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ToolsLisboa, Paulo J.G., Etchells, Terence A., Jarman, Ian H., Arsene, Corneliu T.C., Aung, M. S.Hane, Eleuteri, Antonio, Taktak, Azzam F.G., Ambrogi, Federico, Boracchi, Patrizia and Biganzoli, Elia (2009) Partial logistic artificial neural network for competing risks regularized with automatic relevance determination. IEEE Transactions on Neural Networks, 20 (9). pp. 1403-1416. ISSN 1045-9227
Full text not available from this repository. (Request a copy)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).
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | censorship,prognostic modeling,risk analysis,survival modeling,time-to-event data,software,computer science applications,computer networks and communications,artificial intelligence,sdg 3 - good health and well-being ,/dk/atira/pure/subjectarea/asjc/1700/1712 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 10 Feb 2026 14:37 |
| Last Modified: | 16 Feb 2026 01:28 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/101895 |
| DOI: | 10.1109/TNN.2009.2023654 |
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