Extending the Neural Additive Model for Survival Analysis with EHR Data
This work addresses the need for interpretable survival analysis models in healthcare, specifically for gastric cancer prediction using EHR data, representing an incremental extension of existing methods.
The paper tackled the problem of creating interpretable deep learning models for survival analysis by extending the Neural Additive Model (NAM) with pairwise feature interactions and loss functions for proportional and non-proportional hazards, resulting in TimeNAM models that significantly improve performance over standard NAM and match or surpass state-of-the-art black-box methods on benchmark and gastric cancer datasets.
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.