  We propose a versatile framework for survival analysis that combines advanced
concepts from statistics with deep learning. The presented framework is based
on piecewise exponential models and thereby supports various survival tasks,
such as competing risks and multi-state modeling, and further allows for
estimation of time-varying effects and time-varying features. To also include
multiple data sources and higher-order interaction effects into the model, we
embed the model class in a neural network and thereby enable the simultaneous
estimation of both inherently interpretable structured regression inputs as
well as deep neural network components which can potentially process additional
unstructured data sources. A proof of concept is provided by using the
framework to predict Alzheimer's disease progression based on tabular and 3D
point cloud data and applying it to synthetic data.
