Medical insurance abnormal data on-line intelligent detection method

Abstract

The invention discloses a medical insurance abnormal data on-line intelligent detection method, which comprises the steps of S1, acquiring a training data set; S2, learning in the on-line manner; S3, on-line detecting in the on-line manner. According to the medical insurance abnormal data on-line intelligent detection method, firstly, original medical insurance data are screened to obtain a suspicious characteristic data cluster. Secondly, data in the suspicious characteristic data cluster are artificially audited and annotated, and annotated data are subjected to on-line learning by means of a training model. The well trained model is used for automatically detecting medical insurance data in the on-line manner, so that the labor cost of the manual detection is greatly reduced. At the same time, the detection accuracy of abnormal medical insurance data is effectively improved. Therefore, the use of the medical healthcare insurance fund is more reasonably applied to the medical services of ordinary people, and the fraudulent conduct in the medical insurance field is avoided.

Description

technical field [0001] The invention relates to an online intelligent detection method for medical insurance abnormal data. Background technique [0002] With the development of society and economy, in order to provide a better medical insurance environment for the people, the country has made better and better medical insurance policies. However, there are always some people who use various means to defraud medical insurance. [0003] The current medical insurance, the overall business operation and management of the industry is relatively extensive, lacking risk control; extensive claim settlement services and terms of compensation, lack of in-depth analysis of disease treatment, risk control of medical expenses, and reasonable judgment of medical services, resulting in A large number of fraudulent and unreasonable medical treatments have seriously damaged the rights and interests of other people who really need medical insurance treatment, and have jeopardized the nation...

AI Technical Summary

Problems solved by technology

[0003] The current medical insurance, the overall business operation and management of the industry is relatively extensive, lacking risk control; extensive claim settlement services and terms of compensation, lack of in-depth analysis of disease treatment, risk control of medical expenses, and reasonable judgment of medical services, resulting in A large number of fraudulent and unreasonable medical treatment have seriously damaged the rights and interests of other people who really need medical insurance treatment, and jeopardized the national medical insurance system

[0004] For this kind of problem, it is generally possible to help social security institutions detect abnormal data by analyzing the medical treatment data of medical insurance personnel; , a waste of manpower, and it is difficult to guarantee the accuracy. In addition, the daily data of the hospital is dynamically increasing, which increases the difficulty of manual processing; therefore, the current use of data mining technology is an important intelligent means to find abnormal medical insurance data

[0005] Data mining technology is an important technical means for discovering potential information of data, revealing hidden models, and predicting development trends; it has been widely used and achieved success in industries such as finance, telecommunications, commerce, and insurance; the medical insurance industry at home and abroad often involves the formulation of targeted marketing strategies, Customer loyalty analysis, cross-selling of insurance products, etc.; based on Australian medical institutions, Marisa et al. from IBM Research Center used association rules and neural segmentation technology to obtain unknown patterns from GB-level data; MohitKumar et al. used data mining and machine learning techniques , to predict and prevent payment errors, anomalies, and fraud detection in the process of processing medical insurance claims by insurance companies; domestic research mainly focuses on fund risks and controlling the growth of medical expenses, using simple and rule-based data screening methods, lacking comprehensive and powerful Big data analysis support

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