# Gaussian Processes for Survival Analysis

arXiv: https://arxiv.org/abs/1611.00817 · HTML: https://mlsanity.com/paper/1611.00817/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2016.

## Built on by (most cited first, 14 of 14)

- [MOTOR: A Time-To-Event Foundation Model For Structured Medical Records](https://mlsanity.com/paper/2301.03150/) (2023)
- [CoxKAN: Kolmogorov-Arnold Networks for Interpretable, High-Performance Survival Analysis](https://mlsanity.com/paper/2409.04290/) (2024)
- [Counterfactual Phenotyping with Censored Time-to-Events](https://mlsanity.com/paper/2202.11089/) (2022)
- [Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures](https://mlsanity.com/paper/2112.04828/) (2021)
- [Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Models](https://mlsanity.com/paper/2107.12250/) (2021)
- [Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records](https://mlsanity.com/paper/2308.13104/) (2023)
- [NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification](https://mlsanity.com/paper/2505.11054/) (2025)
- [Individual Survival Curves with Conditional Normalizing Flows](https://mlsanity.com/paper/2107.12825/) (2021)
- [Deep State-Space Generative Model For Correlated Time-to-Event Predictions](https://mlsanity.com/paper/2407.19371/) (2024)
- [SurvUnc: A Meta-Model Based Uncertainty Quantification Framework for Survival Analysis](https://mlsanity.com/paper/2505.14803/) (2025)
- [Metaparametric Neural Networks for Survival Analysis](https://mlsanity.com/paper/2110.06610/) (2021)
- [DAGSurv: Directed Acyclic Graph Based Survival Analysis Using Deep Neural Networks](https://mlsanity.com/paper/2111.01482/) (2021)
- [Composite Survival Analysis: Learning with Auxiliary Aggregated Baselines and Survival Scores](https://mlsanity.com/paper/2312.05854/) (2023)
- [Exact Bayesian Gaussian Cox Processes Using Random Integral](https://mlsanity.com/paper/2406.19722/) (2024)

## Abstract (arXiv)

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.