A novel neural network-based survival analysis model

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

Research output: Contribution to journalArticlepeer-review


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 languageEnglish
Pages (from-to)855-864
Number of pages10
JournalNeural Networks
Issue number5-6
Publication statusPublished - Jun 2003


  • Bayesian learning
  • Conditioning probability estimation
  • MCMC methods
  • Neural networks
  • Survival analysis

ASJC Scopus subject areas

  • Artificial Intelligence
  • Neuroscience(all)


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