A novel neural network-based survival analysis model

Antonio Eleuteri; Roberto Tagliaferri; Leopoldo Milano; Sabino De Placido; Michele De Laurentiis

doi:10.1016/S0893-6080(03)00098-4

A novel neural network-based survival analysis model

Antonio Eleuteri, Roberto Tagliaferri, Leopoldo Milano, Sabino De Placido, Michele De Laurentiis

Istituto Nazionale Tumori Fondazione G. Pascale

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	855-864
Number of pages	10
Journal	Neural Networks
Volume	16
Issue number	5-6
DOIs	https://doi.org/10.1016/S0893-6080(03)00098-4
Publication status	Published - Jun 2003

Keywords

Bayesian learning
Conditioning probability estimation
MCMC methods
Neural networks
Survival analysis

ASJC Scopus subject areas

Artificial Intelligence
Neuroscience(all)

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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.",

keywords = "Bayesian learning, Conditioning probability estimation, MCMC methods, Neural networks, Survival analysis",

author = "Antonio Eleuteri and Roberto Tagliaferri and Leopoldo Milano and {De Placido}, Sabino and {De Laurentiis}, Michele",

year = "2003",

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AU - Tagliaferri, Roberto

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AU - De Placido, Sabino

AU - De Laurentiis, Michele

PY - 2003/6

Y1 - 2003/6

N2 - 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.

AB - 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.

KW - Bayesian learning

KW - Conditioning probability estimation

KW - MCMC methods

KW - Neural networks

KW - Survival analysis

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JF - Neural Networks

SN - 0893-6080

IS - 5-6

ER -

A novel neural network-based survival analysis model

Abstract

Keywords

ASJC Scopus subject areas

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