Reinterpreting survival analysis in the universal approximator age
Sören Dittmer Michael Roberts Jacobus Preller AIX COVNET James H. F. Rudd John A. D. Aston Carola-Bibiane Schönlieb
Survival analysis is an integral part of the statistical toolbox. However,
while most domains of classical statistics have embraced deep learning,
survival analysis only recently gained some minor attention from the deep
learning community. This recent development is likely in part motivated by the
COVID-19 pandemic. We aim to provide the tools needed to fully harness the
potential of survival analysis in deep learning. On the one hand, we discuss
how survival analysis connects to classification and regression. On the other
hand, we provide technical tools. We provide a new loss function, evaluation
metrics, and the first universal approximating network that provably produces
survival curves without numeric integration. We show that the loss function and
model outperform other approaches using a large numerical study.