Robust Survival Analysis with Adversarial Regularization
Michael Potter Stefano Maxenti Michael Everett
Survival Analysis (SA) is about modeling the time for an event of interest to
occur, which has important applications in many fields, including medicine,
defense, finance, and aerospace. Recent work has demonstrated the benefits of
using Neural Networks (NNs) to capture complicated relationships in SA.
However, the datasets used to train these models are often subject to
uncertainty (e.g., noisy measurements, human error), which we show can
substantially degrade the performance of existing techniques. To address this
issue, this work leverages recent advances in NN verification to provide new
algorithms for generating fully parametric survival models that are robust to
such uncertainties. In particular, we introduce a robust loss function for
training the models and use CROWN-IBP regularization to address the
computational challenges with solving the resulting Min-Max problem. To
evaluate the proposed approach, we apply relevant perturbations to publicly
available datasets in the SurvSet repository and compare survival models
against several baselines. We empirically show that Survival Analysis with
Adversarial Regularization (SAWAR) method on average ranks best for dataset
perturbations of varying magnitudes on metrics such as Negative Log Likelihood
(NegLL), Integrated Brier Score (IBS), and Concordance Index (CI), concluding
that adversarial regularization enhances performance in SA. Code:
https://github.com/mlpotter/SAWAR