基于层次化深度学习的医疗数据库离群数据检测算法

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.

Medical Database Outlier Data Detection Algorithm Based on Hierarchical Deep Learning

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.