Partial Logistic Artificial Neural Network for Competing Risks Regularized With Automatic Relevance Determination
作者： P.J.G. Lisboa , T.A. Etchells , I.H. Jarman , C.T.C. Arsene , M.S.H. Aung
关键词: Data set 、 Bayesian probability 、 Relevance (information retrieval) 、 Risk analysis 、 Risk analysis (business) 、 Computer science 、 Risk management 、 Hazard (logic) 、 Artificial neural network 、 Range (mathematics) 、 Data mining 、 Competing risks
摘要: Time-to-event analysis is important in a wide range of applications from clinical prognosis to risk modeling for credit scoring and insurance. In modeling, it sometimes required make simultaneous assessment the hazard arising two or more mutually exclusive factors. This paper applies an existing neural network model competing risks (PLANNCR), Bayesian regularization with standard approximation evidence implement automatic relevance determination (PLANNCR-ARD). The theoretical framework described its application illustrated reference local distal recurrence breast cancer, using data set Veronesi (1995).
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