Medical insurance fraud behavior detection method and early warning device based on graph clustering analysis

A detection method and graph clustering technology, which is applied in data processing applications, finance, instruments, etc., can solve the problems of not passing through the original data and high labeling costs, and achieve the effects of saving medical insurance funds, improving work efficiency, and saving time and space expenses

Pending Publication Date: 2021-06-01
SHANDONG IND TECH RES INST OF ZHEJIANG UNIV
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However, due to the huge amount of medical insurance data and the h

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  • Medical insurance fraud behavior detection method and early warning device based on graph clustering analysis

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[0040] In order to further understand the present invention, a method for detecting fraudulent medical insurance behavior based on graph cluster analysis and an early warning device provided by the present invention will be described in detail below in conjunction with specific embodiments, but the present invention is not limited thereto. Non-essential improvements and adjustments made under the core guiding ideology of the invention still belong to the protection scope of the present invention.

[0041] A method for detecting fraudulent medical insurance behavior based on graph cluster analysis, comprising the following steps:

[0042] S1. Data extraction and desensitization, extract all the settlement records of the insured from the medical insurance database, divide and preprocess by institution. Desensitization includes: desensitizing the raw data with personal sensitive information in the government medical insurance system, exporting it to the working system and transfe...

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Abstract

The invention belongs to the technical field of data mining of medical health big data, and particularly relates to a medical insurance fraud behavior detection method and early warning device based on graph clustering analysis. The invention discloses a medical insurance fraud behavior detection method based on graph clustering analysis. The method comprises the following steps: S1, data extraction and desensitization; s2, preprocessing and mapping; s3, pruning the graph model; s4, performing graph clustering analysis; and S5, threshold post-processing and result extraction. According to the graph clustering analysis-based medical insurance fraud behavior detection method and the early warning device provided by the invention, continuously updated medical insurance big data is used as input, and suspicious group insurance fraud behaviors can be found in time through calculation.

Description

technical field [0001] The invention belongs to the technical field of data mining of medical and health big data, and in particular relates to a method for detecting fraudulent medical insurance behavior and an early warning device based on graph cluster analysis. Background technique [0002] In recent years, with the continuous improvement of people's living standards, the number of people participating in China's basic medical insurance has reached 1.35 billion, and the insurance participation rate has exceeded 95%. Medical expenditures have increased from 1.45 trillion in 2008 to 4.1 trillion in 2015. , with an average annual growth rate of 16%, far exceeding the growth rate of my country's GDP over the same period. One of the most important reasons is Medicare Fraud, Resource Waste and Substance Abuse (FWA). Fraud accounts for 3-10% of Medicare (approximately between $19 billion and $65 billion) annually in the United States, according to FBI estimates. As the populat...

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Application Information

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IPC IPC(8): G06Q40/08G06K9/62
CPCG06Q40/08G06F18/2323
Inventor 吴健姜晓红应豪超徐黎明
Owner SHANDONG IND TECH RES INST OF ZHEJIANG UNIV
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