Deep Neural Networks for Survival Analysis Using Pseudo Values

• Published in: IEEE journal of biomedical and health informatics Vol. 24; no. 11; pp. 3308 - 3314
• Main Authors: Zhao, Lili, Feng, Dai
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Algorithms; Analytical models; Artificial neural networks; Biological system modeling; Cost function; Data models; Deep learning; Hazards; Humans; Informatics; IPCW; Machine learning; Medical research; neural network; Neural networks; Neural Networks, Computer; pseudo probability; Regression analysis; risk prediction; Software; Source code; Survival; Survival Analysis; survival outcome
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2020.2980204

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 simple 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.