title: Artificial neural networks for predictive modeling in prostate cancer.
abstract: Artificial neural networks (ANNs) represent a relatively new methodology for predictive modeling in medicine. ANNs, a form of artificial intelligence loosely based on the brain, have a demonstrated ability to learn complex and subtle relationships between variables in medical applications. In contrast with traditional statistical techniques, ANNs are capable of automatically resolving these relationships without the need for a priori assumptions about the nature of the interactions between variables. As with any technique, ANNs have limitations and potential drawbacks. This article provides an overview of the theoretical basis of ANNs, how they function, their strengths and limitations, and examples of how ANNs have been used to develop predictive models for the management of prostate cancer.
authors.affiliation: University of Colorado Health Sciences Center, C-314, 200 East 9th Avenue, Denver, CO 80262, USA. ed.gamito@uchsc.edu
authors.forename: Eduard J
authors.initials: EJ
authors.lastname: Gamito
authors.forename: E David
authors.initials: ED
authors.lastname: Crawford
mesh_terms: D003937:Diagnosis, Differential; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D009364:Neoplasm Recurrence, Local; D009367:Neoplasm Staging; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011379:Prognosis; D011471:Prostatic Neoplasms / Q000175:diagnosis / Q000401:mortality / Q000628:therapy; D016019:Survival Analysis