Gaussian processes for survival analysis

Tamara Fernández, Nicolás Rivera, Yee Whye Teh

Resultado de la investigación: Contribución a una revistaArtículo de la conferenciarevisión exhaustiva

36 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Páginas (desde-hasta)5021-5029
Número de páginas9
PublicaciónAdvances in Neural Information Processing Systems
EstadoPublicada - 2016
Publicado de forma externa
Evento30th Annual Conference on Neural Information Processing Systems, NIPS 2016 - Barcelona, Espana
Duración: 5 dic. 201610 dic. 2016

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