CN111179102B - Medical insurance verification wind control method, device and storage medium - Google Patents
Medical insurance verification wind control method, device and storage medium Download PDFInfo
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- CN111179102B CN111179102B CN201911360260.9A CN201911360260A CN111179102B CN 111179102 B CN111179102 B CN 111179102B CN 201911360260 A CN201911360260 A CN 201911360260A CN 111179102 B CN111179102 B CN 111179102B
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/08—Insurance
Abstract
In order to solve the problems of the prior art, a medical insurance verification and maintenance wind control method, a device and a storage medium are provided, the verification difficulty is reduced, a more accurate basis is provided for premium measurement and calculation, the disclosure relates to the field of big data, and the method comprises the following steps: acquiring medical data of a person to be insured; determining a disease group of diseases of the personnel to be insured according to the medical data and risk factor index information of the personnel to be insured; and determining and outputting the important disease risk value of the person to be insured according to the logistic regression model of the disease group of the person to be insured and the risk factor index information of the person to be insured. According to the technical scheme, the important disease risk value of the person to be insured can be determined according to the medical data of the person to be insured, so that the difficulty of nuclear insurance is reduced, and a more accurate basis is provided for premium measurement and calculation.
Description
Technical Field
The disclosure relates to the technical field of big data, in particular to a medical insurance nuclear security wind control method, a device and a storage medium.
Background
Commercial insurance is rapidly evolving and more people have purchased medical commercial insurance. However, in order to save cost, the commercial insurance company issues health examination and calls the past treatment information, usually, insurance sales personnel perform simple inquiry and record, and the information registered by the sales personnel of the insurance maintenance company performs the insurance. Information acquisition is very limited and does not preclude sales personnel from deliberately obscuring and concealing some information for performance. The method and the system increase the verification difficulty of the commercial insurance company, can not effectively identify the potential risk of the client, are easy to select reversely or cheat in claims, and bring great loss to the commercial insurance company.
Disclosure of Invention
In order to solve at least one of the above technical problems, the present disclosure provides a method, an apparatus and a storage medium for controlling a medical insurance policy, which reduce the policy difficulty.
In a first aspect of the present disclosure, a medical insurance nuclear security wind control method includes:
acquiring medical data of a person to be insured;
determining a disease group of diseases of the personnel to be insured according to the medical data and risk factor index information of the personnel to be insured;
determining and outputting a major disease risk value of the person to be insured according to a logistic regression model of a disease group of the person to be insured and risk factor index information of the person to be insured; the independent variable of the logistic regression model is a risk factor index, and the dependent variable of the logistic regression model is a serious disease occurrence risk value.
Optionally, before the acquiring the medical data of the person to be insured, the method further includes:
constructing a logistic regression model;
obtaining a case sample of a disease group, the case sample comprising risk factor index information;
and training a logistic regression model according to the case samples of the disease group to obtain the logistic regression model of the disease group.
Optionally, determining, according to the medical data, a disease group of a disease suffered by the person to be insured and risk factor index information of the person to be insured, including:
acquiring diseases of the personnel to be ensured and risk factor index information of the personnel to be ensured from the medical data, and determining a disease group of the diseases of the personnel to be ensured according to the diseases of the personnel to be ensured and the mapping relation between the preset diseases and the disease group.
Optionally, the method further comprises:
acquiring a dimension value of a case group;
determining the disease severity value of the case group according to the dimension value of the case group and the preset dimension value weight;
and determining and outputting the disease severity value of the person to be ensured according to the disease group of the person to be ensured.
In a second aspect of the present disclosure, a medical commercial insurance nuclear insurance wind control device includes:
the acquisition module is used for acquiring medical data of the personnel to be insured;
the information determining module is used for determining a disease group of the disease of the person to be ensured and risk factor index information of the person to be ensured according to the medical data;
the risk analysis module is used for determining and outputting a major disease risk value of the person to be ensured according to the logistic regression model of the disease group of the person to be ensured and the risk factor index information of the person to be ensured; the independent variable of the logistic regression model is a risk factor index, and the dependent variable of the logistic regression model is a serious disease occurrence risk value.
Optionally, the apparatus further includes:
the logistic regression model building module is used for building a logistic regression model; obtaining a case sample of a disease group, the case sample comprising risk factor index information; and training a logistic regression model according to the case samples of the disease group to obtain the logistic regression model of the disease group.
Optionally, determining, according to the medical data, a disease group of a disease suffered by the person to be insured and risk factor index information of the person to be insured, including:
acquiring diseases of the personnel to be ensured and risk factor index information of the personnel to be ensured from the medical data, and determining a disease group of the diseases of the personnel to be ensured according to the diseases of the personnel to be ensured and the mapping relation between the preset diseases and the disease group.
Optionally, the information determining module is further configured to determine a disease severity value of the case group according to a dimension value of the case group and a preset dimension value weight;
the risk analysis module is further used for determining and outputting a disease severity value of the person to be ensured according to the disease group of the person to be ensured.
In a third aspect of the present disclosure, a computer-readable storage medium stores a computer program which, when executed by a processor, implements the steps of the method of any of the first aspects of the present disclosure.
In a fourth aspect of the disclosure, a computing device includes a memory storing a computer program and a processor implementing the steps of the method of any of the first aspects of the disclosure when the computer program is executed.
According to the technical scheme, according to medical data of the personnel to be ensured, acquired from a medical database, a disease group of the disease of the personnel to be ensured and risk factor index information of the personnel to be ensured are determined; and determining and outputting the important disease risk value of the person to be insured according to the logistic regression model of the disease group of the person to be insured and the risk factor index information of the person to be insured. Therefore, an insurance company can determine the insurance risk of the person to be insured according to the major disease risk value of the person to be insured, determine whether the person to be insured can be insured, reduce the difficulty of nuclear insurance, and provide more accurate basis for premium measurement.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the disclosure and together with the description serve to explain the principles of the disclosure.
FIG. 1 is a flow chart of a medical insurance policy method in one embodiment of the present disclosure;
FIG. 2 is another flow chart of a medical insurance policy method in one embodiment of the present disclosure;
fig. 3 is a device diagram of a medical commercial insurance policy device in one embodiment of the present disclosure.
Detailed Description
The present disclosure is described in further detail below with reference to the drawings and the embodiments. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant content and not limiting of the present disclosure. It should be further noted that, for convenience of description, only a portion relevant to the present disclosure is shown in the drawings.
In addition, embodiments of the present disclosure and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Example 1:
referring to fig. 1, a medical insurance nuclear insurance wind control method includes:
step S1: acquiring medical data of a person to be insured;
step S2: determining a disease group of a disease of a person to be insured according to the medical data and risk factor index information of the person to be insured;
step S3: determining and outputting a major disease risk value of the person to be insured according to a logistic regression model of a disease group of the person to be insured and risk factor index information of the person to be insured; the independent variable of the logistic regression model is a risk factor index, and the dependent variable of the logistic regression model is a serious disease occurrence risk value.
The medical database of regional big data companies has a large amount of medical data, but cannot directly output any information of individuals. The regional medical data may be analyzed by authorization, and the analysis results may be utilized. Step S1 may obtain medical data of the person to be insured from a medical database.
According to the method, a disease group of diseases of the personnel to be ensured and risk factor index information of the personnel to be ensured are determined according to medical data of the personnel to be ensured, which are acquired from a medical database; and determining and outputting the important disease risk value of the person to be insured according to the logistic regression model of the disease group of the person to be insured and the risk factor index information of the person to be insured. Therefore, an insurance company can determine the insurance risk of the person to be insured according to the major disease risk value of the person to be insured, determine whether the person to be insured can be insured, reduce the difficulty of nuclear insurance, and provide more accurate basis for premium measurement.
The medical data comprise diseases of the personnel to be insured and risk factor index information of the personnel to be insured;
the risk factor index information includes at least two, preferably all, of region, gender, age, height, weight, body mass index value, wedding history, past history, family history, eating habits, and life information.
The above groups of diseases may be grouped into groups of diseases according to internal disease criteria; for example, a plurality of ICD10 versions in use in the medical industry can be referred to, after fusion and duplication removal, disease standards belonging to the interior of the ICD10 versions are established, and the first 5 bits or the first 3 bits are divided into a group by using functions of excel, sorting and other methods by utilizing the coding characteristics of the disease, and then the group is checked and adjusted empirically according to the risk probability of the disease.
In one embodiment, referring to fig. 2, before acquiring the medical data of the person to be insured, further comprising:
step S01: constructing a logistic regression model;
step S02: obtaining a case sample of a disease group, wherein the case sample comprises risk factor index information;
step S03: and training a logistic regression model according to the case samples of the disease group to obtain the logistic regression model of the disease group.
And training a logistic regression model according to the case samples of the disease group to obtain a logistic regression model of the disease group.
In one embodiment, determining a disease group of a disease of a person to be insured and risk factor indicator information of the person to be insured from medical data includes:
acquiring diseases of the personnel to be ensured and risk factor index information of the personnel to be ensured from the medical data, and determining a disease group of the diseases of the personnel to be ensured according to the diseases of the personnel to be ensured and the mapping relation between the preset diseases and the disease group.
And determining the disease group of the disease through the preset mapping relation between the disease and the disease group. For example, the disease name "hypertension grade 1" in the medical data is mapped to the disease group "mild blood pressure", and the disease name "hypertension grade 3" in the medical data is mapped to the disease group "severe hypertension mild".
In one embodiment, the method further comprises:
acquiring a dimension value of a case group;
determining the disease severity value of the case group according to the dimension value of the case group and the preset dimension value weight;
and determining and outputting the disease severity value of the person to be ensured according to the disease group of the person to be ensured.
The dimension values include at least two of morbidity, mortality, recurrence rate, recurrence interval, rate of occurrence of significant complications, mortality, mild disability, severe disability, and average age of death, and may be selected throughout.
According to the method, the disease severity value of the person to be insured is determined and output according to the disease group of the person to be insured, so that an insurance company can determine insuring risks and whether insuring is provided according to the disease severity value.
Example 2:
referring to fig. 3, a medical commercial insurance nuclear insurance wind control device includes:
the acquisition module 1 is used for acquiring medical data of a person to be insured;
the information determining module 2 is used for determining a disease group of the disease of the person to be insured and risk factor index information of the person to be insured according to the medical data;
the risk analysis module 3 is used for determining and outputting a major disease risk value of the person to be ensured according to the logistic regression model of the disease group of the person to be ensured and the risk factor index information of the person to be ensured; the independent variable of the logistic regression model is a risk factor index, and the dependent variable of the logistic regression model is a serious disease occurrence risk value.
According to the device, according to medical data of the personnel to be ensured, acquired from a medical database, a disease group of the disease of the personnel to be ensured and risk factor index information of the personnel to be ensured are determined; and determining and outputting the important disease risk value of the person to be insured according to the logistic regression model of the disease group of the person to be insured and the risk factor index information of the person to be insured. The insurance company can determine the insurance risk of the person to be insured according to the major disease risk value of the person to be insured and determine whether the person to be insured can be insured, so that the difficulty of nuclear insurance is reduced.
In one embodiment, referring to fig. 3, the apparatus further comprises:
the logistic regression model construction module 4 is used for constructing a logistic regression model; obtaining a case sample of a disease group, wherein the case sample comprises risk factor index information; and training a logistic regression model according to the case samples of the disease group to obtain the logistic regression model of the disease group.
In one embodiment, determining a disease group of a disease of a person to be insured and risk factor indicator information of the person to be insured from medical data includes:
acquiring diseases of the personnel to be ensured and risk factor index information of the personnel to be ensured from the medical data, and determining a disease group of the diseases of the personnel to be ensured according to the diseases of the personnel to be ensured and the mapping relation between the preset diseases and the disease group.
In one embodiment, the information determining module is further configured to determine a disease severity value of the case group according to the dimension value of the case group and a preset dimension value weight;
and the risk analysis module is also used for determining and outputting the disease severity value of the personnel to be ensured according to the disease group of the personnel to be ensured.
The principle and effect of the apparatus of this embodiment are the same as those of the method of embodiment 1, and this embodiment will not be described in detail.
Example 3:
a computer readable storage medium storing a computer program which when executed by a processor performs the steps of the method of any one of embodiment 1.
The principle and effect of the computer readable storage medium of the present embodiment are the same as those of the method of embodiment 1, and the present embodiment will not be described in detail.
Example 4:
a computing device comprising a memory storing a computer program and a processor implementing the steps of any of the methods of embodiment 1 when the processor executes the computer program.
The principle and effect of the computing device of this embodiment are the same as those of the method of embodiment 1, and this embodiment will not be described in detail.
Example 5:
the first step: grouping internal disease standards, establishing statistical dimension and risk index
Referring to a plurality of ICD10 versions in use in the medical industry, after fusion and duplication removal, establishing disease standards belonging to the inside of the ICD10 versions, dividing the first 5 bits or the first 3 bits into a group by using functions of excel, sequencing and other methods by utilizing the coding characteristics of the diseases, and checking and adjusting the groups according to the risk probability of the diseases manually and empirically.
While professionals establish which disease groups belong to major diseases.
Establishing dimension requiring statistics such as morbidity, recurrence rate, recurrence interval, occurrence of major complication rate, mortality, mild disability rate, severe disability rate, average death age, etc. And weight the attention of each dimension from the business insurance.
Risk factor indicators such as region, gender, age, height, weight, BMI value, wedding history, past history, family history, eating habits, and life-style information are established. Each risk factor indicator is given a weight.
And a second step of: disease mapping of business medical data
According to the name, the diseases in the business medical data are mapped to internal disease standards by utilizing an NLP technology, and the matching degree in the NLP matching result is not 1 and is subjected to manual verification.
And a third step of: data nano-array, constructing risk analysis model of disease
In the database, according to the main diagnosis of the business data, the computer automatically classifies the medical records into various disease groups, each group is respectively counted in various dimensions, and the Logistic regression model analyzes the disease groups to be related to the risk factors. For example, the number of new cases of the group of patients with hypertension, the total number of patients with hypertension in the year, the number of serious complications such as cerebral hemorrhage, cerebral infarction and the like. Analysis found that hypertension was associated with past history and eating habits.
Fourth step: inquiry result output of applicant
After the identity card number and the usual address information of the applicant are input, the computer finds all medical information in the business data, and matches the medical information into a corresponding disease risk model according to the information, and outputs the major disease occurrence risk coefficient, disability risk coefficient, recurrence risk coefficient and death risk coefficient of the applicant. If the patient has multiple diseases, the individual values for the multiple diseases are output, and the highest score is displayed to the insurer.
Taking the example of hypertension grade 3 of the personnel to be ensured, the specific diagnosis of hypertension grade 3 of doctors is classified as severe hypertension.
Medical data from hundreds of hospitals in the database extract all cases that are mainly diagnosed as "hypertension grade 3".
The incidence, prevalence, recurrence rate, recurrence interval, rate of major complications occurring, mortality, mild disability, severe disability, average age of death, etc. in this group of cases were analyzed. The score of "severe hypertension" is calculated from the weights of the dimensions, e.g., 75 points. The higher the score, the greater the extent of the disease.
The indexes such as the region, sex, age, wedding history, past history, family history, diet preference, life information and the like in all cases are analyzed, and the risk factors related to the statistical indexes such as the death rate, the average death age, the incidence rate of serious diseases, the disability rate, the recurrence rate and the like are found. Major disease occurrence and sex in severe hypertensive groups such as men, age >65 years, BMI >30, fatiness and sweet taste in eating habits, straight relatives in family history are related to hypertension, and different weights are given according to the degree of affinity of the correlation, with men weight 10%, age >65 years weight 15%, BMI >30 weight 30%, fatiness weight 10% and sweet taste 15% in eating habits, straight relatives in family history are also related to hypertension 20%.
And inputting the identity card number of the applicant, and finding all medical data of the applicant. If the patient is diagnosed with "hypertension grade 3", the patient is classified as "severe hypertension" group, so the disease risk score of the applicant is 75 points; the insurance application was for men (10 points), 50 years (0 points), BMI28 (0 points), "sweet food" in personal history of medical history (15 points), and history of father hypertension (20 points) in family history. The applicant's major disease occurrence risk score is 45 points.
If the patient is still diagnosed with "brain hemorrhage sequelae" and is already a significant disease, then the output significant disease risk score is directly 100 points.
In the description of the present specification, reference to the terms "one embodiment/manner," "some embodiments/manner," "example," "a particular example," "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment/manner or example is included in at least one embodiment/manner or example of the application. In this specification, the schematic representations of the above terms are not necessarily for the same embodiment/manner or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments/modes or examples. Furthermore, the various embodiments/modes or examples described in this specification and the features of the various embodiments/modes or examples can be combined and combined by persons skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
It will be appreciated by those skilled in the art that the above-described embodiments are merely for clarity of illustration of the disclosure, and are not intended to limit the scope of the disclosure. Other variations or modifications will be apparent to persons skilled in the art from the foregoing disclosure, and such variations or modifications are intended to be within the scope of the present disclosure.
Claims (6)
1. The medical insurance nuclear insurance wind control method is characterized by comprising the following steps:
acquiring medical data of a person to be insured;
determining a disease group of a disease of a person to be insured and risk factor index information of the person to be insured according to the medical data, wherein the disease group comprises the disease group of the person to be insured and the risk factor index information of the person to be insured comprises:
acquiring diseases of the personnel to be ensured and risk factor index information of the personnel to be ensured from the medical data, and determining a disease group of the diseases of the personnel to be ensured according to the diseases of the personnel to be ensured and the mapping relation between the preset diseases and the disease group;
determining and outputting a major disease risk value of the person to be insured according to a logistic regression model of a disease group of the person to be insured and risk factor index information of the person to be insured; the independent variable of the logistic regression model is a risk factor index, and the dependent variable of the logistic regression model is a serious disease occurrence risk value;
acquiring a dimension value of a case group;
determining the disease severity value of the case group according to the dimension value of the case group and the preset dimension value weight;
and determining and outputting the disease severity value of the person to be ensured according to the disease group of the person to be ensured.
2. The method of claim 1, wherein prior to obtaining medical data of the person to be insured, further comprising:
constructing a logistic regression model; obtaining a case sample of a disease group, the case sample comprising risk factor index information;
and training a logistic regression model according to the case samples of the disease group to obtain the logistic regression model of the disease group.
3. The utility model provides a medical commerce insurance nuclear insurance wind control device which characterized in that includes:
the acquisition module is used for acquiring medical data of the personnel to be insured;
the information determining module is used for determining a disease group of the disease of the person to be ensured and risk factor index information of the person to be ensured according to the medical data;
the risk analysis module is used for determining and outputting a major disease risk value of the person to be ensured according to the logistic regression model of the disease group of the person to be ensured and the risk factor index information of the person to be ensured; the independent variable of the logistic regression model is a risk factor index, and the dependent variable of the logistic regression model is a serious disease occurrence risk value;
the information determining module is also used for determining the disease severity value of the case group according to the dimension value of the case group and the preset dimension value weight;
the risk analysis module is further used for determining and outputting a disease severity value of the person to be ensured according to the disease group of the person to be ensured.
4. The apparatus of claim 3, wherein the apparatus further comprises:
the logistic regression model building module is used for building a logistic regression model; obtaining a case sample of a disease group, the case sample comprising risk factor index information; and training a logistic regression model according to the case samples of the disease group to obtain the logistic regression model of the disease group.
5. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, implements the steps of the method of any one of claims 1 to 2.
6. Computing device comprising a memory and a processor, characterized in that the memory stores a computer program, the processor implementing the steps of the method of any of claims 1 to 2 when the computer program is executed.
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| CN112561714B (en) * | 2020-12-16 | 2024-03-08 | 中国平安人寿保险股份有限公司 | Nuclear protection risk prediction method and device based on NLP technology and related equipment |
| CN116561183B (en) * | 2023-07-10 | 2023-09-19 | 北京环球医疗救援有限责任公司 | Intelligent information retrieval system for massive medical insurance data |
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