CN112017064A - Insurance application suggestion evaluation method and device suitable for assisted reproductive insurance - Google Patents
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Abstract
The invention provides an insurance application suggestion evaluation method and device suitable for assisted reproductive insurance, which comprises the following steps: receiving index data input by an applicant for reproductive insurance underwriting, wherein the index data comprises any one or more of physical examination result information and physiological characteristic information; and processing the index data based on a preset prediction model to generate an insurance application suggestion result, wherein the prediction model is generated by pre-training. The technical scheme provided by the invention can accurately and quickly confirm the insurance application qualification of the client, provides risk assessment for assisted reproductive insurance underwriting, ensures that the pricing of insurance companies is more accurate, and provides improved risk classification for insurance decision support and risk management, thereby providing high-quality health management and guarantee service for insurance applicants.
Description
Technical Field
The invention relates to an artificial intelligence technology, in particular to an insurance application suggestion evaluation method and device suitable for assisted reproductive insurance.
Background
Existing assisted reproductive insurance determines whether to provide insurance for an individual applicant by cooperating with a specific assisted reproductive medical center and then evaluating the overall risk attributes of the assisted reproductive insurance by the reproductive medical center. Before a specific assisted reproduction medical center evaluates, an insurance company cannot accurately identify the risk attributes of individual insurance applicants in time, thereby failing to provide pricing advantages, and failing to cooperate with more medical institutions and provide support services for more assisted reproduction patients.
Disclosure of Invention
The embodiment of the invention provides an insurance application suggestion evaluation method and device suitable for assisted reproduction insurance, which can accurately and quickly confirm the insurance application qualification of a client, provide risk evaluation for assisted reproduction insurance underwriting, enable the pricing of insurance companies to be more accurate, and provide improved risk classification for insurance decision support and risk management, thereby providing high-quality health management and guarantee service for insurance applicants.
In a first aspect of the embodiments of the present invention, there is provided an insurance application suggestion evaluation method suitable for assisted reproductive insurance, including:
receiving index data input by an applicant for reproductive insurance underwriting, wherein the index data comprises any one or more of physical examination result information and physiological characteristic information;
and processing the index data based on a preset prediction model to generate an insurance application suggestion result, wherein the prediction model is generated by pre-training.
Optionally, in a possible implementation manner of the first aspect, the processing the index data based on a preset prediction model to generate an insurance application suggestion result, where the pre-training generation of the prediction model includes:
acquiring a preset number of samples in a database as a training sample set;
based on a preset algorithm, taking the number of pregnancies of each couple as a prediction target and the original variable or derivative variable after cleaning a training sample set as a training feature set to obtain an association function of the prediction target and the training feature set;
and optimizing the correlation function of the prediction target and the training feature set to obtain a final parameter set.
Optionally, in a possible implementation manner of the first aspect, the prediction model is generated based on a layered random sampling method and training in advance.
Optionally, in a possible implementation manner of the first aspect, the optimal hyper-parameter of the prediction model is determined based on K-fold cross validation grid search, and a hyper-parameter set with maximized average accuracy is obtained;
the predictive models are fitted separately to the entire training data set.
Optionally, in a possible implementation manner of the first aspect, the preset algorithm is any one or more of a logistic regression algorithm, a random forest algorithm, and a catboost algorithm.
In a second aspect of the embodiments of the present invention, there is provided an insurance application advice evaluation apparatus suitable for assisted reproductive insurance, including:
the system comprises an applicant data receiving module, a data processing module and a data processing module, wherein the applicant data receiving module is used for receiving index data input by an applicant for reproductive insurance underwriting, and the index data comprises any one or more of physical examination result information and physiological characteristic information;
and the insurance application suggestion result generation module is used for processing the index data based on a preset prediction model to generate insurance application suggestion results, wherein the prediction model is generated by pre-training.
Optionally, in a possible implementation manner of the second aspect, the processing the index data based on a preset prediction model to generate an insurance application suggestion result, where the pre-training generation of the prediction model includes:
acquiring a preset number of samples in a database as a training sample set;
based on a preset algorithm, taking the number of pregnancies of each couple as a prediction target and the original variable or derivative variable after cleaning a training sample set as a training feature set to obtain an association function of the prediction target and the training feature set;
and optimizing the correlation function of the prediction target and the training feature set to obtain a final parameter set.
Optionally, in a possible implementation manner of the second aspect, the prediction model is generated based on a layered random sampling method and training in advance.
Optionally, in a possible implementation manner of the second aspect, the optimal hyper-parameter of the prediction model is determined based on K-fold cross validation grid search, and a hyper-parameter set with maximized average accuracy is obtained;
the predictive models are fitted separately to the entire training data set.
A third aspect of the embodiments of the present invention provides a readable storage medium, in which a computer program is stored, and the computer program is used for implementing the method according to the first aspect of the present invention and various possible designs of the first aspect of the present invention when the computer program is executed by a processor.
The invention provides an insurance application suggestion evaluation method and device applicable to assisted reproductive insurance.
The invention can accurately and quickly confirm the insurance application qualification of the client, provides risk assessment for assisted reproductive insurance underwriting, ensures that the pricing of insurance companies is more accurate, and provides improved risk classification for insurance decision support and risk management, thereby providing high-quality health management and guarantee service for insurance applicants. The underwriting engine is screened based on personal risk attributes, the application underwriting process is efficient and quick, and the underwriting engine can be widely applied to various auxiliary reproductive mechanisms to carry out business, so that high-quality, continuous and stable guarantee financial services are provided for more patients. .
Drawings
FIG. 1 is a flow chart of a first embodiment of an application recommendation evaluation method suitable for assisted reproductive insurance;
FIG. 2 is a flow chart of a second embodiment of an application recommendation evaluation method suitable for assisted reproductive insurance;
FIG. 3 is a flow chart of risk assessment of assisted reproductive insurance; .
FIG. 4 is a block diagram of an application advice evaluation device adapted for assisted reproductive insurance;
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein.
It should be understood that, in various embodiments of the present invention, the sequence numbers of the processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the internal logic of the processes, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
It should be understood that in the present application, "comprising" and "having" and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that, in the present invention, "a plurality" means two or more. "and/or" is merely an association describing an associated object, meaning that three relationships may exist, for example, and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "comprises A, B and C" and "comprises A, B, C" means that all three of A, B, C comprise, "comprises A, B or C" means that one of A, B, C comprises, "comprises A, B and/or C" means that any 1 or any 2 or 3 of A, B, C comprises.
It should be understood that in the present invention, "B corresponding to a", "a corresponds to B", or "B corresponds to a" means that B is associated with a, and B can be determined from a. Determining B from a does not mean determining B from a alone, but may be determined from a and/or other information. And the matching of A and B means that the similarity of A and B is greater than or equal to a preset threshold value.
As used herein, "if" may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context.
The technical solution of the present invention will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
The invention provides an insurance application suggestion evaluation method suitable for assisted reproductive insurance, which is a flow chart shown in figure 1 and comprises the following steps:
and S10, receiving index data input by the applicant for the reproductive insurance underwriting, wherein the index data comprises any one or more of physical examination result information and physiological characteristic information. The physical examination result information and physiological characteristic information respectively comprise age, ovary reserve (AMH, AFC, FSH and the like), body mass index BMI, six hormones, semen analysis, duration of infertility, previous live birth, previous abortion and type of infertility and the like.
And S20, processing the index data based on a preset prediction model to generate an insurance application suggestion result, wherein the prediction model is generated by pre-training.
In one embodiment, as shown in fig. 2, the index data is processed based on a preset prediction model to generate an insurance recommendation result, where the pre-training generation of the prediction model includes:
s201, obtaining a preset number of samples in a database as a training sample set;
s202, based on a preset algorithm, taking the number of times that each couple performs assisted reproduction and is pregnant as a prediction target, and taking an original variable or a derivative variable after cleaning a training sample set as a training feature set to obtain an association function of the prediction target and the training feature set;
and S203, optimizing the correlation function of the prediction target and the training feature set to obtain a final parameter set.
In the above steps, after data processing, 70% of the data set was randomly selected as a training set to build a prediction model, and the remaining 30% was used for verification. The method adopts various supervised machine learning algorithms to construct the main components of a prediction model/a model needing to be supplemented so as to facilitate the subsequent logistic regression and the reference logistic regression of hierarchical random sampling, the random forest and the catboost. The three algorithms, each of which has merits, all have interpretable properties, and the calculation process can be repeated (the 'simulation') under the condition of completely understanding the algorithm, and each part of the model (the 'algorithm transparency') has an intuitive interpretation. The prediction model takes the fact that each couple is pregnant only after needing to do auxiliary reproductive cycles as a prediction target, cleaned original variables or derivative variables serve as training feature sets, the interaction relation among the training features is mined, and a final parameter set is obtained by optimizing an objective function.
Further, the prediction model is generated by pre-training based on a hierarchical random sampling method.
Further, determining the optimal hyper-parameter of the prediction model based on K-time cross validation grid search, and acquiring a hyper-parameter set with maximized average accuracy;
the predictive models are fitted separately to the entire training data set.
The training process of the prediction model adopts a layered random sampling method to ensure that the proportion of live and non-live cases in the training set and the verification set is the same as that of the original data set. And (3) selecting the optimal hyper-parameter of the algorithm by K-time cross validation grid search, finally obtaining the hyper-parameter set with the maximum average accuracy, and fitting the model with the whole training data set respectively. Finally, the present invention evaluates the Receiver Operating Characteristic (ROC) curve selection final model.
Since the present invention is used for risk assessment of assisted reproductive insurance prior to entry into a cycle, as shown in fig. 3, the present invention can only use clinical data obtained prior to the start of an IVF procedure.
The invention trains and develops an artificial intelligence algorithm based on a large amount of healthy physiological and clinical data, and verifies the algorithm by using reserved data except the training data. The risk attributes of assisted reproductive insurance applicants are evaluated in the case where the applicants provide some physical examination results and physiological characteristics. By identifying adverse events of insurance underwriting and accelerating the application and underwriting process, an effective solution is provided for insurance companies in the fields of insurance consultation, application flow, underwriting flow and risk management, so that high-quality and affordable auxiliary reproduction guarantee and health management services are provided for clients.
In the initial data of model training, anonymous data of over 100,000 couples recorded by the Human Fertilization and Embryology Administration (HFEA) responsible for governing fertility treatment in the uk were used. Anonymous data, after the patient identity information has been obscured, includes most of the enrollment data since 1991. Anonymous data without an identification code protects patient privacy by staging or hiding portions of the information. Scientific research using anonymous data can still answer important questions. Such as which factors (patient, type of treatment, technique used) may affect the outcome of the treatment, or those indicators that suggest that the patient need only implant one to two embryos for treatment. This information covers the period from 1/1991 to 31/march 2009. This data set contains about 90 variables. Not all variables have a significant predictive effect on live babies. Under the guidance of reproductive medical experts, the method provided by the invention cleans and corrects the data again, clearly defines the data, and standardizes the variables so as to reproduce the candidate predictive variables.
Meanwhile, the self-contradictory data points in the original data are removed, for example, the past pregnancy history is not existed, but the frozen embryos of the user are used at the later stage; calculating the body mass index BMI using the raw variables to generate derived variables such as height and weight and performing classification labeling on the same; classifying and scoring the causes of infertility and the medical experts in reproduction.
In the process of the initial training and verification, relevant variables of a plurality of men and women before entering the auxiliary reproductive cycle, such as age, ovarian reserve (AMH, AFC, FSH and the like), Body Mass Index (BMI), six hormones, semen analysis, duration of infertility, previous live birth, previous abortion, type of infertility and the like, which are required for the applicant to make a prediction for several IVF cycles when the applicant successfully makes a pregnancy are extracted by evaluating the importance of parameter characteristics on a final prediction result. And finally evaluating the risk attribute of the assisted reproductive insurance applicant by combining the prediction result and performing characteristic importance ranking through retrospective analysis to find out the most relevant characteristics of the applicant: extremely high risk, moderate risk, medium and low risk, and low risk. Class name/need to add a risk attribute
After determining the final prediction of the risk assessment of assisted reproductive insurance, a final fit is made across the training data to arrive at a final primary underwriting engine. And participate in the domestic assisted reproduction 'good pregnancy insurance' insurance business, and the data of a plurality of reproduction medical centers are used for verification and iteration.
In a second aspect of the embodiments of the present invention, there is provided an insurance application advice evaluation device suitable for assisted reproductive insurance, as shown in fig. 4, including:
the system comprises an applicant data receiving module, a data processing module and a data processing module, wherein the applicant data receiving module is used for receiving index data input by an applicant for reproductive insurance underwriting, and the index data comprises any one or more of physical examination result information and physiological characteristic information;
and the insurance application suggestion result generation module is used for processing the index data based on a preset prediction model to generate insurance application suggestion results, wherein the prediction model is generated by pre-training.
Further, the index data is processed based on a preset prediction model to generate an insurance application suggestion result, wherein the pre-training generation of the prediction model comprises:
acquiring a preset number of samples in a database as a training sample set;
based on a preset algorithm, taking the number of pregnancies of each couple as a prediction target and the original variable or derivative variable after cleaning a training sample set as a training feature set to obtain an association function of the prediction target and the training feature set;
and optimizing the correlation function of the prediction target and the training feature set to obtain a final parameter set.
Further, the prediction model is generated by pre-training based on a hierarchical random sampling method.
Further, determining the optimal hyper-parameter of the prediction model based on K-time cross validation grid search, and acquiring a hyper-parameter set with maximized average accuracy;
the predictive models are fitted separately to the entire training data set.
The readable storage medium may be a computer storage medium or a communication medium. Communication media includes any medium that facilitates transfer of a computer program from one place to another. Computer storage media may be any available media that can be accessed by a general purpose or special purpose computer. For example, a readable storage medium is coupled to the processor such that the processor can read information from, and write information to, the readable storage medium. Of course, the readable storage medium may also be an integral part of the processor. The processor and the readable storage medium may reside in an Application Specific Integrated Circuits (ASIC). Additionally, the ASIC may reside in user equipment. Of course, the processor and the readable storage medium may also reside as discrete components in a communication device. The readable storage medium may be a read-only memory (ROM), a random-access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
The present invention also provides a program product comprising execution instructions stored in a readable storage medium. The at least one processor of the device may read the execution instructions from the readable storage medium, and the execution of the execution instructions by the at least one processor causes the device to implement the methods provided by the various embodiments described above.
In the above embodiments of the terminal or the server, it should be understood that the Processor may be a Central Processing Unit (CPU), other general-purpose processors, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. An insurance application suggestion evaluation method suitable for assisted reproductive insurance, comprising:
receiving index data input by an applicant for reproductive insurance underwriting, wherein the index data comprises any one or more of physical examination result information and physiological characteristic information;
and processing the index data based on a preset prediction model to generate an insurance application suggestion result, wherein the prediction model is generated by pre-training.
2. The application recommendation evaluation method according to claim 1,
the index data are processed based on a preset prediction model to generate an insurance application suggestion result, wherein the pre-training generation of the prediction model comprises the following steps:
acquiring a preset number of samples in a database as a training sample set;
based on a preset algorithm, taking the number of pregnancies of each couple as a prediction target and the original variable or derivative variable after cleaning a training sample set as a training feature set to obtain an association function of the prediction target and the training feature set;
and optimizing the correlation function of the prediction target and the training feature set to obtain a final parameter set.
3. The application recommendation evaluation method according to claim 2,
the prediction model is generated by pre-training based on a layered random sampling method.
4. The application recommendation evaluation method according to claim 3,
determining the optimal hyper-parameter of the prediction model based on the grid search of K times of cross validation, and acquiring a hyper-parameter set with maximized average accuracy;
the predictive models are fitted separately to the entire training data set.
5. The application recommendation evaluation method according to claim 2,
the preset algorithm is any one or more of a logistic regression algorithm, a random forest algorithm and a catboost algorithm.
6. An insurance application advice evaluation device adapted for assisted reproductive insurance, comprising:
the system comprises an applicant data receiving module, a data processing module and a data processing module, wherein the applicant data receiving module is used for receiving index data input by an applicant for reproductive insurance underwriting, and the index data comprises any one or more of physical examination result information and physiological characteristic information;
and the insurance application suggestion result generation module is used for processing the index data based on a preset prediction model to generate insurance application suggestion results, wherein the prediction model is generated by pre-training.
7. The application advice evaluation apparatus according to claim 6,
the index data are processed based on a preset prediction model to generate an insurance application suggestion result, wherein the pre-training generation of the prediction model comprises the following steps:
acquiring a preset number of samples in a database as a training sample set;
based on a preset algorithm, taking the number of pregnancies of each couple as a prediction target and the original variable or derivative variable after cleaning a training sample set as a training feature set to obtain an association function of the prediction target and the training feature set;
and optimizing the correlation function of the prediction target and the training feature set to obtain a final parameter set.
8. The application advice evaluation apparatus according to claim 7,
the prediction model is generated by pre-training based on a layered random sampling method.
9. The application advice evaluation apparatus according to claim 8,
determining the optimal hyper-parameter of the prediction model based on the grid search of K times of cross validation, and acquiring a hyper-parameter set with maximized average accuracy;
the predictive models are fitted separately to the entire training data set.
10. A readable storage medium, in which a computer program is stored which, when being executed by a processor, is adapted to carry out the method of any one of claims 1 to 5.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107910068A (en) * | 2017-11-29 | 2018-04-13 | 平安健康保险股份有限公司 | Insure health risk Forecasting Methodology, device, equipment and the storage medium of user |
| CN108257026A (en) * | 2017-05-08 | 2018-07-06 | 平安科技（深圳）有限公司 | Handle the method and device of insurance application |
| CN109410071A (en) * | 2018-09-17 | 2019-03-01 | 平安科技（深圳）有限公司 | Core protects data processing method, device, computer equipment and storage medium |
| CN109523412A (en) * | 2018-11-14 | 2019-03-26 | 平安科技（深圳）有限公司 | Intelligent core protects method, apparatus, computer equipment and computer readable storage medium |
| CN109559243A (en) * | 2018-12-13 | 2019-04-02 | 泰康保险集团股份有限公司 | Adjuster method, apparatus, medium and electronic equipment |
| CN111222994A (en) * | 2018-11-23 | 2020-06-02 | 泰康保险集团股份有限公司 | Client risk assessment method, device, medium and electronic equipment |
| CN111260243A (en) * | 2020-02-10 | 2020-06-09 | 京东数字科技控股有限公司 | Risk assessment method, device, equipment and computer readable storage medium |
-
2020
- 2020-08-26 CN CN202010872951.3A patent/CN112017064A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108257026A (en) * | 2017-05-08 | 2018-07-06 | 平安科技（深圳）有限公司 | Handle the method and device of insurance application |
| CN107910068A (en) * | 2017-11-29 | 2018-04-13 | 平安健康保险股份有限公司 | Insure health risk Forecasting Methodology, device, equipment and the storage medium of user |
| CN109410071A (en) * | 2018-09-17 | 2019-03-01 | 平安科技（深圳）有限公司 | Core protects data processing method, device, computer equipment and storage medium |
| CN109523412A (en) * | 2018-11-14 | 2019-03-26 | 平安科技（深圳）有限公司 | Intelligent core protects method, apparatus, computer equipment and computer readable storage medium |
| CN111222994A (en) * | 2018-11-23 | 2020-06-02 | 泰康保险集团股份有限公司 | Client risk assessment method, device, medium and electronic equipment |
| CN109559243A (en) * | 2018-12-13 | 2019-04-02 | 泰康保险集团股份有限公司 | Adjuster method, apparatus, medium and electronic equipment |
| CN111260243A (en) * | 2020-02-10 | 2020-06-09 | 京东数字科技控股有限公司 | Risk assessment method, device, equipment and computer readable storage medium |
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