A novel neural network-based survival analysis model

Authors:
Antonio Eleuteri;Roberto Tagliaferri;Leopoldo Milano;Sabino De Placido;Michele De Laurentiis
Affiliations:
DMA, Università di Napoli 'Federico II', Naples, Italy and INFN Sez. Napoli, Naples, Italy;DMI, Università di Salerno, Salerno, Italy and INFM Unità di Salerno, Salerno, Italy;INFN Sez. Napoli, Naples, Italy and Dipartimento di Scienze Fisiche, Università di Napoli 'Federico II', Naples, Italy;Dipartimento di Endocrinologia ed Oncologia Molecolare e Clinica, Università di Napoli 'Federico II', Naples, Italy;Dipartimento di Endocrinologia ed Oncologia Molecolare e Clinica, Università di Napoli 'Federico II', Naples, Italy
Venue:
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Year:
2003

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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.