Gaussian Processes for Survival Analysis
This provides a flexible method for survival analysis in fields like medicine or reliability engineering, though it appears incremental as it builds on existing Bayesian and Gaussian process techniques.
The paper tackles survival analysis by introducing a semi-parametric Bayesian model that combines a parametric baseline hazard with a Gaussian process for nonparametric variations and covariate dependence, handling various censoring types and showing improved performance over models like Cox proportional hazards and random survival forests in experiments.
We introduce a semi-parametric Bayesian model for survival analysis. The model is centred on a parametric baseline hazard, and uses a Gaussian process to model variations away from it nonparametrically, as well as dependence on covariates. As opposed to many other methods in survival analysis, our framework does not impose unnecessary constraints in the hazard rate or in the survival function. Furthermore, our model handles left, right and interval censoring mechanisms common in survival analysis. We propose a MCMC algorithm to perform inference and an approximation scheme based on random Fourier features to make computations faster. We report experimental results on synthetic and real data, showing that our model performs better than competing models such as Cox proportional hazards, ANOVA-DDP and random survival forests.