Adversarial Time-to-Event Modeling.
Proc Mach Learn Res
; 80: 735-744, 2018 Jul.
Article
en En
| MEDLINE
| ID: mdl-33834174
ABSTRACT
Modern health data science applications leverage abundant molecular and electronic health data, providing opportunities for machine learning to build statistical models to support clinical practice. Time-to-event analysis, also called survival analysis, stands as one of the most representative examples of such statistical models. We present a deep-network-based approach that leverages adversarial learning to address a key challenge in modern time-to-event modeling nonparametric estimation of event-time distributions. We also introduce a principled cost function to exploit information from censored events (events that occur subsequent to the observation window). Unlike most time-to-event models, we focus on the estimation of time-to-event distributions, rather than time ordering. We validate our model on both benchmark and real datasets, demonstrating that the proposed formulation yields significant performance gains relative to a parametric alternative, which we also propose.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Tipo de estudio:
Risk_factors_studies
Idioma:
En
Revista:
Proc Mach Learn Res
Año:
2018
Tipo del documento:
Article