Fernandez Aguilar, T;
Rivera, N;
Teh, YW;
(2016)
Gaussian processes for survival analysis.
In:
(Proceedings) Advances in Neural Information Processing Systems 29 (NIPS 2016).
NIPS Proceedings
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Abstract
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.
| Type: | Proceedings paper |
|---|---|
| Title: | Gaussian processes for survival analysis |
| Event: | Advances in Neural Information Processing Systems 29 (NIPS 2016) |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://papers.nips.cc/paper/6443-gaussian-process... |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10090069 |
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