WO2020107909A1

WO2020107909A1 - Method, apparatus and device for determining abnormal treatment expense, and computer storage medium

Info

Publication number: WO2020107909A1
Application number: PCT/CN2019/097447
Authority: WO
Inventors: 黄越; 陈明东
Original assignee: 平安医疗健康管理股份有限公司
Priority date: 2018-11-30
Filing date: 2019-07-24
Publication date: 2020-06-04
Also published as: CN109670971A

Abstract

Disclosed in the present application are a method, apparatus and device for determining an abnormal treatment expense, and a computer storage medium. The method comprises: obtaining multiple pieces of historical treatment data, and transmitting each piece of the historical treatment data to multiple preset classification models for classification to generate each classification result; detecting each classification result to determine a target classification result, and determining the preset classification model generating the target classification result as a target classification model; and upon receiving current treatment data, transmitting the current treatment data to the target classification model, and determining whether the current treatment expense in the current treatment data is abnormal. According to this solution, the target classification result generated on the basis of the classification of treatment big data in the target classification model is the classification of correspondences among different disease information, therapeutic schemes, and expense information, and has relatively high accuracy, so that the abnormity determination for the current treatment data by the target classification model is more accurate and effective.

Description

Judgment method, device, equipment and computer storage medium for abnormal medical expenses The

This application requires the priority of the Chinese patent application submitted to the China Patent Office on November 30, 2018, with the application number 201811462247.X and the invention titled "Judgment method, device, equipment and computer storage medium for abnormal medical expenses". The entire content is incorporated into the application by reference.

Technical field

This application mainly relates to the technical field of medical systems, and in particular, to a method, device, equipment, and computer storage medium for judging the cost of abnormal medical treatment.

Background technique

With the development of the social security system, more and more people have medical insurance and use medical insurance for medical treatment; when sick patients use medical insurance for medical treatment in various medical institutions, part of the medical expenses incurred are reimbursed through medical insurance, and the other part is paid by the patient; The cost of treatment differs between patients due to different diseases and treatment options; for example, for patients with the same type II diabetes but with a large difference in treatment options, the cost of treatment varies greatly.

For some patients who currently use medical insurance for medical treatment, such as using expensive drugs that are far higher than their symptoms during the treatment process, or prescribing other drugs that are inconsistent with their symptoms, etc., which makes the patient's medical expenses unreasonably increase. Abnormal consultation costs; judgment of such abnormal consultation costs is particularly important in preventing malicious use of medical insurance. However, due to the large individual differences of patients, there is no effective judgment mechanism for the cost of abnormal treatment, which makes it impossible to accurately identify the malicious use of medical insurance.

Summary of the invention

The main purpose of the present application is to provide a method, device, equipment and computer storage medium for judging the cost of abnormal medical treatment, aiming to solve the problem of lack of an effective judgment mechanism for the cost of abnormal medical treatment in the prior art.

In order to achieve the above object, the present application provides a method for judging the cost of abnormal medical treatment, which includes the following steps:

Obtain multiple pieces of historical medical data, and transmit each of the historical medical data to multiple preset classification models for classification, and generate each classification result, wherein the multiple preset classification models are set according to different clustering algorithms, And each of the classification results is generated by the plurality of preset classification models based on the patient information, the consultation information, and the historical consultation fee in each of the historical consultation data;

Detecting each of the classification results, determining a target classification result, and determining the preset classification model that generates the target classification result as the target classification model;

When receiving the current medical consultation data regularly sent by the medical institution, the current medical consultation data is transmitted to the target classification model, and it is judged whether the current medical consultation fee in the current medical consultation data is abnormal.

In addition, in order to achieve the above object, the present application also proposes a device for judging abnormal medical expenses, the device for judging abnormal medical expenses includes:

The generating module is used to obtain multiple pieces of historical medical data, and transmit each of the historical medical data to a plurality of preset classification models for classification to generate each classification result, wherein the plurality of preset classification models are based on different A clustering algorithm is set, and each of the classification results is generated by the plurality of preset classification models based on patient information, consultation information, and historical consultation fees in each of the historical consultation data;

A determining module, configured to detect each of the classification results, determine a target classification result, and determine the preset classification model that generates the target classification result as a target classification model;

The judging module is used to transmit the current medical treatment data to the target classification model when receiving the current medical treatment data regularly sent by the medical institution, and determine whether the current medical treatment fee in the current medical treatment data is abnormal.

In addition, in order to achieve the above object, the present application also proposes a device for judging abnormal medical expenses, the device for judging abnormal medical expenses includes: a memory, a processor, a communication bus, and judgment of abnormal medical expenses stored on the memory program;

The communication bus is used to implement connection communication between the processor and the memory;

The processor is used to execute the procedure for judging the abnormal medical expenses, so as to realize the steps of the method for judging the abnormal medical expenses.

In addition, to achieve the above object, the present application also provides a computer storage medium that stores one or more programs, and the one or more programs can be executed by one or more processors as described above. The steps of the method for judging the cost of a doctor.

In the method for judging the cost of abnormal visits in this embodiment, a large amount of historical visit data is used as sample data of each preset classification model, and transmitted to the preset classification model for classification to obtain the classification result classified by each preset classification model; Each classification result is tested to determine the most accurate target classification result, and the preset classification model that generates the target classification result is determined as the target classification model; then the received current medical data is transmitted to the target classification model, The abnormality of the current consultation cost in the current consultation data is judged by the target classification result therein. The target classification result in the target classification model is the classification of the corresponding relationship between various disease information, treatment options and cost information; it is generated from a large amount of real and effective historical visit data, with high accuracy, making it The abnormality judgment is more accurate and effective, which improves the accuracy of abnormal medical expenses as the basis for determining malicious use of medical insurance.

BRIEF DESCRIPTION

FIG. 1 is a schematic flowchart of a first embodiment of a method for judging abnormal medical expenses of the present application;

2 is a schematic diagram of a functional module of the first embodiment of the apparatus for judging abnormal medical expenses of the present application;

FIG. 3 is a schematic diagram of the device structure of the hardware operating environment involved in the method of the embodiment of the present application.

The implementation, functional characteristics and advantages of the present application will be further described in conjunction with the embodiments and with reference to the drawings.

detailed description

It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

This application provides a method for judging the cost of abnormal medical treatment.

Please refer to FIG. 1. FIG. 1 is a schematic flowchart of a first embodiment of a method for judging an abnormal treatment fee of an application. In this embodiment, the method for judging the abnormal medical expenses includes:

Step S10: Acquire multiple pieces of historical consultation data, and transmit each of the historical consultation data to multiple preset classification models for classification to generate various classification results, wherein multiple preset classification models are based on different clusters An algorithm is set, and each of the classification results is generated by a plurality of the preset classification models based on the patient information, the consultation information, and the historical consultation fee in each of the historical consultation data;

The method for judging the abnormal medical expenses of this application is applied to the server, and it is suitable for judging the abnormality of the medical expenses in medical institutions through the server; the medical expenses are the expenses for the patients to go to the medical institutions for certain diseases. Institutions include but are not limited to general hospitals, traditional Chinese medicine hospitals, specialized hospitals and other types of hospitals, as well as clinics, health centers, pharmacies, etc. The types of diseases suffered by patients with different diseases are different, and the treatment options for the same disease are also very different, which makes the cost of medical treatment different when treating various diseases with various treatment options; but for For the same type of illness and similar treatment plan, the cost of the consultation is similar; thus, the current real-time consultation cost can be judged by the usual general consultation cost of various types of diseases with various treatment plans. Sex; for example, when treating patients with hypertensive heart disease and heart failure under the plan A, 90% of the patients' cost of treatment was between a1 and a2, so that the cost of the consultation can be used as a reference to determine the real-time cost of treatment Is it abnormal? There is a communication connection between the server and each medical institution. Before judging the abnormality of the medical expenses, you need to obtain the historical data of various diseases in the medical institution; use the historical data related to the medical disease as history Visiting data, including various data such as the patient's age, gender, illness, visiting time, medication plan, and visiting cost. The server sends a request to each medical institution to obtain historical visit data. After receiving the request, each medical institution transmits multiple copies of the historical visit data stored in it to the server. One of the visit data corresponds to one visit Historical visit data.

Due to the variety of historical visit data uploaded by medical institutions, they need to be classified and processed first to reflect the relationship between various types of diseases and treatment options and visit costs. Specifically, a plurality of preset classification models for classifying historical visit data are preset in the server. Because different preset classification models have different classification methods, the accuracy of classification results varies; in order to make the classification results more accurate For accuracy, multiple historical classification data are simultaneously classified through multiple preset classification models to obtain multiple classification results; then the classification results with the highest accuracy are determined from the multiple classification results. A preset classification model is used to judge the abnormality of real-time medical expenses to ensure the accuracy of judgment. The preset classification model is set according to the clustering algorithm, and the difference of the preset classification model is formed according to the difference of the clustering algorithm; such as the corresponding clustering algorithm K-Means (K-means) clustering, mean drift clustering 3. Consolidate hierarchical clustering to form three different types of preset classification models. Transmit the acquired multiple pieces of historical visit data as sample data to each preset classification model, classify each historical data through each preset classification model, and generate classification results in each preset classification model. The results reflect the correspondence between various disease information, treatment options and cost information. Specifically, each historical visit data is transmitted to multiple preset classification models for classification, and the steps of generating each classification result include:

Step S11: Transmit each of the historical visit data to a plurality of preset classification models, and read the patient information and visit information in each of the historical visit data by each of the preset classification models to transfer the patient Each historical visit data whose similarity of the information and the visit information is higher than a preset value is divided into the same group category, and the historical visit cost of the historical visit data in each of the group categories is read;

Understandably, for the same type of disease, different patients may use different treatment plans due to differences in age, gender, etc., that is, the treatment plan varies according to the patient information; thus, each historical data is transmitted to the server After each preset classification model, the patient information and consultation information are read by each preset classification model; the patient information is personal information that may affect the treatment plan such as the patient's age, gender, past medical history, family medical history, etc., The visit information is various treatment-related data generated by the patient during the visit, such as surgical treatment or drug treatment, type of disease, type of medication, amount of medication, treatment course, etc. At the same time, each preset classification model compares the patient information and the consultation information between each piece of historical consultation data, and judges the similarity between each piece of historical consultation data between the patient information and the consultation information; The degree of similarity between information and medical information. The similarity between the patient information is whether the age of each patient is within a certain range, whether the gender is the same, whether the family medical history is similar, etc.; the similarity between the medical information is whether the symptoms of each patient are the same, whether the treatment is the same, Whether the type of medication is the same, whether the amount of medication is within a certain range, etc. The similarity between the patient information and the similarity between the visit information represent the similarity of the treatment plan between the patients. The ratio between the amount of similar information in patient information and consultation information and the total amount of judgment information can be used to determine the similarity of patient information and the similarity of consultation information; for example, the total amount of judgment information in patient information includes age, gender, There are three items of family medical history, and the amount of similar information is age and gender, the similarity is 2/3.

In order to determine the degree of similarity, a preset value is set in advance, and the similarity between each piece of patient information obtained by comparison is compared with the preset value to determine whether the similarity of patient information is greater than the preset value. If it is larger, it means that the compared patient information has similarity, otherwise it has no similarity. At the same time, compare the similarity between the obtained medical information and the preset value to determine whether the similarity of the medical information is greater than the preset value, if it is greater, it means that the compared medical information is similar, otherwise it is not similar Sex. When the patient information with similarity and the medical information with similarity are derived from the same historical medical data, the historical medical data are divided into the same group, that is, the historical medical data in the same group It looks like patient information and medical information. For example, the historical visit data B includes patient information b1, visit information b2, the historical visit data C includes patient information c1, visit information c2, and the historical visit data D includes patient information d1, visit information d2; It is not similar to d1, but b2 is similar to c2, and c2 is similar to d2. Due to the similar patient information b1 and c1, and the similar visit information b2 and c2, they are derived from the same historical visit data B and C , Then divide B and C into the same group.

At the same time, each historical consultation data also includes the consultation cost of the patients. After dividing each historical consultation data into different groups according to the similarity, the historical consultation cost of each historical consultation data in each group is read , To characterize the cost of each historical visit that can be used for reference in each group of similar patient information and visit information.

Step S12: Read each of the group categories divided by each of the preset classification models, and the historical medical expenses corresponding to each of the group categories to form each model group category set, and determine each model group category set as The classification result of each of the preset classification models.

Further, after each preset classification model divides each historical consultation data into different group categories and reads each historical consultation fee in each of the divided group categories, the server divides each preset classification model into Each group class and the historical visit fee corresponding to each group class are read, and a model group class set is formed between each group class corresponding to the preset classification model and each historical visit fee corresponding to each group class. Through the set of model groups, there is a correspondence between the preset classification model and the groups classified by the preset classification model, and the historical consultation cost of each historical consultation data in each group category; the correspondence is essentially the preset classification Correspondence of the model between each disease information, treatment plan and cost of treatment. The model group class set is used as the classification result of the preset classification model, and one model group class set corresponds to the classification result divided by the preset classification model.

Step S20, detecting each of the classification results, determining a target classification result, and determining the preset classification model that generates the target classification result as a target classification model;

Furthermore, after determining each classification result generated by each preset classification model, the server needs to determine the most accurate classification result from each classification result. Because the classification results are based on the patient information and medical information generated by each group category, the accuracy of the classification results and the difference between the patient information and medical information between the different group categories, and the same group of patient information and medical information Similarity is related; the greater the difference between the patient information and the consultation information between different groups, and the higher the similarity between the patient information and the consultation information in the same group category, the better the accuracy of the classification results. The server detects the difference between different groups in each classification result and the similarity in the same group, and determines the most accurate target classification result in each classification result. Since the target classification result is classified and generated by the preset classification model, the preset classification model that generates the target classification result is determined as the target classification model, so that the target classification result in the target classification model can be used later to perform anomalies on the cost of treatment Judgment.

Step S30: When receiving the current medical consultation data regularly sent by the medical institution, the current medical consultation data is transmitted to the target classification model, and it is judged whether the current medical treatment fee in the current medical consultation data is abnormal.

Further, when it is necessary to determine the abnormality of the medical expenses of the sick patient, the server sends an acquisition request to the medical institution to request the current medical treatment data requiring abnormality determination; or the medical institution may periodically send the required diagnosis data to the server The current medical data of abnormality judgment. When the server receives the current medical data, it will transmit the current medical data to the target classification model. The current medical data includes the characteristic data of the medical patient's medical treatment and the current medical cost; the target classification model looks for and The group corresponding to the medical characteristic data, and then the historical medical cost for reference in the corresponding group type, to determine whether the current medical cost in the current medical data is abnormal. Because there are multiple similar historical visit data in the group category, and each historical visit data includes historical visit cost, the numerical interval formed between the historical visit cost with the smallest value and the historical visit cost with the largest value is used as the reference interval , If the current consultation cost is within the reference interval, it means that the current consultation cost matches each historical consultation cost, and it can be determined that the current consultation cost is normal; and if the current consultation cost is not within the reference interval, it means that the current consultation cost and each historical consultation The costs do not match, and it is determined that the current cost of medical treatment is abnormal.

Further, in another embodiment of the method for judging abnormal medical expenses of the present application, the step of detecting each of the classification results and determining the target classification results includes:

Step S21: Compare the inter-group spacing between each of the model group class sets to generate the inter-group comparison result of each model group class set, and determine each institute according to the inter-group comparison result The first model group class set with the largest spacing between the groups;

Furthermore, when the classification results generated by each preset classification model are tested for differences between different groups and similarities in the same group, to determine the most accurate target classification result among the classification results, the The difference between different group categories is used as the inter-group spacing between each group category, reflecting the difference between the group category and the group category; the larger the interval between the groups, the greater the difference, the more the classification of each group category accurate. For example, the classification results divided by the preset classification model include three groups of M, N, and K. If the difference between M and N, M and K, and N and K is greater, the distance between groups is greater; It means that the more obvious the boundary between the three groups is, the more accurate the classification is.

Specifically, in each model group class set as a result of each classification, group comparisons are performed in units of two non-repeating group classes to generate the group-to-group spacing between each group class; for the above group class M, For N and K, group comparisons are made in units of M and N, M and K, and N and K, respectively, and the spacing between the generated groups is m, n, and k, respectively. When it needs to be explained, in the process of comparison, different types of data in each group are compared with each other, and the minimum value of the compared difference is used as the distance between groups. As in the above M and N group categories, both M and N include age and gender data, in which the age boundaries in M and N overlap, M represents the disease patients between 20 and 30, and N represents 30~ Patients with diseases between 40; and genders in M and N are different, M represents female patients with disease, and N represents male patients with disease; because the difference between the number types of "sex" in M and N is 100 %, and there is an overlap of 30 between the "age" data types, which may affect the classification, and the difference between ages is used as the interval m between the groups of M and N. After generating the inter-group spacing between multiple group classes involved in each model group class set, compare the spacing between each group group in each model group class set to compare the smallest group group Distance, that is, the distance between the two groups with the smallest difference between the two groups is used as the distance between the groups in the model group class set, which represents the difference in data between the group groups divided in the model group class set化度。 Degree.

After each model group class set generates the inter-group spacing between each group class, that is, after each model group class group set each group spacing, compare the spacing between each group to determine the size relationship between each group spacing ; The size relationship is the generated comparison result between each group, in order to characterize the accuracy of the classification of each model group set by each model group class set by the comparison result between each group. Because the larger the inter-group spacing, the more accurate the classification. The largest inter-group spacing is read from the comparison results between the groups that characterize the size relationship between the groups, and the model group class set that generates the largest inter-group spacing is used as the first model. Group class set, the first model group class set is the most accurate classification result among the classification results for classifying data between each group class.

Step S22, comparing the intra-group spacing of each group in each of the model group class sets to generate an in-group comparison result of each model group class set, and determining each of the groups according to the in-group comparison result The second model group class set with the smallest inner distance;

Further, when the classification results generated by the preset classification models are tested for differences between different groups and similarities in the same group, to determine the most accurate target classification result among the classification results, the same The similarity between group categories is used as the intra-group spacing in each group category, reflecting the similarity of data in the same group category; the smaller the intra-group spacing, the greater the similarity and the more accurate the classification of the data in each group category. For the three classifications of M, N, and K in the classification result divided by the above-mentioned preset classification model, if the data similarity in M, M, and K is greater, that is, the smaller the intra-group spacing; The more similar the data classification in the class, the more accurate the classification.

Specifically, since each model group class set involves multiple group classes, the data in each group class is inconsistent, so it is necessary to first determine the intra-group spacing of each group class in the same model group class set, that is, each group The similarity of the data in the class, and then the intra-group distance of the model group class set is determined by the intra-group distance of each group class. For example, for the above-mentioned group categories M, N, and K, compare the data in M, N, and K, respectively, and generate the inner-group spacings of p, w, and s, respectively. When it needs to be explained, in the process of comparison, similarity comparison is performed on various types of data in each group, and the minimum value of the compared similarity is used as the intra-group spacing. As mentioned above, M includes 20 items of age-type data, of which 10 items are 22 years old, and 2 items are 25 years old, 8 items are 29 years old, because the 8 items of data are 29 years old and The similarity between the 22-year-olds is smaller than the similarity between the two data at the age of 25 and the 22-year-old, so the similarity between the 29-year-old and the 22-year-old is regarded as the M within-group distance. After generating the intra-group spacing for the multiple groups involved in each model group class set, compare the intra-group spacing between each model group class set to compare the maximum intra-group distance, That is, the intra-group distance of the smallest similarity group class is used as the intra-group distance of each group class in the model group class set, and represents the degree of similarity of the data of each group class divided in the model group class set.

After each model group class set generates an intra-group spacing of each group class, that is, after each model group class group within each group spacing, compare the intra-group spacing to determine the size relationship between each group spacing; The size relationship is the generated comparison result within each group, and the comparison result within each group is used to characterize the accuracy of the classification of each model group set by each model group class set. Because the smaller the intra-group spacing, the more accurate the classification. The minimum intra-group spacing is read from the intra-group comparison results that characterize the relationship between the size of each group, and the set of model group classes that generate the smallest intra-group spacing is used as the second model Group class set, the second model group class set is the classification result with the most accurate classification of the data in each group class in the classification result.

Step S23: Determine whether the first model group class set and the second model group class set are the same model group class set, if they are the same model group class set, the same model The set of groups is determined as the target classification result.

Understandably, because the first model group class set is the most accurate classification result in the data classification among the group classes in the classification result, and the second model group class set is the most accurate classification data in the group results in the classification result Classification results; when the first model group class set and the second model group class set represent the same model group class set, it means that the preset classification model that generates the same model group class set is used to classify the historical visit data The data between the divided groups has a high difference, and the data in each group has a high similarity, and the classification accuracy is high. In order to judge whether the first model group class set and the second model group class set represent the same model group class set, if they represent the same model group class set, the same model group class set is determined as the target classification result, so as to The preset classification model corresponding to the target classification result with high representation classification accuracy judges the abnormality of the current medical data to improve the accuracy of judgment. When it is judged that the first model group class set and the second model group class set are not the same model group class set, it is necessary to filter from the comparison results between the groups and the comparison results for the data classification between the group classes. Difference, and the classification of data in the group class has a high similarity, and belongs to the target classification result of the same model group class set. Specifically, the step of determining whether the first model group class set and the second model group class set are the same model group class set includes:

Step S24, if the first model group class set and the second model group class set are not the same model group class set, then search for a group between the group comparison results that is greater than a first preset distance The result value, and the result value in the group that is greater than the second preset distance among the comparison results in each of the groups;

Further, the server is preset with a first preset pitch for determining the size of the spacing between groups and a second preset pitch for determining the size of the spacing within the group. When it is determined that the first model group class set and the second model group class set are not When characterizing the same set of model groups, compare the comparison results between the groups that characterize the size relationship between the groups with the first preset spacing to find out the results between the groups that are larger than the first preset spacing Value; the result value between the groups represents the distance between the groups that is greater than the first preset distance in the comparison results between the groups, and the data classification between the groups has a large difference. At the same time, the comparison results between the groups that characterize the relationship between the distances between the groups and the second preset distance are compared to find out the result values between the groups that are greater than the second preset distance from the comparison results between the groups; In the inter-group comparison result, the interval between the groups is greater than the second preset interval, and the data classification in each group has a large similarity.

Step S25, when the inter-group result value and the intra-group result value are derived from the same model group class set, the same model group class set is determined as the target classification result.

There may be multiple result values between groups greater than the first preset distance and within the group values greater than the second preset distance, and may be derived from different sets of model group classes; thus, the result values between each group and each group Compare the internal result values to determine whether there are inter-group result values and intra-group result values from the same model group class set among the inter-group result values and the intra-group result values. When there is an inter-group result value and an intra-group result value from the same model group class set, that is, the component result value and the in-group result value come from the same model group class set, it means that the preset that generates the same model group class set When the classification model divides the historical visit data into groups, the data between the divided groups has a high difference, and the data in each group has a high similarity, and its classification accuracy is more high. The same model group class set is determined as the target classification result, and the preset classification model corresponding to the target classification result with high representation classification accuracy is used to judge the abnormality of the current medical data to improve the accuracy of the judgment.

Further, the target classification result involves multiple group categories, and each group category has multiple pieces of historical consultation data, and each piece of historical consultation data has corresponding historical consultation fees; in order to make the historical consultation fees of each group category more Accurately reflect the abnormality of the current medical data, after determining the target classification result, integrate the historical medical expenses corresponding to each group in the target classification result for reference. Specifically, after detecting each classification result, the step of determining the target classification result includes:

Step S26: Generate an average historical cost of each group in the target classification result according to each historical medical expense corresponding to each group in the target classification result;

Because the same group of classes represents similar disease information and treatment plan information, and the historical cost of each historical consultation data in the same group of classes may be different, and only the difference between the minimum historical cost and the maximum historical cost The reference interval is used to judge the abnormality of the current consultation cost, which may make the judgment inaccurate due to the large range of the reference interval; therefore, the historical consultation costs corresponding to each group in the target classification result are averaged. Specifically, according to the division of each group in the target classification result, the number of historical consultation data in each group and the various historical consultation costs of each historical consultation data are counted; Add up to get the addition result, and then use the addition result and the number of historical consultation data in each group to make a ratio; the obtained ratio result is the historical cost average of each group, which represents the corresponding Reference cost of illness and treatment plan.

In step S27, the target classification result is updated according to each historical cost average value, so as to determine whether the current consultation cost in the current consultation data is abnormal based on the updated target classification result in the target classification model.

Further, because the target classification result is generated by comparing the classification results generated by each preset classification model, each classification result reads the historical medical expenses of the group class in the generation process, and does not involve the historical cost The average value; so that the generated target classification result does not involve the average historical cost of each group. Therefore, after the historical cost average value is generated for each group in the target classification result, the target classification result is updated with the historical cost average to form a correspondence between each group class in the target classification result and the historical cost average ; In order to follow up based on the target classification results updated in the target classification model, that is, the average value of the historical costs corresponding to each group of classes, to determine whether the current cost of treatment in the current consultation data is abnormal.

Further, in another embodiment of the method for judging abnormal medical expenses of the present application, the current medical data is transferred to the target classification model, and it is judged whether the current medical expenses in the current medical data are abnormal The steps include:

Step S31: Transmit the current medical treatment data to the target classification model, read the medical characteristic data in the current medical treatment data from the target classification model, and combine the medical characteristic data and the target classification result Compare each group in the group to determine the target group corresponding to the medical characteristic data and the average value of the target historical costs in the target group;

Furthermore, after receiving the current medical data requiring abnormality judgment, the server transmits the current medical data to the target classification model with the most accurate classification. The current consultation data includes various types of data such as the patient's age, gender, medical history, consultation time, treatment duration, disease information, medication type, and dosage; the target classification model reads the consultation characteristic data from the current consultation data, and the diagnosis characteristic data is characterized Personalized data related to the treatment plan during the patient's treatment, such as the patient's personal information such as the patient's age, gender, and medical history, as well as disease information, medication type, and medication amount. Because the target classification model divides the group according to the patient information and the consultation information in each historical consultation data, one group category corresponds to one type of patient information and consultation information; and the consultation feature data represents the patient information in the current consultation data And the visit information, so that you can compare the visit feature data and the target classification results to determine the target group category corresponding to the visit feature data. The target group class has the patient information, visit information, and visit feature data The characterized patient information and the consultation information are consistent, and the average historical cost of the target group can be used to judge the abnormality of the current consultation cost in the current consultation characteristic data. Therefore, after the target group type is determined, the historical cost average value corresponding to the target group type is determined, and the average historical cost value is used as the target historical cost average value to judge the abnormality of the current medical cost.

Step S32: Read the cost identifier generated by the target classification model, and determine whether the current cost of the current consultation in the current medical consultation data is abnormal according to the cost identifier, where the cost identifier is determined by the target classification model The average value of the historical cost of the target and the current cost of the visit are generated by comparison.

Further, after the target classification model determines the target group class corresponding to the visit feature data in the current visit data and the target average historical cost of the target; the target classification model calls the target historical average cost and uses the target historical average cost Compare with the current cost of the current visit in the current visit data to determine whether the current cost of the visit is consistent with the average value of the target historical cost for reference. Consistency is characterized by the floating range of the average value of the target historical cost. For example, if the floating range of the average value of the target historical cost is set to plus or minus 10; that is, when the judgment is based on the average value of the target historical cost, the current cost of treatment is at the target historical cost The range between the average minus 10 and the increase of 10 is normal; otherwise, it is abnormal to ensure the accuracy of the judgment of the current cost of treatment. The comparison between the average value of the target historical cost and the current cost of medical treatment will generate a judgment result of whether the current cost of the medical treatment is within the floating range of the average value of the target historical cost, and the method of adding a cost identifier to characterize the abnormality of the current cost of medical treatment; namely When the result of the judgment is that the current cost of medical treatment is not within the floating range of the average value of the target historical cost, it means that the current cost of medical treatment is abnormal, and a cost identifier characterizing the abnormality is added to the current cost of medical treatment. The server reads the cost identifier generated by the comparison of the target cost model and judges whether the cost identifier is an identifier representing abnormality, that is, whether the current cost of the current consultation in the current consultation data is abnormal according to the cost identifier. Specifically, the step of judging whether the current consultation fee in the current consultation data is abnormal according to the fee identifier includes:

Step S321, judging whether the cost identifier is an abnormal identifier, if the cost identifier is an abnormal identifier, it is determined that the current cost of medical treatment in the current medical data is abnormal;

There is an anomaly identifier characterizing the anomaly between the server and the target classification model. After reading the fee identifier, the server compares the fee identifier with the anomaly identifier to determine whether the fee identifier is an anomaly identifier; if the fee identifier If the symbol is an abnormal identifier, it means that the current consultation cost in the current consultation data is not within the floating range of the average value of the target historical cost, and the current consultation cost is abnormal.

Step S322, if the fee identifier is not an abnormal identifier, it is determined that the current fee for the current visit in the current visit data is normal.

When the cost identifier and the abnormal identifier are compared and it is determined that the cost identifier is not an abnormal identifier, it means that the current medical cost in the current medical data is within the floating range of the average value of the target historical cost, and the current medical cost is normal.

In addition, please refer to FIG. 2, the present application provides a device for judging abnormal medical expenses. In the first embodiment of the device for judging abnormal medical expenses of the present application, the device for judging abnormal medical expenses includes:

The generating module 10 is used to obtain multiple pieces of historical medical consultation data, and transmit each of the historical medical consultation data to a plurality of preset classification models for classification to generate each classification result, wherein the plurality of preset classification models are based on different The clustering algorithm is set, and each of the classification results is generated by a plurality of the preset classification models based on the patient information, the consultation information, and the historical consultation fee in each of the historical consultation data;

The determining module 20 is configured to detect each of the classification results, determine a target classification result, and determine the preset classification model that generates the target classification result as the target classification model;

The judging module 30 is configured to transmit the current medical treatment data to the target classification model when receiving the current medical treatment data regularly sent by the medical institution, and determine whether the current medical treatment fee in the current medical treatment data is abnormal.

In the apparatus for judging abnormal medical expenses in this embodiment, the generating module 10 uses a large amount of historical medical data as sample data of each preset classification model, and transmits it to the preset classification model for classification to obtain the classification result classified by each preset classification model The determination module 20 then detects each classification result to determine the most accurate target classification result, and determines the preset classification model that generates the target classification result as the target classification model; then the determination module 30 will receive the current medical treatment received The data is transferred to the target classification model, and the abnormality of the current cost of treatment in the current consultation data is judged by the target classification result. The target classification result in the target classification model is the classification of the corresponding relationship between various disease information, treatment options and cost information; it is generated from a large amount of real and effective historical visit data, with high accuracy, making it The abnormality judgment is more accurate and effective, which improves the accuracy of abnormal medical expenses as the basis for determining malicious use of medical insurance.

Wherein, each virtual function module of the apparatus for determining abnormal medical expenses is stored in the memory 1005 of the apparatus for determining abnormal medical expenses shown in FIG. 3, and when the processor 1001 executes the program for determining the abnormal medical expenses, the embodiment shown in FIG. 2 is implemented The function of each module in

It should be noted that, those of ordinary skill in the art may understand that all or part of the steps to implement the above embodiments may be completed by hardware, or may be completed by a program instructing related hardware. The program may be stored in a computer-readable In the storage medium, the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk.

Referring to FIG. 3, FIG. 3 is a schematic structural diagram of a device in a hardware operating environment involved in a method according to an embodiment of the present application.

The device for judging abnormal medical expenses in the embodiment of the present application may be a PC (personal computer, personal computer ), or terminal devices such as smart phones, tablet computers, e-book readers, and portable computers.

Those skilled in the art can understand that the structure of the abnormal visit cost judgment device shown in FIG. 3 does not constitute a limitation on the abnormal visit cost judgment device, and may include more or fewer parts than shown, or a combination of certain Components, or different component arrangements.

As shown in FIG. 3, the memory 1005, which is a computer storage medium, may include an operating system, a network communication module, and a judgment program for abnormal medical expenses. The operating system is a program that manages and controls equipment hardware and software resources for judging abnormal medical expenses, and supports the operation of the abnormal medical expenses judgment program and other software and/or programs. The network communication module is used to realize communication between the components inside the memory 1005, and to communicate with other hardware and software in the device for judging the cost of abnormal treatment.

In the apparatus for judging the abnormal visit fee shown in FIG. 3, the processor 1001 is used to execute a procedure for judging the abnormal visit fee stored in the memory 1005 to implement the steps in each embodiment of the above method for determining the abnormal visit fee.

The present application provides a computer storage medium. The computer storage medium is preferably a computer-readable computer storage medium. The computer-readable computer storage medium stores one or more programs. The one or more programs may also be One or more processors execute the steps in the embodiments of the method for judging the abnormal medical expenses.

Through the description of the above embodiments, those skilled in the art can clearly understand that the methods in the above embodiments can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware, but in many cases the former is better Implementation. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product in essence or part that contributes to the existing technology, and the computer software product is stored in a computer storage medium (such as ROM/ The RAM, magnetic disk, and optical disk include several instructions to enable a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the embodiments of the present application.

The above are only the preferred embodiments of the present application, and therefore do not limit the patent scope of the present application. Any equivalent structural transformations made by using the description and drawings of this application under the concept of this application, or directly/indirectly used in Other related technical fields are included in the patent protection scope of this application.

Claims

A method for judging the cost of abnormal visits, characterized in that the method for judging the cost of abnormal visits includes the following steps:

Obtain multiple pieces of historical medical data, and transmit each of the historical medical data to multiple preset classification models for classification, and generate each classification result, wherein the multiple preset classification models are set according to different clustering algorithms, And each of the classification results is generated by the plurality of preset classification models based on the patient information, the consultation information, and the historical consultation fee in each of the historical consultation data;

Detecting each of the classification results, determining a target classification result, and determining the preset classification model that generates the target classification result as the target classification model;

When receiving the current medical consultation data regularly sent by the medical institution, the current medical consultation data is transmitted to the target classification model, and it is judged whether the current medical consultation fee in the current medical consultation data is abnormal.
The method for judging abnormal medical expenses according to claim 1, wherein the step of transmitting each of the historical medical data to a plurality of preset classification models for classification and generating each classification result includes:

Transmitting each of the historical visit data to a plurality of preset classification models, and reading the patient information and visit information in each of the historical visit data by each of the preset classification models, so as to transfer the patient information and visits Each historical consultation data whose similarity of the consultation information is higher than a preset value is divided into the same group category, and the historical consultation fee of the historical consultation data in each of the group categories is read;

Reading each of the group categories divided by each of the preset classification models, and the historical medical expenses corresponding to each of the group categories to form each model group category set, and determining each model group category set as each of the The classification result of the preset classification model.
The method for judging abnormal medical expenses according to claim 2, wherein the step of detecting each of the classification results and determining the target classification result includes:

Comparing the inter-group spacing between each of the model group class sets to generate an inter-group comparison result for each of the model group class sets, and determining each of the groups according to the inter-group comparison result The first model group class set with the largest distance;

Comparing the intra-group spacing of each of the model group class sets to generate the intra-group comparison results of each of the model group class sets, and determining the minimum intra-group spacing according to the intra-group comparison results The second model group class collection;

Judging whether the first model group class set and the second model group class set are the same model group class set, if it is the same model group class set, the same model group class set Determined as the target classification result.
The method for judging abnormal medical expenses according to claim 3, wherein the step of judging whether the first model group class set and the second model group class set are the same model group class set Later includes:

If the first model group class set and the second model group class set are not the same model group class set, then find an inter-group result value greater than a first preset distance in each of the comparison results between the groups, And the result value in the group that is greater than the second preset distance in the comparison result in each of the groups;

When the inter-group result value and the intra-group result value are derived from the same set of model group classes, the same set of model group classes is determined as the target classification result.
The method for judging abnormal medical expenses according to claim 3, wherein after the step of detecting each of the classification results and determining the target classification result includes:

Generating an average historical cost of each group in the target classification result according to the historical medical expenses corresponding to each group in the target classification result;

Update the target classification result according to the average value of each historical cost, so as to determine whether the current medical cost in the current medical data is abnormal based on the updated target classification result in the target classification model.
The method for judging abnormal medical expenses according to claim 4, wherein after the step of detecting each of the classification results and determining the target classification result includes:

Generating an average historical cost of each group in the target classification result according to the historical medical expenses corresponding to each group in the target classification result;

Update the target classification result according to the average value of each historical cost, so as to determine whether the current medical cost in the current medical data is abnormal based on the updated target classification result in the target classification model.
The method for judging abnormal medical expenses according to claim 5, wherein the current medical data is transferred to the target classification model, and it is judged whether the current medical expenses in the current medical data are abnormal The steps include:

Transmitting the current visit data to the target classification model, reading the visit feature data in the current visit data by the target classification model, and comparing the visit feature data and each of the target classification results Group comparison, to determine the target group corresponding to the visit feature data, and the average historical cost of the target in the target group;

Reading the cost identifier generated by the target classification model, and judging whether the current cost of the current consultation in the current consultation data is abnormal according to the cost identifier, wherein the cost identifier is used by the target classification model to determine the target history The average cost is generated in comparison with the current medical cost.
The method for judging abnormal medical expenses according to claim 6, wherein the current medical data is transferred to the target classification model, and it is judged whether the current medical expenses in the current medical data are abnormal The steps include:

Transmitting the current visit data to the target classification model, reading the visit feature data in the current visit data by the target classification model, and comparing the visit feature data and each of the target classification results Group comparison, to determine the target group corresponding to the visit feature data, and the average historical cost of the target in the target group;

Reading the cost identifier generated by the target classification model, and judging whether the current cost of the current consultation in the current consultation data is abnormal according to the cost identifier, wherein the cost identifier is used by the target classification model to determine the target history The average cost is generated in comparison with the current medical cost.
The method for judging the abnormal visit fee according to claim 7, wherein the step of determining whether the current visit fee in the current visit data is abnormal according to the fee identifier includes:

Judging whether the cost identifier is an abnormal identifier, if the cost identifier is an abnormal identifier, it is determined that the current cost of medical treatment in the current medical data is abnormal;

If the fee identifier is not an abnormal identifier, it is determined that the current fee for the current visit in the current visit data is normal.
A device for judging abnormal medical expenses, characterized in that the device for judging abnormal medical expenses includes:

The generating module is used to obtain multiple pieces of historical medical data, and transmit each of the historical medical data to a plurality of preset classification models for classification to generate each classification result, wherein the plurality of preset classification models are based on different A clustering algorithm is set, and each of the classification results is generated by the plurality of preset classification models based on patient information, consultation information, and historical consultation fees in each of the historical consultation data;

A determining module, configured to detect each of the classification results, determine a target classification result, and determine the preset classification model that generates the target classification result as a target classification model;

The judging module is used to transmit the current medical treatment data to the target classification model when receiving the current medical treatment data regularly sent by the medical institution, and determine whether the current medical treatment fee in the current medical treatment data is abnormal.
The apparatus for judging abnormal medical expenses according to claim 10, wherein the generating module further comprises:

A transmission unit, configured to transmit each historical consultation data to a plurality of preset classification models, and read patient information and consultation information in each of the historical consultation data from each of the preset classification models to transfer all Each historical visit data whose similarity of the patient information and the visit information is higher than a preset value is divided into the same group category, and the historical visit cost of the historical visit data in each of the group categories is read;

A reading unit, configured to read each of the group categories divided by each of the preset classification models, and the historical medical expenses corresponding to each of the group categories to form a collection of model group categories, and combine the model group categories The set is determined as the classification result of each of the preset classification models.
The apparatus for judging abnormal medical expenses according to claim 11, wherein the determining module further comprises:

The first comparison unit is used to compare the inter-group spacing between each group in each of the model group class sets to generate an inter-group comparison result for each of the model group class sets, and according to the inter-group comparison The result determines the first model group class set with the largest spacing between the groups;

The second comparison unit is used to compare the intra-group spacing of each of the model group class sets to generate the intra-group comparison result of each of the model group class sets, and determine according to the intra-group comparison result The second model group class set with the smallest spacing in each of the groups;

The determining unit is used to determine whether the first model group class set and the second model group class set are the same model group class set. If the model group class set is the same, the same The set of model groups is determined as the target classification result.
The apparatus for judging abnormal medical expenses according to claim 12, wherein the determination module further comprises:

A searching unit, configured to search for a comparison result between the groups that is greater than a first preset distance if the first model group class set and the second model group class set are not the same model group class set The result value between the groups, and the result value within the group that is greater than the second preset distance among the comparison results within each group;

The determining unit is further configured to determine the same set of model group classes as the target classification result when the result values between the groups and the result values within the groups are derived from the same set of model group classes.
The apparatus for judging abnormal medical expenses according to claim 12, wherein the determination module further comprises:

A generating unit, configured to generate an average historical cost of each group in the target classification result according to the historical medical expenses corresponding to each group in the target classification result;

The updating unit is configured to update the target classification result according to the average value of each historical cost, so as to determine whether the current medical cost in the current medical data is abnormal based on the updated target classification result in the target classification model .
The apparatus for judging abnormal medical expenses according to claim 13, wherein the determination module further comprises:

A generating unit, configured to generate an average historical cost of each group in the target classification result according to the historical medical expenses corresponding to each group in the target classification result;

The updating unit is configured to update the target classification result according to the average value of each historical cost, so as to determine whether the current medical cost in the current medical data is abnormal based on the updated target classification result in the target classification model .
The apparatus for judging abnormal medical expenses according to claim 14, wherein the judging module is further used to:

Transmitting the current visit data to the target classification model, reading the visit feature data in the current visit data by the target classification model, and comparing the visit feature data and each of the target classification results Group comparison, to determine the target group corresponding to the visit feature data, and the average historical cost of the target in the target group;

Reading the cost identifier generated by the target classification model, and judging whether the current cost of the current consultation in the current consultation data is abnormal according to the cost identifier, wherein the cost identifier is used by the target classification model to determine the target history The average cost is generated in comparison with the current medical cost.
The apparatus for judging abnormal medical expenses according to claim 15, wherein the judging module is further used to:

Transmitting the current visit data to the target classification model, reading the visit feature data in the current visit data by the target classification model, and comparing the visit feature data and each of the target classification results Group comparison, to determine the target group corresponding to the visit feature data, and the average historical cost of the target in the target group;

Reading the cost identifier generated by the target classification model, and judging whether the current cost of the current consultation in the current consultation data is abnormal according to the cost identifier, wherein the cost identifier is used by the target classification model to determine the target history The average cost is generated in comparison with the current medical cost.
The apparatus for judging abnormal medical expenses according to claim 16, wherein the judging module is further used to:

Judging whether the cost identifier is an abnormal identifier, if the cost identifier is an abnormal identifier, it is determined that the current cost of medical treatment in the current medical data is abnormal;

If the fee identifier is not an abnormal identifier, it is determined that the current fee for the current visit in the current visit data is normal.
A device for judging abnormal medical expenses, characterized in that the device for judging abnormal medical expenses includes: a memory, a processor, a communication bus, and a program for judging abnormal medical expenses stored on the memory;

The communication bus is used to implement connection communication between the processor and the memory;

The processor is used to execute the procedure for judging the abnormal medical expenses to realize the following steps:

Obtain multiple pieces of historical medical data, and transmit each of the historical medical data to multiple preset classification models for classification, and generate each classification result, wherein the multiple preset classification models are set according to different clustering algorithms, And each of the classification results is generated by the plurality of preset classification models based on the patient information, the consultation information, and the historical consultation fee in each of the historical consultation data;

Detecting each of the classification results, determining a target classification result, and determining the preset classification model that generates the target classification result as the target classification model;

When receiving the current medical consultation data regularly sent by the medical institution, the current medical consultation data is transmitted to the target classification model, and it is judged whether the current medical consultation fee in the current medical consultation data is abnormal.
A computer storage medium, characterized in that the computer storage medium stores a program for judging abnormal medical expenses, and when the program for judging abnormal medical expenses is executed by a processor, the following steps are realized:

Obtain multiple pieces of historical medical data, and transmit each of the historical medical data to multiple preset classification models for classification, and generate each classification result, wherein the multiple preset classification models are set according to different clustering algorithms, And each of the classification results is generated by the plurality of preset classification models based on the patient information, the consultation information, and the historical consultation fee in each of the historical consultation data;

Detecting each of the classification results, determining a target classification result, and determining the preset classification model that generates the target classification result as the target classification model;

When receiving the current medical consultation data regularly sent by the medical institution, the current medical consultation data is transmitted to the target classification model, and it is judged whether the current medical consultation fee in the current medical consultation data is abnormal. The