title: Survival analysis of censored data: neural network analysis detection of complex interactions between variables.
abstract: Neural networks can be used as pattern recognition systems in complex data sets. We are exploring their utility in performing survival analysis to predict time to relapse or death. This technique has the potential to find easily some types of very complex interactions in data that would not be easily recognized by conventional statistical methods. In this paper we demonstrate that there are several ways neural networks can be used to find three-way interactions among variables. Thus, in data sets where such complex interactions exist, neural networks may find utility in detecting such interactions and in helping to produce predictive models.
authors: Department of Medicine/Oncology, University of Texas Health Science Center at San Antonio 78284-7884.
authors: M
authors: M
authors: De Laurentiis
authors: P M
authors: PM
authors: Ravdin
mesh_terms: D001943:Breast Neoplasms / Q000401:mortality; D003627:Data Interpretation, Statistical; D006801:Humans; D016571:Neural Networks, Computer; D011379:Prognosis; D016019:Survival Analysis