Hierarchical Bayesian neural networks: An application to a prostate cancer study

• 成果类型: Article
• 署名作者: Ghosh, M; Maiti, T; Kim, D; Chakraborty, S; Tewari, A
• 署名单位: State University System of Florida; University of Florida; Iowa State University; Kyungpook National University (KNU); Henry Ford Health System; Henry Ford Hospital; Henry Ford Health System; Henry Ford Hospital; Henry Ford Health System; Henry Ford Hospital
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214504000000665
• 发表日期: 2004
• 页码: 601-608
• 关键词: predict pathological stage; radical prostatectomy; gleason score; gaussian-processes; clinical stage; antigen; rates

摘要

Prostate cancer is one of the most common cancers in American men. Management depends on the staging of prostate cancer. Only cancers that are confined to organs of origin are potentially curable. The article considers a hierarchical Bayesian neural network approach for posterior prediction probabilities of certain features indicative of non-organ-confined prostate cancer. The Bayesian procedure is implemented by an application of the Markov chain Monte Carlo numerical integration technique. For the problem at hand, the hierarchical Bayesian neural network approach is shown to be superior to the approach based on hierarchical Bayesian logistic regression model as well as the classical feedforward neural networks.