title: A novel neural network-based survival analysis model.
abstract: A feedforward neural network architecture aimed at survival probability estimation is presented which generalizes the standard, usually linear, models described in literature. The network builds an approximation to the survival probability of a system at a given time, conditional on the system features. The resulting model is described in a hierarchical Bayesian framework. Experiments with synthetic and real world data compare the performance of this model with the commonly used standard ones.
authors.affiliation: DMA, Università di Napoli Federico II, Naples, Italy. eleuteri@na.infn.it
authors.forename: Antonio
authors.initials: A
authors.lastname: Eleuteri
authors.forename: Roberto
authors.initials: R
authors.lastname: Tagliaferri
authors.forename: Leopoldo
authors.initials: L
authors.lastname: Milano
authors.forename: Sabino
authors.initials: S
authors.lastname: De Placido
authors.forename: Michele
authors.initials: M
authors.lastname: De Laurentiis
mesh_terms: D001499:Bayes Theorem; D008954:Models, Biological; D016571:Neural Networks, Computer; D016019:Survival Analysis