Artificial Intelligence in Medicine
Volume 28, Issue 1 , Pages 1-25, May 2003

A Bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer

  • P.J.G. Lisboa

      Affiliations

    • School of Computing and Mathematical Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK
    • Corresponding Author InformationCorresponding author. Tel.: +55-51-33165571; fax: +55-51-33168010.
    • ,
  • H. Wong

      Affiliations

    • School of Computing and Mathematical Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK
    • ,
  • P. Harris

      Affiliations

    • School of Computing and Mathematical Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK
    • ,
  • R. Swindell

      Affiliations

    • Medical Statistics Department, Christie Hospital, Wilmslow Road, Withington, Manchester M20 4BX, UK

Received 29 July 2002; received in revised form 15 November 2002; accepted 10 December 2002.

Abstract 

A Bayesian framework is introduced to carry out Automatic Relevance Determination (ARD) in feedforward neural networks to model censored data. A procedure to identify and interpret the prognostic group allocation is also described.

These methodologies are applied to 1616 records routinely collected at Christie Hospital, in a monthly cohort study with 5-year follow-up. Two cohort studies are presented, for low- and high-risk patients allocated by standard clinical staging.

The results of contrasting the Partial Logistic Artificial Neural Network (PLANN)–ARD model with the proportional hazards model are that the two are consistent, but the neural network may be more specific in the allocation of patients into prognostic groups. With automatic model selection, the regularised neural network is more conservative than the default stepwise forward selection procedure implemented by SPSS with the Akaike Information Criterion.

Keywords: Artificial neural networks, Automatic Relevance Determination (ARD), Censorship, Cohort study, Proportional hazards, Nottingham Prognostic Index (NPI)

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PII: S0933-3657(03)00033-2

doi:10.1016/S0933-3657(03)00033-2

Artificial Intelligence in Medicine
Volume 28, Issue 1 , Pages 1-25, May 2003

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