CN110993103A - Method for establishing disease risk prediction model and method for recommending disease insurance product - Google Patents
Method for establishing disease risk prediction model and method for recommending disease insurance product Download PDFInfo
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Abstract
The invention relates to a method for establishing a disease risk prediction model and a method for recommending a disease insurance product, wherein the establishing method comprises the steps of obtaining historical diagnosis and treatment data of medical insurance personnel in a preset area, carrying out classified sampling treatment on the historical diagnosis and treatment data to obtain a sample data set, carrying out pretreatment of eliminating invalid data on the sample data set, clustering the historical disease diagnosis and coding information of all samples in the pretreated sample data set according to disease attributes and focus positions to obtain a disease clustering characteristic label, screening the disease clustering characteristic label by adopting a preset characteristic selection algorithm to obtain a re-clustering disease characteristic label, establishing a disease risk prediction model corresponding to preset re-diseases according to the re-clustering characteristic label, gender, age and the diagnosis behavior information and combining an extreme gradient promotion algorithm, and a foundation is laid for the accuracy of recommending disease insurance products.
Description
Technical Field
The invention relates to the field of insurance, in particular to a method for establishing a disease risk prediction model and a method for recommending a disease insurance product.
Background
With the development and the demand of the society, the level of awareness of consumers to insurance also gradually increases. The demand of consumers for insurance also develops towards more refinement, and a simple product pricing mode according to two dimensions of age and gender is more mechanical.
Currently, in the insurance industry, risk models or rules for determining health risks of customers are often formed based on traditional experience of the insurance industry, and the situation that health conditions are concealed for reverse insurance application cannot be eliminated, so that insurance products recommended to customers based on the traditional models often have the defect of low accuracy.
Disclosure of Invention
In view of this, a method for establishing a disease risk prediction model and a method for recommending a disease insurance product are provided, which can perform classified sampling processing on historical diagnosis and treatment data of medical insurance insurers in a preset area, extract a cluster feature label of the serious disease corresponding to the preset serious disease, then establish a disease risk prediction model corresponding to the preset serious disease according to the cluster feature label of the serious disease, gender, age and the information of the hospitalization behavior and by combining an extreme gradient lifting algorithm, can accurately evaluate the risk of the disease insurance insured person, and further provide a method for recommending the disease insurance product according to the disease risk prediction model, thereby greatly improving the popularization accuracy of the insurance product.
A method for establishing a disease risk prediction model comprises the following steps:
acquiring historical diagnosis and treatment data of medical insurance personnel in a preset area;
performing classified sampling processing on historical diagnosis and treatment data according to gender, a preset age interval and a preset comparison proportion to obtain a sample data set, wherein the sample data set comprises preset positive sample data and preset negative sample data of the serious disease, the preset comparison proportion is a ratio of the number of the preset positive sample data to the number of the preset negative sample data, and each sample data comprises historical disease diagnosis coding information and diagnosis behavior information of each sample in a preset time range;
preprocessing the sample data set to remove invalid data, and clustering historical disease diagnosis coding information corresponding to all samples in the preprocessed sample data set according to corresponding disease attributes and focus positions to obtain corresponding disease clustering feature labels;
screening the disease clustering feature labels by adopting a preset feature selection algorithm to obtain a preset severe disease clustering feature label corresponding to severe diseases;
and establishing a disease risk prediction model corresponding to the preset severe disease by combining an extreme gradient lifting algorithm according to the clustering feature label of the severe disease, the gender, the age and the information of the treatment behavior.
In one embodiment, the step of performing classified sampling processing on the historical diagnosis and treatment data according to gender, age and a preset comparison ratio to obtain a sample data set comprises:
classifying the historical diagnosis and treatment data according to the rule that the gender and the preset age interval are the same to obtain an initial data set;
respectively screening a first preset number of positive sample data and a second preset number of negative sample data of preset serious diseases from the initial data set according to a preset comparison proportion, wherein the ratio of the first preset number to the second preset number is equal to the preset comparison proportion;
and obtaining a corresponding sample data set according to the positive sample data and the negative sample data.
In one embodiment, the establishing method further comprises:
re-screening the clustering feature labels of the heavy diseases by combining the related pre-ordered diseases corresponding to the preset heavy diseases;
and establishing a disease risk prediction model corresponding to the preset serious disease by combining an extreme gradient lifting algorithm according to the re-screened serious disease clustering feature label, the sex, the age and the information of the treatment behavior.
In one embodiment, the preset comparison ratio is set to
In addition, a recommendation method of the disease insurance product is provided, the disease risk prediction model is adopted, and the recommendation method comprises the following steps:
designing a corresponding questionnaire according to a disease risk prediction model of a preset region;
acquiring basic data of a disease insurance applicant according to the questionnaire;
predicting the disease insurance applicant according to the basic data and the disease risk prediction model to obtain a corresponding disease risk prediction result;
and recommending the corresponding disease insurance product for the disease insurance applicant according to the disease risk prediction result.
In addition, a design method of the disease insurance product is provided, the disease risk prediction model is adopted, and the design method comprises the following steps:
respectively predicting the disease risk of medical insurance personnel in a preset area according to the disease risk prediction model to obtain corresponding disease risk prediction probability;
and generating a corresponding disease insurance product rate table according to the disease risk prediction probability, the gender and the age, and designing a corresponding disease insurance product according to the disease insurance product rate table.
In one embodiment, the step of generating a corresponding disease insurance product rate table based on the disease risk prediction probability, gender and age comprises:
dividing medical insurance personnel in a preset area into a plurality of risk level groups according to the disease risk prediction probability;
and according to the disease occurrence probability distribution corresponding to each risk level crowd, carrying out interval division on each risk level crowd according to gender and age, and generating a corresponding disease insurance product rate table.
In addition, a device for establishing a disease risk prediction model is also provided, the device comprising:
the data acquisition unit is used for acquiring historical diagnosis and treatment data of medical insurance personnel in a preset area;
the data set generating unit is used for performing classified sampling processing on the historical diagnosis and treatment data according to gender, a preset age interval and a preset comparison proportion to obtain a sample data set, wherein the sample data set comprises preset positive sample data and negative sample data of the serious disease, the preset comparison proportion is a ratio of the number of the preset positive samples to the number of the preset negative samples, and each sample data comprises historical disease diagnosis coding information and diagnosis behavior information of each sample in a preset time range;
the cluster feature tag generation unit is used for preprocessing the sample data set by removing invalid data, and clustering the historical disease diagnosis coding information corresponding to all samples in the preprocessed sample data set according to the corresponding disease attribute and the focus part to obtain a corresponding disease cluster feature tag;
the cluster characteristic label generating unit is used for screening the disease cluster characteristic labels by adopting a preset characteristic selection algorithm to obtain cluster characteristic labels of the disease clusters corresponding to preset disease clusters;
and the prediction model generation unit is used for establishing a disease risk prediction model corresponding to the preset serious disease according to the clustering feature label of the serious disease, the gender, the age and the information of the treatment behavior and by combining an extreme gradient lifting algorithm.
In addition, an apparatus terminal is also provided, which includes a memory for storing a computer program and a processor for operating the computer program to make the apparatus terminal execute the above establishment method.
Furthermore, a readable storage medium is provided, which stores a computer program, which, when executed by a processor, performs the above-mentioned establishing method.
The method for establishing the disease risk prediction model comprises the steps of obtaining historical diagnosis and treatment data of medical insurance personnel in a preset area, carrying out classified sampling processing on the historical diagnosis and treatment data according to gender, a preset age interval and a preset comparison proportion to obtain a sample data set, wherein the sample data set comprises positive sample data and negative sample data of preset serious diseases, the preset comparison proportion is the ratio of the number of the positive samples and the number of the negative samples of the preset serious diseases, each sample data comprises historical disease diagnosis coding information and treatment behavior information of each sample in a preset time range, carrying out pretreatment of removing invalid data on the sample data set, clustering the historical disease diagnosis coding information corresponding to all samples in the pretreated sample data set according to corresponding disease attributes and focus parts to obtain corresponding disease clustering feature labels, the disease clustering feature labels are screened by adopting a preset feature selection algorithm to obtain the heavy disease clustering feature labels corresponding to preset heavy diseases, the disease risk of a disease insurance applicant can be accurately predicted according to the heavy disease clustering feature labels, the sex, the age and the information of the treatment behavior, and a disease risk prediction model corresponding to the preset heavy diseases is established by combining an extreme gradient promotion algorithm, so that a proper basis is provided for designing insurance products for insurance companies, a proper disease insurance product recommendation method can be established according to the disease risk prediction model when the disease insurance products are recommended in the following process, and the popularization accuracy and the fitness of the disease insurance products can be improved on the whole.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings required to be used in the embodiments will be briefly described below, and it should be understood that the following drawings only illustrate some embodiments of the present invention, and therefore should not be considered as limiting the scope of the present invention. Like components are numbered similarly in the various figures.
FIG. 1 is a schematic flow chart illustrating a method for building a disease risk prediction model according to an embodiment;
FIG. 2 is a graph of receiver operating characteristics of a disease risk prediction model provided in one embodiment;
FIG. 3 is a flowchart illustrating a method for obtaining a sample data set according to an embodiment;
FIG. 4 is a schematic flow chart illustrating a method for building a disease risk prediction model according to another embodiment;
FIG. 5 is a flow diagram illustrating a method for recommending disease insurance products in one embodiment;
FIG. 6 is a schematic flow chart diagram illustrating a method for designing a disease insurance product, according to one embodiment;
FIG. 7 is a flow diagram illustrating a method for generating a disease insurance product rate table according to one embodiment;
fig. 8 is a block diagram of an apparatus for building a disease risk prediction model according to an embodiment.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
Various embodiments of the present disclosure will be described more fully hereinafter. The present disclosure is capable of various embodiments and of modifications and variations therein. However, it should be understood that: there is no intention to limit the various embodiments of the disclosure to the specific embodiments disclosed herein, but rather, the disclosure is to cover all modifications, equivalents, and/or alternatives falling within the spirit and scope of the various embodiments of the disclosure.
Hereinafter, the terms "including", "having", and their derivatives, which may be used in various embodiments of the present invention, are only intended to indicate specific features, numbers, steps, operations, elements, components, or combinations of the foregoing, and should not be construed as first excluding the existence of, or adding to, one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing.
Furthermore, the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments of the present invention belong. The terms (such as those defined in commonly used dictionaries) should be interpreted as having a meaning that is consistent with their contextual meaning in the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein in various embodiments of the present invention.
As shown in fig. 1, a method for establishing a disease risk prediction model is provided, where the method includes:
and step S110, acquiring historical diagnosis and treatment data of medical insurance personnel in a preset area.
Because the health conditions of people in different areas have great difference due to the difference of environment, eating habits and medical levels in different areas, historical diagnosis and treatment data of people in a specific area need to be acquired when the data are processed, and the historical diagnosis and treatment data of medical insurance personnel in the preset area are generally used as the standard to be more accurate.
The historical diagnosis and treatment data generally comprises the sex, age and the information of the diagnosis behavior of the patient, wherein the information of the diagnosis behavior generally comprises the grade of the hospital, the frequency of the diagnosis, the number of hospitalizations, the time of the diagnosis and the information of the accumulated cost of the diagnosis.
For a positive patient, the age of the extracted positive sample is the age when the patient is diagnosed with a certain serious disease for the first time, the hospital grade, frequency, number of hospitalizations, time, cost and corresponding historical disease diagnosis code information for the patient are data which are advanced within two years before the confirmation day of the certain serious disease for the first time, for example, the patient is confirmed as a cancer for the first time in 12 and 5 days in 2018, belongs to the positive sample, and the corresponding diagnosis and treatment data are extracted as diagnosis related data within the time range from 2016 12 and 5 days in 12 and 4 days in 2018 in 12 and 4 days in 4.
For negative samples (patients who are never diagnosed as a serious disease in the database), the diagnosis and treatment data in two years before the current database is considered as a starting point, and the diagnosis and treatment data in two years before the starting point is considered as a starting point, for example, the diagnosis and treatment data in 2015 and 2016 are usually extracted from the database data currently used to 12 and 31 2018.
The historical diagnosis and treatment data is usually corresponding data (such as personal information including identification numbers and addresses) with sensitive information of patients removed.
Step S120, performing classified sampling processing on the historical diagnosis and treatment data according to the gender, the preset age interval and the preset comparison proportion to obtain a sample data set, wherein the sample data set comprises preset positive sample data and preset negative sample data of the serious disease, the preset comparison proportion is the ratio of the number of the preset positive samples to the number of the preset negative samples of the serious disease, and each sample data comprises historical disease diagnosis coding information and diagnosis behavior information of each sample in a preset time range.
According to the historical diagnosis and treatment data, classified sampling processing needs to be further performed on the historical diagnosis and treatment data according to the gender, the preset age interval and the preset comparison proportion, then sample data sets are further obtained, each sample data set comprises preset positive sample data and preset negative sample data of the serious disease, and the preset positive sample data and the preset negative sample data of the serious disease are set according to the comparison proportion.
Besides the historical disease diagnosis code information of each sample in the preset time range, each sample data generally also comprises gender, age and information of the treatment behavior, the information of the treatment behavior generally comprises information of the grade of a treatment hospital, the frequency of treatment, the number of hospitalizations, the treatment time and the cumulative cost of treatment, and the historical disease diagnosis code information is generally coded by international disease classification ICD 10.
The ICD10 code is a normalized representation of the diagnostic description of the patient for the physician, i.e., to avoid using different textual descriptions for the same disease.
The positive sample data corresponds to the time range of the historical diagnosis and treatment data extracted from the positive patient sample, and is also data within two years before the confirmation date of first diagnosis of a certain serious disease, and correspondingly, the preset time range is usually within two years before the confirmation date of first diagnosis of a certain serious disease.
The negative sample data corresponds to the time range of the historical diagnosis and treatment data extracted from the negative sample, and for the negative sample (the patient who is not diagnosed as a serious disease in the database), the preset time range is started from two years before the deadline year included in the current database, and is started from the starting point again within two years.
The preset age interval may be divided into 5 years, such as 0-4 years, 5-9 years, …, 80+ years.
And step S130, preprocessing the sample data set to remove invalid data, and clustering the historical disease diagnosis coding information corresponding to all samples in the preprocessed sample data set according to the corresponding disease attribute and the focus part to obtain the corresponding disease clustering feature label.
The historical disease diagnosis code information of certain sample data possibly existing in the sample data set is an empty set, preprocessing of direct elimination is needed to be carried out, the preprocessed sample data set is obtained, then, the historical disease diagnosis code information corresponding to each sample data is clustered according to the corresponding disease attribute and the corresponding lesion part, the corresponding disease cluster label characteristic is obtained, and therefore sparsity of the sample data is reduced.
And step S140, screening the disease clustering feature labels by adopting a preset feature selection algorithm to obtain the cluster feature labels of the serious diseases corresponding to the preset serious diseases.
The preset feature selection algorithm generally adopts any one of a mutual information algorithm, a P value algorithm and an information increasing algorithm, and the disease clustering feature labels can be screened through the preset feature selection algorithm to obtain the cluster feature labels of the serious diseases corresponding to the preset serious diseases.
And S150, establishing a disease risk prediction model corresponding to the preset serious disease by combining an extreme gradient lifting algorithm according to the clustering feature label of the serious disease, the gender, the age and the information of the treatment behavior.
After the cluster characteristic label of the severe disease is obtained, the sex, age and diagnosis behavior information of each sample of the corresponding preprocessed sample data set are further combined to be used as model characteristic factors to be generated to participate in model building, and a disease risk prediction model corresponding to the preset severe disease is built by combining an extreme gradient lifting algorithm.
In one embodiment, firstly, 70% proportion of data in the sample data set is randomly extracted as a training sample data set, the rest 30% of data is used as a test sample data set, an extreme gradient lifting algorithm is adopted for a training model, wherein in the extreme gradient lifting algorithm, a two-term logistic regression function is selected as an objective function, the lifting type is set as a gradient lifting tree, the learning rate value range is 0.001-0.3, the maximum iteration number value range is 50-3000, a grid search algorithm is selected to carry out circular traversal on the hyperparameter set in the extreme gradient lifting algorithm, the training sample data set is pre-trained, the training effect of the model is evaluated by adopting K-fold cross validation, the appropriate model parameters are obtained by training and screening, and the test sample data set is predicted according to the appropriate model parameters obtained by screening, and obtaining a disease risk prediction result corresponding to the test sample data set, and comparing the disease risk prediction result with actual serious disease positive sample data of the test sample data set, thereby continuously correcting the disease risk prediction result until training and establishing a corresponding disease risk prediction model.
In one embodiment, the learning rate in the extreme gradient boosting algorithm is set to any one of 0.001, 0.003, 0.01, 0.03, 0.1 and 0.3, and the maximum number of iterations is set to any one of 50, 100, 300, 500, 1000 and 3000.
In an embodiment, a Receiver operating characteristic Curve for predicting the sample data set by using the disease risk prediction model is shown in fig. 2, where a true positive rate on an ordinate in the Receiver operating characteristic Curve (ROC Curve for short) indicates a number of predicted positive samples/an actual number of positive samples, a false positive rate on an abscissa indicates a number of predicted positive negative samples/an actual number of negative samples, and an AUC (Area Under ROC Curve) corresponding to the ROC Curve in fig. 2 is equal to 0.86, obviously, the AUC is greater than 0.5, i.e., greater than an Area Under a dashed straight line in fig. 2, which indicates that the disease risk prediction model is effective.
The method for establishing the disease risk prediction model can accurately predict the risk of the disease insurance applicant, further provide a proper basis for the design of insurance products for insurance companies, and enable a proper disease insurance product recommendation method to be established according to the disease risk prediction model when disease insurance product recommendation is subsequently performed, so that the popularization accuracy and the fitness of the disease insurance products are improved on the whole.
In one embodiment, the model characteristic factors corresponding to the disease risk prediction model are as follows:
| model feature factor |
| Sex |
| Age (age) |
| Number of times of visit |
| Cumulative amount of consumption |
| Number of hospitalization |
| Affecting the symptoms and signs of the skin and subcutaneous tissues |
| Diseases of veins, lymphatic vessels and lymph nodes, not classifiable elsewhere |
| Aplastic and other anemias |
| Other blood and hematopoietic organ diseases |
| Abnormal findings in blood tests |
| Blood coagulation defects, purpura and other bleeding conditions |
| Structural node hoof tissue disease of the system |
| Certain diseases involving immune mechanisms |
TABLE 1
In one embodiment, as shown in fig. 3, step S120 includes:
and S122, classifying the historical diagnosis and treatment data respectively according to the rule that the gender and the preset age interval are the same to obtain an initial data set.
The historical diagnosis and treatment data are divided into two parts according to gender, and then each part is further divided according to a preset age interval, so that initial data sets corresponding to each part can be obtained.
And S124, respectively screening a first preset number of positive sample data and a second preset number of negative sample data of the preset serious diseases from the initial data set according to a preset comparison proportion, wherein the ratio of the first preset number to the second preset number is equal to the preset comparison proportion.
After the initial data set is obtained, a first preset number of positive sample data with preset serious diseases can be further screened out from the initial data set, a second preset number of negative sample data can be further screened out from the initial data set, and the ratio of the first preset number to the second preset number is equal to a preset comparison ratio.
In an embodiment, for a preset severe disease, first, positive data corresponding to all patients with the preset severe disease are obtained from historical diagnosis and treatment data, then, a first preset number of positive sample data are selected from the positive data, and then, a second preset number of negative sample data are selected from the historical diagnosis and treatment data according to gender, a preset age interval and a preset comparison proportion.
Wherein the preset contrast ratio setting range is usually
In one embodiment, the predetermined comparison ratio is a ratio of 1: 4.
And S126, obtaining a corresponding sample data set according to the positive sample data and the negative sample data.
In an embodiment, as shown in fig. 4, the establishing method further includes:
step S160, the relevant pre-sequences corresponding to the preset repeat diseases are combined, and the repeat clustering feature labels are screened again.
Wherein, each preset serious disease has symptoms corresponding to a certain related preamble disease before the diagnosis is confirmed, so the clustering feature label of the serious disease can be further screened again according to the related preamble disease corresponding to the preset serious disease.
And S170, establishing a disease risk prediction model corresponding to the preset severe disease by combining an extreme gradient lifting algorithm according to the re-screened severe disease clustering feature label, the sex, the age and the information of the diagnosis behavior.
After the re-screened cluster characteristic label of the severe disease is obtained, the sex, age and diagnosis behavior information of each sample of the corresponding preprocessed sample data set are further combined to be used as model characteristic factors to be generated to participate in model training, and a disease risk prediction model corresponding to the preset severe disease is established by combining an extreme gradient lifting algorithm.
Further, as shown in fig. 5, there is provided a method of recommending a disease insurance product, the method of recommending using the disease risk prediction model, the method of recommending including:
step S210, acquiring basic data of the disease insurance applicant.
The disease risk prediction model is adopted for a preset region, a specific questionnaire is further designed, and the disease risk prediction model is more accurately applied to make prediction subsequently.
For example, relevant questionnaires can be designed through the model characteristic factors in the above table 1, and then information of potential policyholders can be mined in a targeted manner.
Of course, in addition to the questionnaires described above, the underlying data of the disease insurance applicant may be obtained through other channels, such as interview recordings and the like.
In one embodiment, relevant questionnaires are designed according to the model characteristic factors in the table 1, so that basic data of the disease insurance applicant relevant to the correspondence of each model characteristic factor is obtained in a targeted manner.
The questionnaire includes, but is not limited to, an internet questionnaire, a WeChat questionnaire, a QQ questionnaire, and a paper questionnaire.
And S220, predicting the disease insurance applicant according to the basic data and the disease risk prediction model to obtain a corresponding disease risk prediction result.
After the basic data are obtained, the basic data are further extracted and input into a disease risk prediction model to predict a disease insurance applicant, and a corresponding disease risk prediction result is obtained.
And step S230, recommending a corresponding disease insurance product for the disease insurance applicant according to the disease risk prediction result.
Through the targeted acquisition of the corresponding basic data for acquiring the disease insurance applicant, the disease insurance applicant is predicted according to the basic data and the disease risk prediction model to obtain a corresponding disease risk prediction result, and finally, the corresponding disease insurance product is recommended to the disease insurance applicant according to the disease risk prediction result, so that the recommendation and popularization accuracy of the insurance product is greatly improved, and the marketing capacity of an insurance company can be improved.
Further, as shown in fig. 6, there is provided a method of designing a disease insurance product, the method of designing using the disease risk prediction model, the method of designing including:
and S310, respectively predicting the disease risks of medical insurance personnel in the preset area according to the disease risk prediction model to obtain corresponding disease risk prediction probabilities.
Due to the differences of environment, dietary habits, medical levels and the like in different regions, for example, in northern regions and southern regions, coastal regions and plain regions, people in respective regions have obvious differences in climate and dietary habits, for example, thyroid cancer of residents in coastal regions is higher than that in non-coastal regions, and intestinal cancer incidence rate in southern regions is higher than that in northern regions. According to the regional differentiation characteristics, the method is divided into a plurality of regions, and different disease risk prediction models are set up for people in different regions.
In one embodiment, the preset region is the beijing region, and all tests of lymphoma serious diseases can be performed on medical insurance personnel in the beijing region according to the disease risk prediction model to respectively obtain the corresponding disease risk prediction probabilities.
Step S320, generating a corresponding disease insurance product rate table according to the disease risk prediction probability, gender and age, and designing a corresponding disease insurance product according to the disease insurance product rate table.
Wherein, the disease risk prediction probability and the age belong to important factors which are positively correlated with the disease insurance product rate, the gender of the male and the female also have important influence, the corresponding disease insurance product rate table is generated according to the disease risk prediction probability, the gender and the age, and the corresponding disease insurance product is designed according to the disease insurance product rate table.
According to the design method of the disease insurance product, different disease risk prediction models can be adopted according to different regions, and then the disease insurance product which is suitable for the preset region is designed, so that the disease insurance product can be matched with the actual situation of the preset region, the risk of the disease insurance product is reduced, the accurate adaptability of the disease insurance product is greatly improved, and the market competitiveness of insurance companies is improved.
In one embodiment, as shown in fig. 7, step S320 includes:
and S322, dividing medical insurance personnel in a preset area into a plurality of risk level groups according to the disease risk prediction probability.
After the disease risk prediction probability is obtained, the disease risk prediction probability can be further divided into a plurality of levels, and then medical insurance personnel in a preset area are divided into a plurality of risk level groups.
Step S324, according to the disease occurrence probability distribution corresponding to each risk level crowd, dividing each risk level crowd into intervals according to gender and age, and generating a corresponding disease insurance product rate table.
The disease occurrence probability distribution refers to the probability distribution of the disease with preset serious diseases, which actually corresponds to each risk level crowd.
Therefore, the corresponding disease insurance product rate table can be designed and generated according to the disease occurrence probability distribution of each risk level crowd and the interval division of each risk level crowd according to gender and age.
Further, as shown in fig. 8, there is provided a device for creating a disease risk prediction model, the device including:
the data acquisition unit 410 is configured to acquire historical diagnosis and treatment data of medical insurance personnel in a preset area.
The data set generating unit 420 is configured to perform classified sampling processing on the historical diagnosis and treatment data according to gender, a preset age interval, and a preset comparison ratio to obtain a sample data set, where the sample data set includes positive sample data and negative sample data of a preset serious disease, the preset comparison ratio is a ratio between the number of the positive samples and the number of the negative samples of the preset serious disease, and each sample data includes historical disease diagnosis coding information and diagnosis behavior information of each sample within a preset time range.
The first feature tag generating unit 430 is configured to perform preprocessing for removing invalid data on the sample data set, and cluster historical disease diagnosis coding information corresponding to each sample in the preprocessed sample data set according to the corresponding disease attribute and the lesion site to obtain a corresponding disease cluster feature tag.
The second feature tag generating unit 440 is configured to filter the disease cluster feature tags by using a preset feature selection algorithm to obtain cluster feature tags of severe diseases corresponding to preset severe diseases.
The prediction model generating unit 450 is configured to establish a disease risk prediction model corresponding to a preset severe disease according to the severe disease clustering feature label, the gender, the age, and the information of the visit behavior, and by combining an extreme gradient lifting algorithm.
In addition, an apparatus terminal is also provided, which includes a memory for storing a computer program and a processor for operating the computer program to make the apparatus terminal execute the above establishment method.
Furthermore, a readable storage medium is provided, which stores a computer program, which, when executed by a processor, performs the above-mentioned establishing method.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative and, for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, each functional module or unit in each embodiment of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention or a part of the technical solution that contributes to the prior art in essence can be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a smart phone, a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention.
Claims (10)
1. A method for establishing a disease risk prediction model is characterized by comprising the following steps:
acquiring historical diagnosis and treatment data of medical insurance personnel in a preset area;
performing classified sampling processing on the historical diagnosis and treatment data according to gender, a preset age interval and a preset comparison proportion to obtain a sample data set, wherein the sample data set comprises preset positive sample data and preset negative sample data of serious diseases, the preset comparison proportion is a ratio of the number of the preset positive sample data to the number of the preset negative sample data, and each sample data comprises historical disease diagnosis coding information and diagnosis behavior information of each sample in a preset time range;
preprocessing the sample data set to remove invalid data, and clustering historical disease diagnosis coding information corresponding to all samples in the preprocessed sample data set according to corresponding disease attributes and focus positions to obtain corresponding disease clustering feature labels;
screening the disease clustering feature labels by adopting a preset feature selection algorithm to obtain a cluster feature label of the serious diseases corresponding to the preset serious diseases;
and establishing a disease risk prediction model corresponding to the preset serious disease by combining an extreme gradient lifting algorithm according to the clustering feature label of the serious disease, the gender, the age and the information of the visiting behavior.
2. The method for establishing the medical diagnosis and treatment data set according to claim 1, wherein the step of performing classified sampling processing on the historical diagnosis and treatment data according to gender, age and a preset comparison ratio to obtain a sample data set comprises the following steps:
classifying the historical diagnosis and treatment data according to the rule that the gender and the preset age interval are the same to obtain an initial data set;
respectively screening a first preset number of positive sample data and a second preset number of negative sample data of preset serious diseases from the initial data set according to a preset comparison proportion, wherein the ratio of the first preset number to the second preset number is equal to the preset comparison proportion;
and obtaining a corresponding sample data set according to the positive sample data and the negative sample data.
3. The method of claim 1, further comprising:
re-screening the clustering feature labels of the serious diseases by combining the related pre-existing diseases corresponding to the preset serious diseases;
and establishing a disease risk prediction model corresponding to the preset serious disease by combining an extreme gradient lifting algorithm according to the re-screened serious disease clustering feature label, the sex, the age and the information of the visit behavior.
4. The method of claim 1, wherein the predetermined comparison ratio is set to be
5. A method of recommending a disease insurance product, using the disease risk prediction model of any one of claims 1 to 4, the method comprising:
acquiring basic data of a disease insurance applicant;
predicting the disease insurance applicant according to the basic data and the disease risk prediction model to obtain a corresponding disease risk prediction result;
and recommending a corresponding disease insurance product for the disease insurance applicant according to the disease risk prediction result.
6. A method of designing a disease insurance product, using the disease risk prediction model of any one of claims 1 to 4, the method comprising:
respectively predicting the disease risk of the medical insurance personnel in the preset area according to the disease risk prediction model to obtain corresponding disease risk prediction probability;
and generating a corresponding disease insurance product rate table according to the disease risk prediction probability, the gender and the age, and designing a corresponding disease insurance product according to the disease insurance product rate table.
7. The method of claim 6, wherein said step of generating a corresponding disease insurance product rate table based on said disease risk prediction probability, gender and age comprises:
dividing medical insurance personnel in the preset area into a plurality of risk level groups according to the disease risk prediction probability;
and according to the disease occurrence probability distribution corresponding to each risk level crowd, carrying out interval division on each risk level crowd according to gender and age, and generating a corresponding disease insurance product rate table.
8. An apparatus for building a disease risk prediction model, the apparatus comprising:
the data acquisition unit is used for acquiring historical diagnosis and treatment data of medical insurance personnel in a preset area;
the data set generating unit is used for performing classified sampling processing on the historical diagnosis and treatment data according to gender, a preset age interval and a preset comparison proportion to obtain a sample data set, wherein the sample data set comprises positive sample data and negative sample data of preset serious diseases, the preset comparison proportion is a ratio of the number of the positive samples and the number of the negative samples of the preset serious diseases, and each sample data comprises historical disease diagnosis coding information and diagnosis behavior information of each sample in a preset time range;
the cluster feature tag generation unit is used for carrying out pretreatment of eliminating invalid data on the sample data set and clustering historical disease diagnosis coding information corresponding to all samples in the pretreated sample data set according to corresponding disease attributes and focus parts to obtain corresponding disease cluster feature tags;
the serious disease clustering feature tag generating unit is used for screening the disease clustering feature tags by adopting a preset feature selection algorithm to obtain the serious disease clustering feature tags corresponding to the preset serious diseases;
and the prediction model generation unit is used for establishing a disease risk prediction model corresponding to the preset serious disease by combining an extreme gradient lifting algorithm according to the clustering feature label of the serious disease, the gender, the age and the information of the treatment behavior.
9. A device terminal, characterized in that it comprises a memory for storing a computer program and a processor for running the computer program to make the device terminal execute the set-up method of any one of claims 1 to 4.
10. A readable storage medium, characterized in that it stores a computer program which, when executed by a processor, performs the set-up method of any one of claims 1 to 4.
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