CN111179102A - Medical insurance underwriting and protecting wind control method and device and storage medium - Google Patents
Medical insurance underwriting and protecting wind control method and device and storage medium Download PDFInfo
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Abstract
In order to solve the problems in the prior art, the invention provides a medical insurance underwriting wind control method, a medical insurance underwriting wind control device and a storage medium, which reduce underwriting difficulty and provide more accurate basis for premium measurement and calculation, and 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 the disease suffered by the personnel to be insured and risk factor index information of the personnel to be insured according to the medical data; and determining and outputting the major disease risk value of the personnel to be insured according to the logistic regression model of the disease group of the disease suffered by the personnel to be insured and the risk factor index information of the personnel to be insured. According to the technical scheme, the major 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 underwriting 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 underwriting wind control method, a medical insurance underwriting wind control device and a storage medium.
Background
Commercial insurance has developed at a rapid pace, and more people have purchased medical commercial insurance. However, in the current business insurance company, in order to save cost, not everyone issues health physical examination and calls previous visit information, but usually insurance sales personnel carry out simple inquiry and record, and the information registered by the sales personnel of the insurance officer carries out the insurance. The information access is very limited and does not preclude some information from being intentionally obscured and hidden by the salesperson for performance. These all make the business insurance company guarantee difficult increase, can't effectively discern customer's potential risk, the condition such as easy occurrence counter selection or cheat the claim brings huge loss for the business insurance company.
Disclosure of Invention
In order to solve at least one of the above technical problems, the present disclosure provides a medical insurance underwriting wind control method, device and storage medium, which reduce underwriting difficulty.
In a first aspect of the disclosure, a medical insurance underwriting wind control method includes:
acquiring medical data of a person to be insured;
determining a disease group of the disease suffered by the personnel to be insured and risk factor index information of the personnel to be insured according to the medical data;
determining and outputting a major disease risk value of the personnel to be insured according to the logistic regression model of the disease group of the disease suffered by the personnel to be insured and the risk factor index information of the personnel 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 major disease occurrence risk value.
Optionally, before 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 including risk factor indicator information;
and training the logistic regression model according to the case samples of the disease group to obtain the logistic regression model of the disease group.
Optionally, determining a disease group of a disease suffered by the person to be insured and risk factor index information of the person to be insured according to the medical data includes:
acquiring the disease of the person to be protected and the risk factor index information of the person to be protected from the medical data, and determining the disease group of the disease of the person to be protected according to the disease of the person to be protected and a preset mapping relation between the disease and the disease group.
Optionally, the method further includes:
acquiring a dimension value of a case group;
determining a disease severity value of the case group according to the dimension value of the case group and a preset dimension value weight;
and determining and outputting the disease severity value of the person to be insured according to the disease group of the disease suffered by the person to be insured.
In a second aspect of the present disclosure, a medical business insurance underwriting wind control device includes:
the acquisition module is used for acquiring medical data of a person to be insured;
the information determining module is used for determining a disease group of the disease suffered by the personnel to be insured and risk factor index information of the personnel to be insured according to the medical data;
the risk analysis module is used for determining and outputting a major disease risk value of the personnel to be insured according to the logistic regression model of the disease group of the disease of the personnel to be insured and the risk factor index information of the personnel 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 major disease occurrence risk value.
Optionally, 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 including risk factor indicator information; and training the logistic regression model according to the case samples of the disease group to obtain the logistic regression model of the disease group.
Optionally, determining a disease group of a disease suffered by the person to be insured and risk factor index information of the person to be insured according to the medical data includes:
acquiring the disease of the person to be protected and the risk factor index information of the person to be protected from the medical data, and determining the disease group of the disease of the person to be protected according to the disease of the person to be protected and a preset mapping relation between the disease and the disease group.
Optionally, 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;
the risk analysis module is also used for determining and outputting a disease severity value of the personnel to be insured according to the disease group of the disease of the personnel to be insured.
In a third aspect of the disclosure, a computer readable storage medium, having stored thereon a computer program which, when executed by a processor, carries out the steps of the method of any of the first aspects of the disclosure.
In a fourth aspect of the disclosure, a computing device comprises 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 processor executes the computer program.
According to the technical scheme, a disease group of the disease suffered by the person to be insured and risk factor index information of the person to be insured are determined according to medical data of the person to be insured, which are acquired from a medical database; and determining and outputting the major disease risk value of the personnel to be insured according to the logistic regression model of the disease group of the disease suffered by the personnel to be insured and the risk factor index information of the personnel to be insured. The insurance company can determine the insurance risk of the personnel to be insured and determine whether the insurance can be insured according to the serious disease risk value of the personnel to be insured, thereby reducing the difficulty of underwriting and providing more accurate basis for premium measurement and calculation.
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 underwriting wind control method in an embodiment of the present disclosure;
FIG. 2 is another flow chart of a medical insurance underwriting wind control method in an embodiment of the present disclosure;
fig. 3 is an apparatus diagram of a medical business insurance underwriting wind control apparatus in an embodiment of the present disclosure.
Detailed Description
The present disclosure will be described in further detail with reference to the drawings and embodiments. It is to be understood that the specific embodiments described herein are for purposes of illustration only and are not to be construed as limitations of the present disclosure. It should be further noted that, for the convenience of description, only the portions relevant to the present disclosure are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present disclosure 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 underwriting wind control method includes:
step S1: acquiring medical data of a person to be insured;
step S2: determining a disease group of the disease suffered by the personnel to be insured and risk factor index information of the personnel to be insured according to the medical data;
step S3: determining and outputting a major disease risk value of the personnel to be insured according to the logistic regression model of the disease group of the disease suffered by the personnel to be insured and the risk factor index information of the personnel 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 the regional big data company holds a large amount of medical data, but cannot directly output any information of an individual. Regional medical data can be analyzed by authorization and the results of the analysis can be utilized. Step S1 may obtain medical data of the person to be insured from the medical database.
According to the method, a disease group of the disease suffered by the personnel to be insured and risk factor index information of the personnel to be insured are determined according to medical data of the personnel to be insured, which are acquired from a medical database; and determining and outputting the major disease risk value of the personnel to be insured according to the logistic regression model of the disease group of the disease suffered by the personnel to be insured and the risk factor index information of the personnel to be insured. The insurance company can determine the insurance risk of the personnel to be insured and determine whether the insurance can be insured according to the serious disease risk value of the personnel to be insured, thereby reducing the difficulty of underwriting and providing more accurate basis for premium measurement and calculation.
The medical data comprises 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 of, preferably all of, region, sex, age, height, weight, body mass index value, marriage and childbirth history, past history, family history, dietary preference, and daily work and rest.
The disease groups can be grouped according to internal disease criteria; for example, a plurality of ICD10 versions in use in the medical industry can be referred to, after de-duplication is fused, disease standards belonging to the internal part of the ICD10 version are established, coding characteristics of diseases are utilized, methods such as excel functions and sorting are utilized to divide the first 5 bits or the first 3 bits into a group, and the group is verified and adjusted manually according to the risk probability of the diseases in experience.
In one embodiment, referring to fig. 2, before acquiring the medical data of the person to be insured, the method further comprises:
step S01: constructing a logistic regression model;
step S02: acquiring a case sample of a disease group, wherein the case sample comprises risk factor index information;
step S03: and training the logistic regression model according to the case samples of the disease group to obtain the logistic regression model of the disease group.
And training the logistic regression model according to the case sample of the disease group to obtain the logistic regression model of the disease group.
In one embodiment, determining a disease group of a disease suffered by a person to be insured and risk factor index information of the person to be insured according to medical data comprises:
acquiring the disease of the person to be protected and the risk factor index information of the person to be protected from the medical data, and determining the disease group of the disease of the person to be protected according to the disease of the person to be protected and a preset mapping relation between the disease and the disease group.
And determining the disease group of the disease through a preset mapping relation between the disease and the disease group. For example, the disease name "hypertension level 1" in the medical data is mapped to the disease group "mild blood pressure", and the disease name "hypertension level 3" in the medical data is mapped to the disease group "severe mild hypertension".
In one embodiment, the method further comprises:
acquiring a dimension value of a case group;
determining a disease severity value of the case group according to the dimension value of the case group and a preset dimension value weight;
and determining and outputting the disease severity value of the person to be insured according to the disease group of the disease suffered by the person to be insured.
The dimension values comprise at least two of morbidity, recurrence rate, recurrence interval, occurrence rate of major complications, mortality, mild disability rate, severe disability rate and average death age, and can be selected.
The method disclosed by the invention determines and outputs the disease severity value of the person to be insured according to the disease group of the disease suffered by the person to be insured, so that an insurance company can determine the insurance risk and provide insurance according to the disease severity value.
Example 2:
referring to fig. 3, the medical business insurance underwriting wind control device comprises:
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 suffered by the personnel to be insured and risk factor index information of the personnel 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 personnel to be insured according to the logistic regression model of the disease group of the disease of the personnel to be insured and the risk factor index information of the personnel 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 device disclosed by the invention determines the disease group of the disease suffered by the personnel to be insured and the risk factor index information of the personnel to be insured according to the medical data of the personnel to be insured, which is acquired from the medical database; and determining and outputting the major disease risk value of the personnel to be insured according to the logistic regression model of the disease group of the disease suffered by the personnel to be insured and the risk factor index information of the personnel to be insured. So that the insurance company can determine the insurance risk of the personnel to be insured and determine whether the insurance can be insured according to the serious disease risk value of the personnel to be insured, thereby reducing the difficulty of underwriting.
In one embodiment, referring to fig. 3, the apparatus further comprises:
the logistic regression model building module 4 is used for building a logistic regression model; acquiring a case sample of a disease group, wherein the case sample comprises risk factor index information; and training the 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 suffered by a person to be insured and risk factor index information of the person to be insured according to medical data comprises:
acquiring the disease of the person to be protected and the risk factor index information of the person to be protected from the medical data, and determining the disease group of the disease of the person to be protected according to the disease of the person to be protected and a preset mapping relation between the disease 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 a disease severity value of the personnel to be insured according to the disease group of the disease of the personnel to be insured.
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, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any of embodiment 1.
The principle and effect of the computer-readable storage medium of this embodiment are the same as those of the method of embodiment 1, and this embodiment will not be described in detail.
Example 4:
a computing device comprising a memory and a processor, the memory storing a computer program that when executed by the processor implements the steps of any of the methods of embodiment 1.
The principle and effect of the computing device of the present embodiment are the same as those of the method in embodiment 1, and the present embodiment will not be described in detail.
Example 5:
the first step is as follows: grouping internal disease standards, establishing statistical dimensions and risk indexes
Referring to a plurality of ICD10 versions used in the medical industry, after fusion and de-duplication, establishing a disease standard belonging to the internal part of the user, dividing the first 5 bits or the first 3 bits into a group by using the coding characteristics of the disease and methods such as excel function, sorting and the like, and manually checking and adjusting the group according to the risk probability of the disease from experience.
While the practitioner establishes which disease groups belong to major diseases.
Establishing the dimension needing statistics, such as morbidity, recurrence rate, recurrence interval, occurrence rate of major complications, mortality, mild disability rate, severe disability rate, average death age and the like. And weights the attention level of each dimension from the commercial insurance.
Establishing risk factor indexes such as region, sex, age, height, weight, BMI value, marriage and childbirth history, past history, family history, diet hobby and life work and rest. Each risk factor indicator is given a weight.
The second step is that: disease mapping of business medical data
And mapping the diseases in the business medical data to an internal disease standard by using an NLP technology according to the names, and manually verifying that the matching degree in the NLP matching result is not 1.
The third step: data nanoranking, 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, and each group respectively counts various dimensions and analyzes the disease groups to be related to risk factors by a Logistic regression model. For example, the number of new cases of hypertension in a group of patients per year, the total number of hypertension patients in the group per year, the number of serious complications such as cerebral hemorrhage and cerebral infarction, and the like. The analysis finds that the hypertension is related to the past history and the food preference.
The fourth step: applicant's query result output
After the applicant identity card number and the permanent address information are input, the computer finds all medical information of the applicant in the service data, matches the medical information with the corresponding disease risk model according to the medical information, and outputs a major disease occurrence risk coefficient, a disability risk coefficient, a recurrence risk coefficient and a death risk coefficient of the applicant. If the patient has multiple diseases, the values for the multiple diseases are output and the insurance company is presented with the highest score.
Taking the patient to be protected with hypertension of grade 3 as an example, the specific diagnosis of the doctor, namely the hypertension grade 3, is classified into the group of severe hypertension.
Medical data from hundreds of hospitals in the database draw all cases primarily diagnosed as "grade 3 hypertension".
The group was analyzed for morbidity, recurrence rate, inter-recurrence interval, incidence of major complications, mortality, mild disability rate, severe disability rate, average age of death, etc. The score for "severe hypertension" is calculated based on the weights of the dimensions, e.g., 75. The higher the score, the more severe the disease.
Analyzing the indexes of region, sex, age, marriage and childbearing history, past history, family history, diet hobby, life work and rest and the like in all cases, and finding out the risk factors related to the above statistical indexes such as mortality, average death age, incidence rate of serious diseases, disability rate, recurrence rate and the like. Such as the incidence of major disease in the severe hypertensive group and gender of men, age >65 years, BMI >30, satiety and sweetish in dietary cravings, and immediate relatives in family history are related to hypertension and are given different weights depending on the closeness of the association, male weight 10%, age >65 years weight 15%, BMI >30 weight 30%, fatty weight 10% and sweetish in dietary cravings 15%, and immediate relatives in family history are equally hypertensive 20%.
And inputting the applicant identification number and finding all medical data of the applicant. If the patient is diagnosed with hypertension grade 3, the patient is classified as severe hypertension, so the disease risk score of the applicant is 75 points; the insurant is male (10 points), 50 years old (0 points), BMI28(0 points), the 'love sweets' in personal history in medical history (15 points), and the father hypertension history in family history (20 points). Then the applicant has a serious disease occurrence risk score of 45.
If the patient is already a critical disease by itself, but also has a diagnosis of "sequelae after cerebral hemorrhage", the output risk score for a critical disease is directly 100.
In the description herein, reference to the description of the terms "one embodiment/mode," "some embodiments/modes," "example," "specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment/mode or example is included in at least one embodiment/mode or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to be the same embodiment/mode 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/aspects or examples and features of the various embodiments/aspects or examples described in this specification can be combined and combined by one skilled in the art without conflicting therewith.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
It will be understood by those skilled in the art that the foregoing 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 may occur to those skilled in the art, based on the foregoing disclosure, and are still within the scope of the present disclosure.
Claims (10)
1. The medical insurance underwriting wind protection control method is characterized by comprising the following steps:
acquiring medical data of a person to be insured;
determining a disease group of the disease suffered by the personnel to be insured and risk factor index information of the personnel to be insured according to the medical data;
determining and outputting a major disease risk value of the personnel to be insured according to the logistic regression model of the disease group of the disease suffered by the personnel to be insured and the risk factor index information of the personnel 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 major disease occurrence risk value.
2. The method of claim 1, wherein prior to obtaining the 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 including risk factor indicator information;
and training the logistic regression model according to the case samples of the disease group to obtain the logistic regression model of the disease group.
3. The method of claim 1, wherein determining a disease group of a disease suffered by a person to be insured and risk factor index information of the person to be insured based on the medical data comprises:
acquiring the disease of the person to be protected and the risk factor index information of the person to be protected from the medical data, and determining the disease group of the disease of the person to be protected according to the disease of the person to be protected and a preset mapping relation between the disease and the disease group.
4. The method of claim 1, wherein the method further comprises:
acquiring a dimension value of a case group;
determining a disease severity value of the case group according to the dimension value of the case group and a preset dimension value weight;
and determining and outputting the disease severity value of the person to be insured according to the disease group of the disease suffered by the person to be insured.
5. Medical business insurance underwriting wind-protection control device, its characterized in that includes:
the acquisition module is used for acquiring medical data of a person to be insured;
the information determining module is used for determining a disease group of the disease suffered by the personnel to be insured and risk factor index information of the personnel to be insured according to the medical data;
the risk analysis module is used for determining and outputting a major disease risk value of the personnel to be insured according to the logistic regression model of the disease group of the disease of the personnel to be insured and the risk factor index information of the personnel 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 major disease occurrence risk value.
6. The apparatus of claim 5, 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 including risk factor indicator information; and training the logistic regression model according to the case samples of the disease group to obtain the logistic regression model of the disease group.
7. The apparatus of claim 5, wherein determining a disease group of a disease suffered by a person to be insured and risk factor index information of the person to be insured based on the medical data comprises:
acquiring the disease of the person to be protected and the risk factor index information of the person to be protected from the medical data, and determining the disease group of the disease of the person to be protected according to the disease of the person to be protected and a preset mapping relation between the disease and the disease group.
8. The apparatus of claim 5,
the information determining module is further used for determining a disease severity value of the case group according to the dimension value of the case group and a preset dimension value weight;
the risk analysis module is also used for determining and outputting a disease severity value of the personnel to be insured according to the disease group of the disease of the personnel to be insured.
9. Computer-readable storage medium, characterized in that it stores a computer program which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.
10. Computing device comprising a memory and a processor, characterized in that the memory stores a computer program which, when executed by the processor, implements the steps of the method of any one of claims 1 to 5.
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| CN116561183A (en) * | 2023-07-10 | 2023-08-08 | 北京环球医疗救援有限责任公司 | Intelligent information retrieval system for massive medical insurance data |
| 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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