摘要
分析腰椎间盘早期退变诊断现状及问题,提出基于代谢组学和标签分布学习方法可建立腰椎间盘早期退变分类器,并利用PT-Bayes、AA-BP和SA-IIS算法实施构建,通过实际计算分析各分类器的性能、应用意义及效果。
The present situation and problems of diagnosis of early lumbar disc degeneration are analyzed. The classifiers for early lumbar disc degeneration(LDD) are proposed based on metabonomics and label distribution learning. The LDD classifiers based on PT-Bayes, AA-BP and SA-IIS algorithms are established, and the performance, application significance and effect of each classifier are analyzed by practical calculation.
作者
汤阳杰
吴晓锋
欧阳林
刘群
罗爱芳
TANG Yangjie;WU Xiaofeng;OUYANG Lin;LIU Qun;LUO Aifang(School of Mathematics and Statistics,Minnan Normal University,Zhangzhou 363000,China;Department of Medical Imaging,PLA 909th Hospital,Zhangzhou 363000,China)
出处
《医学信息学杂志》
CAS
2023年第2期42-46,共5页
Journal of Medical Informatics
基金
福建省区域发展项目“基于多模态功能影像的腰椎间盘退变代谢组学研究”(项目编号:2019Y3007)。
关键词
腰椎间盘早期退变
代谢组学
标签分布学习
分类器
智能诊断
early lumbar disc degeneration
metabonomics
label distribution learning
classifier
intelligent diagnosis
