DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values

Abstract

There has been increasing interest in modelling survival data using deep learning methods in medical research. Current approaches have focused on designing special cost functions to handle censored survival data. We propose a very different method with two steps. In the first step, we transform each subject's survival time into a series of jackknife pseudo conditional survival probabilities and then use these pseudo probabilities as a quantitative response variable in the deep neural network model. By using the pseudo values, we reduce a complex survival analysis to a standard regression problem, which greatly simplifies the neural network construction. Our two-step approach is simple, yet very flexible in making risk predictions for survival data, which is very appealing from the practice point of view. The source code is freely available at http://github.com/lilizhaoUM/DNNSurv.

arXiv comment12 pages, 4 figures

References in corpus (4)

• DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network
• A Scalable Discrete-Time Survival Model for Neural Networks
• Deep Neural Networks for Survival Analysis Based on a Multi-Task Framework
• Deep Learning for Patient-Specific Kidney Graft Survival Analysis

Cited by in corpus (3)

• SurvLIME-Inf: A simplified modification of SurvLIME for explanation of machine learning survival models
• DeepHazard: neural network for time-varying risks
• Fast approximations of pseudo-observations in the context of right-censoring and interval-censoring