Partial logistic artificial neural network for competing risks regularized with automatic relevance determination

Paulo J G Lisboa, Terence A. Etchells, Ian H. Jarman, Corneliu T C Arsene, M. S Hane Aung, Antonio Eleuteri, Azzam F G Taktak, Federico Ambrogi, Patrizia Boracchi, Elia Biganzoli

Research output: Contribution to journalArticle

Abstract

Time-to-event analysis is important in a wide range of applications from clinical prognosis to risk modeling for credit scoring and insurance. In risk modeling, it is sometimes required to make a simultaneous assessment of the hazard arising from two or more mutually exclusive factors. This paper applies to an existing neural network model for competing risks (PLANNCR), a Bayesian regularization with the standard approximation of the evidence to implement automatic relevance determination (PLANNCR-ARD). The theoretical framework for the model is described and its application is illustrated with reference to local and distal recurrence of breast cancer, using the data set of Veronesi et al (1995).

Original languageEnglish
Pages (from-to)1403-1416
Number of pages14
JournalIEEE Transactions on Neural Networks
Volume20
Issue number9
DOIs
Publication statusPublished - 2009

Keywords

  • Censorship
  • Prognostic modeling
  • Risk analysis
  • Survival modeling
  • Time-to-event data

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Software
  • Medicine(all)

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  • Cite this

    Lisboa, P. J. G., Etchells, T. A., Jarman, I. H., Arsene, C. T. C., Aung, M. S. H., Eleuteri, A., Taktak, A. F. G., Ambrogi, F., Boracchi, P., & Biganzoli, E. (2009). Partial logistic artificial neural network for competing risks regularized with automatic relevance determination. IEEE Transactions on Neural Networks, 20(9), 1403-1416. https://doi.org/10.1109/TNN.2009.2023654