Partial logistic artificial neural network for competing risks regularized with automatic relevance determination.
IEEE Trans Neural Netw
; 20(9): 1403-16, 2009 Sep.
Article
em En
| MEDLINE
| ID: mdl-19628458
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 (1995).
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Automação
/
Modelos Logísticos
/
Risco
/
Redes Neurais de Computação
Tipo de estudo:
Diagnostic_studies
/
Etiology_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Adolescent
/
Adult
/
Aged
/
Female
/
Humans
/
Middle aged
Idioma:
En
Ano de publicação:
2009
Tipo de documento:
Article