  Survival analysis is widely used as a technique to model time-to-event data
when some data is censored, particularly in healthcare for predicting future
patient risk. In such settings, survival models must be both accurate and
interpretable so that users (such as doctors) can trust the model and
understand model predictions. While most literature focuses on discrimination,
interpretability is equally as important. A successful interpretable model
should be able to describe how changing each feature impacts the outcome, and
should only use a small number of features. In this paper, we present DyS
(pronounced ``dice''), a new survival analysis model that achieves both strong
discrimination and interpretability. DyS is a feature-sparse Generalized
Additive Model, combining feature selection and interpretable prediction into
one model. While DyS works well for all survival analysis problems, it is
particularly useful for large (in $n$ and $p$) survival datasets such as those
commonly found in observational healthcare studies. Empirical studies show that
DyS competes with other state-of-the-art machine learning models for survival
analysis, while being highly interpretable.
