  An accurate model of patient-specific kidney graft survival distributions can
help to improve shared-decision making in the treatment and care of patients.
In this paper, we propose a deep learning method that directly models the
survival function instead of estimating the hazard function to predict survival
times for graft patients based on the principle of multi-task learning. By
learning to jointly predict the time of the event, and its rank in the cox
partial log likelihood framework, our deep learning approach outperforms, in
terms of survival time prediction quality and concordance index, other common
methods for survival analysis, including the Cox Proportional Hazards model and
a network trained on the cox partial log-likelihood.
