  With increasing interest in applying machine learning to develop healthcare
solutions, there is a desire to create interpretable deep learning models for
survival analysis. In this paper, we extend the Neural Additive Model (NAM) by
incorporating pairwise feature interaction networks and equip these models with
loss functions that fit both proportional and non-proportional extensions of
the Cox model. We show that within this extended framework, we can construct
non-proportional hazard models, which we call TimeNAM, that significantly
improve performance over the standard NAM model architecture on benchmark
survival datasets. We apply these model architectures to data from the
Electronic Health Record (EHR) database of Seoul National University Hospital
Gangnam Center (SNUHGC) to build an interpretable neural network survival model
for gastric cancer prediction. We demonstrate that on both benchmark survival
analysis datasets, as well as on our gastric cancer dataset, our model
architectures yield performance that matches, or surpasses, the current
state-of-the-art black-box methods.
