摘要
针对传统的医疗数据敏感度度量方法存在的度量开销大、数据查准率和查全率低的问题,提出基于量子计算的医疗数据敏感度度量方法。首先采用分布式样本重构方法重组医疗数据的分布式结构,建立医疗数据敏感度度量的统计分析模型;其次采用量化回归分析方法进行医疗数据的模糊融合和聚类分析,建立其定量递归分析模型;最后采用量子计算方法进行医疗数据敏感度度量过程中的自适应寻优控制,建立量子寻优约束进化模型,采用动态全局规划方法实现对医疗数据敏感度的度量。实验结果表明,采用该方法进行医疗数据敏感度度量的统计分析能力较强,且度量时间开销较小、对敏感数据的查准率和查全率较高,提高了对医疗数据的检索和特征分辨能力,有效实现了对医疗数据的统计分析和检测度量。
In order to address the problems existing in the traditional medical data sensitivity measurement methods,such as high measurement cost,low accuracy and low recall rate,a medical data sensitivity measurement method based on quantum computing is proposed.First,the distributed sample reconstruction method is used to reconstruct the distributed structure of medical data and establish the statistical analysis model of medical data sensitivity measurement.Secondly,the fuzzy fusion and clustering analysis of medical data are carried out by quantitative regression analysis method,and the quantitative recursive analysis model is established.Finally,the quantum computing method is used for adaptive optimization control in the process of medical data sensitivity measurement,the quantum optimization constraint evolution model is established,and the dynamic global programming method is used to measure the sensitivity of medical data.Experiment shows that the proposed method has a strong statistical analysis ability to measure the sensitivity of medical data,a low cost of measuring time,and a high precision and recall rate of sensitive data,which improves the retrieval and feature resolution ability of medical data and effectively realizes the statistical analysis and detection measurement of medical data.
作者
李晓峰
焦洪双
王妍玮
LI Xiao-feng;JIAO Hong-shuang;WANG Yan-wei(Department of Information Engineering,Heilongjiang International University,Harbin 150025,China;Department of Mechanical Engineering,Purdue University,West Lafayette IN47906,US)
出处
《计算机技术与发展》
2021年第1期187-191,197,共6页
Computer Technology and Development
基金
国家自然科学基金资助项目(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)
关键词
量子计算
医疗数据
敏感度
度量
统计分析
模糊融合
聚类分析
全局规划
quantum computing
medical data
sensitivity
measurement
statistical analysis
fuzzy fusion
clustering analysis
global planning
