Data di Pubblicazione:
2002
Citazione:
Radial Basis Function Neural Networks for the Analysis of Survival Data / P. Boracchi, E. Biganzoli. - In: METRON. - ISSN 0026-1424. - 60:1-2(2002), pp. 191-210.
Abstract:
The growing interest in artificial neural networks for outcome prediction of oncological patients is motivated by the increasing number of variables related to patient and/or disease characteristics to be investigated and by the possible presence of complex prognostic effects. Neural networks suitable for survival data should consider censoring in a correct way to avoid suboptimal models. Starting from the relationship between survival regression models and generalized linear models with Poisson error, we proposed their extensions as feed-forward neural networks. In particular, radial basis function networks are considered, which can be implemented with standard statistical software. The proposed models can be applied in an exploratory framework, for a single event and in the presence of competing risks. An application of the proposed models to literature data is presented.
Tipologia IRIS:
01 - Articolo su periodico
Elenco autori:
P. Boracchi, E. Biganzoli
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