|本期目录/Table of Contents|

[1]李飞,吴丽. KNN离群点检测算法在医保审核中的应用[J].内江师范学院学报(自然科学),2017,08:69-72.
 LI Fei,WU Li. On the Application of KNN Outlier Detection Algorithm in the Medical Insurance Verification[J].,2017,08:69-72.
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 KNN离群点检测算法在医保审核中的应用(PDF)

《内江师范学院学报(自然科学)》[ISSN:1671-1785/CN:51-1521/Z]

期数:
2017年第08期
页码:
69-72
栏目:
出版日期:
2017-09-05

文章信息/Info

Title:
 On the Application of KNN Outlier Detection Algorithm in the Medical
Insurance Verification
文章编号:
1671- 1785(2017)08-0069-04
作者:
 李飞 吴丽
 内江师范学院 计算机科学学院, 四川 内江 641199
Author(s):
 LI Fei WU Li
 College of Computer Science, Neijiang Normal University, Neijiang, Sichuan641199, China
关键词:
 医疗保险审核离群点检测 KNN
Keywords:
 medical insuranceverificationthe outlier detectionKNN
分类号:
F840. 684
DOI:
10. 13603/j. cnki. 51-1621/z. 2017. 08. 016
文献标识码:
A
摘要:
 为解决医保基金被滥用、被浪费的问题,本文对某地区医保中心的数据进行分析,将问题归结为找出违规可能性较大的“可疑”处方,排除大部分正常处方,以达到减少人工审核量的目的.运用离群点检测方法,在对医保数据进行预处理分析、解决其高维稀疏问题的基础上,提出了一种面向医保审核的属性权重计算公式,以提高检测的准确率;将 KNN算法应用于白内障、胆结石、阑尾炎三种疾病的病例处方检测.实验显示, KNN离群点检测算法能检测出大部分的“可疑”处方.
Abstract:
 To solve the abuse and squandering of medical insurance fund, through the analysis of the data from a local medical insurance center, a simplified method, in which the major task of verification is to screen out those“suspicious” medi- cal prescriptions and let go of those normal ones, was found to reduce the workload of human verification. By use of the outlier detection algorithm and on the basis of solving the high dimensional sparsity after a pre-processing analysis of the medical in- surance data, a computational formula of attribute weights is worked out for medical insurance verification for the purpose of improving the detection accuracy; by applying the KNN algorithm to the detection of prescriptions of patients afflicted with cat- aract, gall-stone and appendicitis, it is found that the said algorithm is capable of screening out most“suspicious” medical pre- scriptions

参考文献/References

[1] 王智勇. 上海市医疗保险违规行为及其监督的研究[D].上海:复旦大学,2005.
[2]项凤华.江苏上半年查出医保定点机构违规金额2237 万[N].现代快报,2012-08-20(A9).
[3]林源.国内外医疗保险欺诈研究现状分析[J].保险研究, 2010(12):115~122.
[4] Jiawei Han, Micheline Kamber, Jian Pei. Data mining:concepts and techniques[M]. 3nd edition. singapore: Elsevier(Singapore), 2012.
[5] V Chandola, A Banerjee, V Kumar. Outlier detection:A survey[R]. Technical Report,University of Minne-sota, 2007.

备注/Memo

备注/Memo:
收稿日期:2017-06-21
作者简介:李飞(1983- ) ,男,四川盐源人,内江师范学院教师,工学硕士,研究方向:数据挖掘、机器学习;吴丽(1976- ) ,女, 四川内江人,内江师范学院高级实验师,硕士,研究方向:软件工程,计算机应用技术
更新日期/Last Update: 2017-09-06