CN113554519A - Medical insurance expenditure risk analysis method and system - Google Patents
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Abstract
The invention provides a medical insurance expenditure risk analysis method and system, which are used for acquiring medical insurance related business data; performing data correlation analysis to determine an analysis data set; screening the analysis data set to determine an evaluation index; selecting proper characteristics according to the importance of the evaluation indexes on the medical insurance expenditure risk, and constructing a characteristic project; and determining a final medical insurance expenditure risk factor in the characteristic engineering by using expert experience, and performing medical insurance fund expenditure condition risk early warning by using the medical insurance expenditure risk factor. The invention can more accurately judge the medical insurance fund expenditure condition and can not influence the normal fund expenditure at the same time.
Description
Technical Field
The invention belongs to the technical field of data analysis, and particularly relates to a medical insurance expenditure risk analysis method and system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Under the background condition that the population is aging increasingly, the balance of income and expenditure of basic medical insurance funds gradually faces the situation that the fund expenditure is increased rapidly and continuously increases, and the problem that the urgent attention is needed is how to adjust and perfect the fund expenditure mechanism to ensure the long-term benign operation of the basic medical insurance funds. However, the current wind control analysis aiming at medical insurance fund expenditure mostly adopts manual analysis, so that the efficiency is low, the accuracy is not high, risk factors cannot be effectively analyzed, too much time and labor cost are wasted, and the effect is not satisfactory.
Disclosure of Invention
The invention aims to solve the problems and provides a medical insurance expenditure risk analysis method and system.
According to some embodiments, the invention adopts the following technical scheme:
a medical insurance expenditure risk analysis method comprises the following steps:
acquiring medical insurance related business data;
performing data correlation analysis to determine an analysis data set;
screening the analysis data set to determine an evaluation index;
selecting proper characteristics according to the importance of the evaluation indexes on the medical insurance expenditure risk, and constructing a characteristic project;
and determining a final medical insurance expenditure risk factor in the characteristic engineering by using expert experience, and performing medical insurance fund expenditure condition risk early warning by using the medical insurance expenditure risk factor.
In an alternative embodiment, the business data includes current medical insurance income data, current medical insurance expenditure, fund income, fund expenditure and fund surplus data.
As an alternative embodiment, the correlation analysis is performed using pearson correlation coefficients when performing the data correlation analysis.
As a further limitation, the medical insurance fund expenditure data is set as X, and other medical insurance internal business data such as fund income, fund surplus, number of participating insurers and the like are set as Y1,Y2,Y3,...YnFinding X and Y respectively1,Y2,Y3,...YnPearson correlation coefficient between:
wherein
The covariance between X and Y is shown, Var is the variance, the higher the correlation degree is, the Pearson correlation coefficient tends to 1 or-1, the trend to 1 shows that the correlation is positive, and the trend to-1 shows that the correlation is negative; if the correlation coefficient is equal to 0, indicating that no linear correlation exists between the correlation coefficient and the correlation coefficient;
and ranking according to the correlation coefficient, and acquiring a data set required by medical insurance expenditure risk factor analysis ranked before the set name.
As an alternative embodiment, the screening of the analysis data set and the specific process of determining the evaluation index include: and analyzing the data set to perform index analysis, manually judging according to expert experience, and screening appropriate indexes for analyzing the medical insurance expenditure risk factors.
As an alternative implementation, the specific process of constructing the feature engineering includes performing feature screening through a random forest, analyzing the contribution of each feature on each tree in the random forest, taking an average value, comparing the contribution sizes of different features, and selecting a kini index as a measure index of the contribution degree, thereby completing the construction of the feature engineering.
As a further limitation, assume that there are m features related to medical insurance, which is set as X1,X2,X3,...,XcTo measure the importance of features to the analysis of the risk factors for medical insurance expenses, we will calculate each feature XjCoefficient of kini score of
Namely, the average change amount of the node splitting purities of the jth characteristic in all decision trees of the random forest is calculated by the following formula:
wherein K denotes K classes, pmkRepresenting the proportion of the class k in the node m;
characteristic XjThe importance of the node m, i.e., the Gini index change amount before and after the node m branches, is:
wherein, GIlAnd GIrRespectively representing Gini indexes of two new nodes after branching; if, feature XjThe node appearing in decision tree i is set M, then XjThe importance in the ith tree is:
if n trees are arranged in random forest, then
And finally, normalizing all the obtained importance scores:
and (4) obtaining index construction characteristic engineering of the set value before ranking according to the ranking of the Gini coefficient.
A medical insurance expense risk analysis system, comprising:
the data acquisition module is used for acquiring medical insurance related business data;
a correlation analysis module for performing a data correlation analysis to determine an analysis data set;
the index selection module is used for screening the analysis data set and determining an evaluation index;
the characteristic engineering module is used for selecting proper characteristics according to the importance of the evaluation indexes on the medical insurance expenditure risk and constructing characteristic engineering;
and the risk analysis module is used for determining a final medical insurance expenditure risk factor in the characteristic engineering by using expert experience, and performing medical insurance fund expenditure condition risk early warning by using the medical insurance expenditure risk factor.
A computer readable storage medium having stored therein a plurality of instructions adapted to be loaded by a processor of a terminal device and to carry out the steps of the above-mentioned method.
A terminal device comprising a processor and a computer readable storage medium, the processor being configured to implement instructions; the computer readable storage medium is used for storing a plurality of instructions adapted to be loaded by a processor and to perform the steps of the above-described method.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, through collecting the business data, different dimensional characteristics are screened out, correlation analysis is carried out on the data, and the medical insurance fund expenditure risk factor is given, so that the health condition of medical insurance fund expenditure is more accurately judged, and meanwhile, the normal expenditure of the fund is not influenced.
The invention can push fund collection and payment results of different areas and all the years of crowds to relevant business personnel, and meanwhile, carries out risk reminding aiming at risk factors with larger fund expenditure influence, thereby realizing risk prevention and control.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a schematic structural diagram of an analysis system for medical insurance expenditure risk factors according to the present invention.
FIG. 2 is a flow chart of the medical insurance expense risk factor analysis method of the present invention.
The specific implementation mode is as follows:
the invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
A method for analyzing medical insurance expenditure risk, as shown in fig. 2, comprising the steps of:
the method comprises the following steps: collecting and integrating internal business data;
step two: acquiring a data set required by medical insurance expenditure risk factor analysis;
step three: selecting a proper index from the target data set;
step four: selecting proper characteristics and constructing a characteristic project;
step five: and outputting the medical insurance expenditure risk factors, and performing medical insurance fund expenditure condition risk early warning by using the medical insurance expenditure risk factors.
In this embodiment, in the first step, the collected and integrated internal business data mainly includes the current medical insurance income data, the current medical insurance expenditure, the fund income, the fund expenditure, the fund surplus data, and the like.
In this embodiment, in step two, a correlation analysis is performed, using a pearson correlation coefficient, which is a correlation (linear correlation) for measuring the correlation between two variables X and Y, and has a value between-1 and 1.
The medical insurance fund expenditure data is set as X, and other medical insurance internal business data such as fund income, fund surplus, insured people number and the like are set as Y1,Y2,Y3,...YnFinding X and Y respectively1,Y2,Y3,...YnThe pearson correlation coefficient between them, the formula is as follows:
wherein
Representing the covariance between X and Y, Var representing the variance, the higher the correlation, the pearson correlation coefficient tends to have a value of 1 or-1 (tending to 1 means that they are positively correlated and tending to-1 means that they are negatively correlated); if the correlation coefficient is equal to 0, it indicates that there is no linear correlation between them.
And ranking according to the correlation coefficient, and acquiring a data set required by medical insurance expenditure risk factor analysis and ranked at the top.
In the third step, index analysis is performed on the selected medical insurance expense risk factor analysis data set, the index analysis is mainly performed according to manual judgment and manual judgment according to experience, and a proper index is screened for medical insurance expense risk factor analysis.
In the fourth step, feature screening is performed through a random forest, how much each feature contributes to each tree in the random forest is analyzed, then an average value is taken, finally, the contribution sizes of different features are compared, and a Gini index (gi n i) is selected as a measure index of the contribution degree, so that the construction of the feature engineering is completed.
Assuming that there are m characteristics such as medical insurance fund income, fund surplus, number of insured persons, etc., it is set as X1,X2,X3,...,XcTo measure the importance of features to the analysis of the risk factors for medical insurance expenses, we will calculate each feature XjCoefficient of kini score of
Namely, the average change amount of the node splitting purities of the jth characteristic in all decision trees of the random forest is calculated by the following formula:
wherein K denotes K classes, pmkIndicating the proportion of class k in node m.
Intuitively, two samples are randomly drawn from node m at any time, with the probability that the class labels are inconsistent.
Characteristic XjThe importance of the node m, i.e., the Gini index change amount before and after the node m branches, is:
wherein, GIlAnd GIrRespectively representing the Gini indexes of two new nodes after branching.
If, feature XjThe node appearing in decision tree i is set M, then XjThe importance in the ith tree is:
if n trees are arranged in random forest, then
And finally, normalizing all the obtained importance scores:
and obtaining the index structure characteristic engineering with the top ranking according to the ranking of the Gini coefficients.
In this embodiment, based on expert experience, for features in feature engineering, factors with risk of medical insurance expenditure, such as fund balance, participation types and other features, are weighted and scored, and risk factors with influence on fund expenditure greater than a set value are output for further risk analysis and early warning.
A medical insurance expenditure risk factor analysis system, as shown in fig. 1, comprising:
the risk factor library module is used for collecting and integrating relevant external macroscopic data and internal business data;
the data acquisition module is used for selecting data with high correlation with medical insurance expenditure through correlation analysis;
the index selection module is used for selecting a proper index;
the characteristic engineering module is used for selecting proper characteristics and constructing characteristic engineering;
and the risk analysis module is used for outputting the medical insurance expenditure risk factors.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. A medical insurance expenditure risk analysis method is characterized by comprising the following steps: the method comprises the following steps:
acquiring medical insurance related business data;
performing data correlation analysis to determine an analysis data set;
screening the analysis data set to determine an evaluation index;
selecting proper characteristics according to the importance of the evaluation indexes on the medical insurance expenditure risk, and constructing a characteristic project;
and determining a final medical insurance expenditure risk factor in the characteristic engineering by using expert experience, and performing medical insurance fund expenditure condition risk early warning by using the medical insurance expenditure risk factor.
2. The medical insurance expenditure risk analysis method of claim 1, wherein: the business data comprises current medical insurance income data, current medical insurance expenditure, fund income, fund expenditure and fund surplus data.
3. The medical insurance expenditure risk analysis method of claim 1, wherein: and when the data correlation analysis is carried out, the correlation analysis is carried out by adopting a Pearson correlation coefficient.
4. A medical insurance expenditure risk analysis method according to claim 3, wherein: the medical insurance fund expenditure data is set as X, and other medical insurance internal business data such as fund income, fund surplus, insured people number and the like are set as Y1,Y2,Y3,...YnFinding X and Y respectively1,Y2,Y3,...YnPearson correlation coefficient between:
wherein
The covariance between X and Y is shown, Var is the variance, the higher the correlation degree is, the Pearson correlation coefficient tends to 1 or-1, the trend to 1 shows that the correlation is positive, and the trend to-1 shows that the correlation is negative; if the correlation coefficient is equal to 0, indicating that no linear correlation exists between the correlation coefficient and the correlation coefficient;
and ranking according to the correlation coefficient, and acquiring a data set required by medical insurance expenditure risk factor analysis ranked before the set name.
5. The medical insurance expenditure risk analysis method of claim 1, wherein: screening the analysis data set, wherein the specific process for determining the evaluation index comprises the following steps: and analyzing the data set to perform index analysis, manually judging according to expert experience, and screening appropriate indexes for analyzing the medical insurance expenditure risk factors.
6. The medical insurance expenditure risk analysis method of claim 1, wherein: the specific process of constructing the feature engineering comprises the steps of screening features through random forests, analyzing the contribution of each feature on each tree in the random forests, averaging, comparing the contribution of different features, and selecting a kini index as a measure index of the contribution degree, so that the construction of the feature engineering is completed.
7. The medical insurance expenditure risk analysis method of claim 6, wherein: assume m characteristics related to medical insurance, set them as X1,X2,X3,...,XcTo measure the risk factor of a characteristic to medical insurance expenditureFor the importance of the sub-analysis, we will calculate each feature XjCoefficient of kini score of
Namely, the average change amount of the node splitting purities of the jth characteristic in all decision trees of the random forest is calculated by the following formula:
wherein K denotes K classes, pmkRepresenting the proportion of the class k in the node m;
characteristic XjThe importance of the node m, i.e., the Gini index change amount before and after the node m branches, is:
wherein, GIlAnd GIrRespectively representing Gini indexes of two new nodes after branching; if, feature XjThe node appearing in decision tree i is set M, then XjThe importance in the ith tree is:
if n trees are arranged in random forest, then
And finally, normalizing all the obtained importance scores:
and (4) obtaining index construction characteristic engineering of the set value before ranking according to the ranking of the Gini coefficient.
8. A medical insurance expenditure risk analysis system is characterized in that: the method comprises the following steps:
the data acquisition module is used for acquiring medical insurance related business data;
a correlation analysis module for performing a data correlation analysis to determine an analysis data set;
the index selection module is used for screening the analysis data set and determining an evaluation index;
the characteristic engineering module is used for selecting proper characteristics according to the importance of the evaluation indexes on the medical insurance expenditure risk and constructing characteristic engineering;
and the risk analysis module is used for determining a final medical insurance expenditure risk factor in the characteristic engineering by using expert experience, and performing medical insurance fund expenditure condition risk early warning by using the medical insurance expenditure risk factor.
9. A computer-readable storage medium characterized by: in which a plurality of instructions are stored, said instructions being adapted to be loaded by a processor of a terminal device and to carry out the steps of the method according to any one of claims 1 to 7.
10. A terminal device is characterized in that: the system comprises a processor and a computer readable storage medium, wherein the processor is used for realizing instructions; a computer readable storage medium for storing a plurality of instructions adapted to be loaded by a processor and for performing the steps of the method according to any of claims 1-7.
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