Patient feature depiction method and false hospitalization behavior detection method based on the patient feature depiction method

Abstract

The invention discloses a patient feature depiction method and a false hospitalization behavior detection method based on the patient feature depiction method to solve the problem of insurance fraud behavior of false hospitalization in medical insurance. By combining statistics with machine learning, a hospitalization behavior of a patient can be effectively depicted so as to reduce artificial determining workload. The patient feature depiction method comprises the depictions for patient hospitalization features and patient basic personal information. The false hospitalization behavior detection method constructs a false hospitalization behavior model, which is constructed by the four procedures of feature depiction, cluster analysis, artificial screening and machine learning for model-setting. The false hospitalization behavior detection method effectively screens a false hospitalization behavior by utilizing the combination of the statistics and the machine learning, performs the feature depiction of the hospitalization behavior of the patient, effectively depicts the patient, screens out outliers by utilizing a method of clustering, and clustering can greatly reduce normal data so that the workload at an artificial screening stage is reduced; after the false hospitalization behavior model is set up, the hospitalization behavior of the patient is predicted by utilizing the same.

Description

technical field [0001] The invention relates to a method for characterizing patient characteristics and a method for detecting bed-hanging behavior based on the method for characterizing patients. 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 defraud medical insurance through some false medical treatment behaviors, seriously damaging the rights and interests of other people who really need medical insurance treatment. Generally, medical insurance personnel's medical data can be used to help social security institutions to further detect abnormal data, but hospitals generate a large amount of original medical insurance data every day. If abnormal detection is only done manually, it will not only be slow and waste manpower, but also difficult to guarantee. Accuracy. ...

AI Technical Summary

Problems solved by technology

In many cases, it is not known in advance how many categories a given data set should be divided into. The selection of the initial clustering center has a great impact on the clustering results. Once the initial value is not well selected, it may not be possible to obtain effective clustering. Clustering results; the K-means algorithm needs to continuously adjust the sample classification and continuously calculate the adjusted new cluster center, so when the amount of data is very large, the time overhead of the algorithm is very large

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