Deep Convolutional Neural Network for Survival Estimation of Amyotrophic Lateral Sclerosis patients

We propose a convolutional neural network (CNN) coupled with a fully connected top layer for survival estimation. We design an objective function to directly estimate the probability of survival at discrete time intervals, conditional to the patient not having incurred any adverse event at previous time points. We test our CNN and objective function on a large dataset of longitudinal data of patients with Amyotrophic Lateral Sclerosis (ALS). We compare our CNN and the objective function against other neural networks designed for survival analysis, and against the optimization of Cox-partial-likelihood or a simple logistic classifier. The use of our objective function outperforms both Cox-partial-likelihood and logistic classifier, independently of the network architecture, and our deep CNN provides the best results in terms of AU-ROC, accuracy and mean absolute error