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.

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survival probability estimation model synthetic and real world data ones feedforward neural network architecture hierarchical bayesian linear models

Antonio, Eleuteri Roberto, Tagliaferri Leopoldo, Milano Sabino, De Placido Michele, De Laurentiis,.A novel neural network-based survival analysis model.. 16 (5-6),855-64.

Antonio, Eleuteri Roberto, Tagliaferri Leopoldo, Milano Sabino, De Placido Michele, De Laurentiis,"A novel neural network-based survival analysis model." 16

Antonio, Eleuteri Roberto, Tagliaferri Leopoldo, Milano Sabino, De Placido Michele, De Laurentiis,"A novel neural network-based survival analysis model."