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Title: GPSRL: Learning Semi-Parametric Bayesian Survival Rule Lists from Heterogeneous Patient Data
Survival data is often collected in medical applications from a heterogeneous population of patients. While in the past, popular survival models focused on modeling the average effect of the covariates on survival outcomes, rapidly advancing sensing and information technologies have provided opportunities to further model the heterogeneity of the population as well as the non-linearity of the survival risk. With this motivation, we propose a new semi-parametric Bayesian Survival Rule List model in this paper. Our model derives a rule-based decision-making approach, while within the regime defined by each rule, survival risk is modelled via a Gaussian process latent variable model. Markov Chain Monte Carlo with a nested Laplace approximation on the Gaussian process posterior is used to search over the posterior of the rule lists efficiently. The use of ordered rule lists enables us to model heterogeneity while keeping the model complexity in check. Performance evaluations on a synthetic heterogeneous survival dataset and a real world sepsis survival dataset demonstrate the effectiveness of our model.  more » « less
Award ID(s):
1718513
PAR ID:
10220434
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
International Conference on Pattern Recognition
Volume:
2020
ISSN:
1051-4651
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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