Referenced Items are not available for this document.
Cox's proportional hazards model has been widely used for the analysis of treatment and prognostic effects with censored survival data. In this paper, we propose a neural network model based on bootstrapping to estimate the survival function and predict the short-term survival at any time during the course of the disease. The bootstrapping for the neural network is introduced when selecting the optimum number of hidden units and testing the goodness-of-fit. The proposed methods are illustrated using data from a long-term study of patients with primary biliary cirrhosis (PBC).
Published in:
Neural Networks, IEEE Transactions on
(Volume:20
,
Issue:
3
)
Date of Publication: March 2009
- Page(s):
- 389 - 394
- ISSN :
- 1045-9227
- INSPEC Accession Number:
- 10477141
- Digital Object Identifier :
- 10.1109/TNN.2008.2008328
- Product Type:
- Journals & Magazines
- Date of Publication :
- 27 January 2009
- Date of Current Version :
- 24 February 2009
- Issue Date :
- March 2009
- Sponsored by :
- IEEE Computational Intelligence Society
- PubMed ID :
- 19179250
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