Gamito Eduard J, Crawford E David
University of Colorado Health Sciences Center, C-314, 200 East 9th Avenue, Denver, CO 80262, USA.
Curr Oncol Rep. 2004 May;6(3):216-21. doi: 10.1007/s11912-004-0052-z.
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.
人工神经网络(ANNs)是医学预测建模中一种相对较新的方法。人工神经网络是一种基于大脑的人工智能形式,在医学应用中已证明有能力学习变量之间复杂而微妙的关系。与传统统计技术相比,人工神经网络能够自动解析这些关系,而无需对变量之间相互作用的性质进行先验假设。与任何技术一样,人工神经网络也有局限性和潜在缺点。本文概述了人工神经网络的理论基础、其运作方式、优势和局限性,以及人工神经网络如何用于开发前列腺癌管理预测模型的示例。