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计算机系统应用:2020,29(3):180-186
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基于层次化深度学习的医疗数据库离群数据检测算法
(1.黑龙江外国语学院 信息工程系, 哈尔滨 150025;2.普渡大学 机械工程系, 西拉法叶市 IN47906;3.哈尔滨工业大学 计算机科学与技术学院, 哈尔滨 150001)
Medical Database Outlier Data Detection Algorithm Based on Hierarchical Deep Learning
(1.Department of Information Engineering, Heilongjiang International University, Harbin 150025, China;2.Department of Mechanical Engineering, Purdue University, West Lafayette IN47906, USA;3.School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China)
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投稿时间:2019-08-03    修订日期:2019-09-02
中文摘要: 对医疗数据库中存在的离散数据进行检测时,由于缺少数据过滤等过程而导致检测执行时间较长、检测效率低、离散点检测率低等问题,为此提出基于层次化深度学习的医疗数据库离散数据检测算法.首先,采用动态网格划分法划分空间中的稀疏区域和稠密区域,降低数据检测的规模,缩短检测执行时间;然后,通过层次化深度学习过程融合专家知识和数据的属性取值分布信息,实现医疗数据库中离散数据的检测.实验结果表明,该算法可以在较短的时间内准确完成医疗数据库中离散数据的检测,且相较于传统算法来说更具有应用优势.
Abstract:When using the current algorithm to detect the discrete data in the medical database, problems such as long execution time, low detection efficiency and low detection rate of discrete points are caused by the lack of data filtering and other processes. Therefore, an algorithm for detecting discrete data in the medical database based on hierarchical deep learning is proposed. Firstly, the dynamic grid method is used to divide the sparse and dense areas in the space, so as to reduce the size of data detection and shorten the detection execution time. Then, the expert knowledge and data attribute value distribution information are integrated through the hierarchical deep learning process, and realize the detection of discrete data in medical database. Experimental results show that this algorithm can accurately complete the detection of discrete data in the medical database in a relatively short time, and has more advantages in application compared with the traditional algorithm.
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基金项目:国家自然科学基金(61803117);教育部科技发展中心产学研创新基金(2018A01002);国家科技部创新方法专项(2017IM010500)
引用文本:
李晓峰,王妍玮,李东.基于层次化深度学习的医疗数据库离群数据检测算法.计算机系统应用,2020,29(3):180-186
LI Xiao-Feng,WANG Yan-Wei,LI Dong.Medical Database Outlier Data Detection Algorithm Based on Hierarchical Deep Learning.COMPUTER SYSTEMS APPLICATIONS,2020,29(3):180-186

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