous and discrete time. Learning in both models is approached in a Bayesian inference framework. We test the models on a real survival analysis problem, and we show that both models exhibit good discrimination and calibration capabilities. The C index of discrimination varied from 0.8 (SE=0.093) at year 1, to 0.75 (SE=0.034) at year 7 for the continuous time model; from 0.81 (SE=0.07) at year 1, to 0.75 (SE=0.033) at year 7 for the discrete time model. For both models the calibration was good (p<0.05) up to 7 years.
Keywords :
Bayes methods; learning (artificial intelligence); medical computing; neural nets; Bayesian inference; calibration; continuous time model; continuous time survival analysis; discrete time model; discrete time survival analysis; learning; neural network; survival analysis modeling; Bayesian methods; Calibration; Computer architecture; Continuous time systems; Hazards; Hospitals; Neural networks; Parametric statistics; Proposals; Testing; Algorithms; Data Interpretation, Statistical; Discriminant Analysis; Eye Neoplasms; Humans; Incidence; Melanoma; Neural Networks (Computer); Pattern Recognition, Automated; Risk Assessment; Risk Factors; Survival Analysis; Survival Rate;
Conference_Titel :
Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE
Conference_Location :
Lyon
Print_ISBN :
978-1-4244-0787-3
DOI :
10.1109/IEMBS.2007.4353568
