Extending the Neural Additive Model for Survival Analysis with EHR Data. (arXiv:2211.07814v1 [cs.LG])

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 …

analysis arxiv data ehr survival