  Deep learning models for survival analysis have gained significant attention
in the literature, but they suffer from severe performance deficits when the
dataset contains many irrelevant features. We give empirical evidence for this
problem in real-world medical settings using the state-of-the-art model
DeepHit. Furthermore, we develop methods to improve the deep learning model
through novel approaches to feature selection in survival analysis. We propose
filter methods for hard feature selection and a neural network architecture
that weights features for soft feature selection. Our experiments on two
real-world medical datasets demonstrate that substantial performance
improvements against the original models are achievable.
