A Unified Framework for Reliable Survival Analysis

Summary

This article proposes a unified framework for survival analysis, which predicts the time until an event such as disease relapse or death. The framework combines a stochastic, biologically inspired sparsity mechanism for feature selection, a Bayesian treatment of model weights, and an information-bound regularization for generalization. Experiments on five real-world survival datasets show competitive discrimination and calibration against state-of-the-art deep survival models, and the framework shows robustness to adversarial attacks.