RNN-SURV: A deep recurrent model for survival analysis

Abstract

Current medical practice is driven by clinical guidelines which are designed for the “average” patient. Deep learning is enabling medicine to become personalized to the patient at hand. In this paper we present a new recurrent neural network model for personalized survival analysis called rnn-surv. Our model is able to exploit censored data to compute both the risk score and the survival function of each patient. At each time step, the network takes as input the features characterizing the patient and the identifier of the time step, creates an embedding, and outputs the value of the survival function in that time step. Finally, the values of the survival function are linearly combined to compute the unique risk score. Thanks to the model structure and the training designed to exploit two loss functions, our model gets better concordance index (C-index) than the state of the art approaches.