Deep Survival Analysis Based On Nuclear Norm

Survival analysis is a discipline which mainly focuses on survival status,survival time and their statistical relationship with different covariates.The data of survival analysis has different kinds of censoring,including left censoring,interval censoring and right censoring.The data studied in this paper is survival data in medical statistics with right cencoring.Problems of data missing and categorical attributes representation are solved in this paper,and deep learning methods are used in survival analysis model to propose a kind of new algorithm to study the regression of survival data.This paper devotes to apply DeepSurv algorithm to different kinds of data,and compare it with other well-performed algorithm to see the performance of DeepSurv,then propose new method based on it.As for the large scale survival data in this paper,the classical MIMIC-III data in medical statistics is selected.While processing the data,the missing part is really large,so nuclear norm method,a method usually used in image denoising,is used to complete the missing data;then there exist lots of discrete categorical variables in the data,so dummy variable method and empirical Bayes method are taken into consideration to find better interpretability.When deep neural network is used to train the survival parameter model,there may exist problems like slow converging speed,overfitting and so on.To solve these problems,this paper applies Scaled Exponential Linear Unit(SELU)activa-tion function,adaptive moment estimation(Adam)optimization algorithm,learning rate decay and Dropout method to get the final model.In summary,this paper pro-poses DeepSurv algorithm based on nuclear norm(NN-DeepSurv),and applies it to simulated data and actual data(including MIMIC-III)to illustrate the performance of the proposed algorithm.