WO2017157029A1 - 基于医学大数据的保险精算方法和系统 - Google Patents

基于医学大数据的保险精算方法和系统 Download PDF

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WO2017157029A1
WO2017157029A1 PCT/CN2016/105113 CN2016105113W WO2017157029A1 WO 2017157029 A1 WO2017157029 A1 WO 2017157029A1 CN 2016105113 W CN2016105113 W CN 2016105113W WO 2017157029 A1 WO2017157029 A1 WO 2017157029A1
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information
insurance
medical
actuarial
probability
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张贯京
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Shenzhen Qianhai AnyCheck Information Technology Co Ltd
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Shenzhen Qianhai AnyCheck Information Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • 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
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"

Definitions

  • the present invention relates to the field of data processing technologies, and in particular, to a method and system for insurance actuarial based on medical big data.
  • insurance companies introduce a variety of insurance products, and insurance sales personnel are directed to different people according to the type and characteristics of insurance products.
  • the insured person is required to carry out the necessary medical examination before finalizing the premium and the insured amount.
  • the premium is relatively high and the insurance amount will be adjusted.
  • Premiums and insured amounts are adjusted based on the actual physical condition of the insured and the expected risk.
  • the existing insurance actuarial methods and systems refer to the medical examination reports of the insured personnel, they do not combine the vital signs data and medical information during the period of the insured personnel to predict the probability of disease risk of the insured personnel, and expand the insurance products. The risk of output.
  • the main object of the present invention is to provide an insurance actuarial method and system based on medical big data, which aims to solve the problem that the existing insurance product creation system and method does not have the vital signs and medical information of the insured person during a certain period of time. Combined to predict the probability of disease risk of the insured, the risk of insurance product output is high.
  • the present invention provides an insurance actuarial method based on medical big data.
  • the medical actuarial method based on medical big data includes the following steps:
  • S10 receiving the vital sign information sent by the physical sign information collecting system as the first reference data, and receiving the medical information sent by the medical information collecting system as the second reference data, and calculating according to the first reference data and the second reference data. The probability of disease risk for the insured person;
  • S20 creating the insurance product by using the disease risk probability as one of the insurance factors
  • step S10 output insurance information according to the insurance product and generate an insurance policy.
  • step S10 includes:
  • S11 transmitting a request instruction including a preset time range and user information to the vital information collection system and the medical information collection system;
  • S12 receiving, by the physical sign information collecting system, the physical information of the user information in the preset time range and the medical information sent by the medical information collecting system in the preset time range.
  • S13 extracting the vital sign information and the abnormality information in the medical information, where the abnormality information includes first probability information in the vital sign information that exceeds the preset physical sign information and/or the medical information in the medical information Second probability information for a disease of class;
  • S14 Calculate the disease risk probability of the insured person according to the preset weight distribution ratio according to the first probability information and/or the second probability information.
  • the insurance factor includes a non-actuarial factor and an actuarial factor.
  • the disease risk probability has a weight of at least 20% of the weight of the actuarial factor.
  • the insurance information includes an insured object, a contracting scope, a premium, a guarantee amount, and insurance attribute information.
  • the present invention also provides an insurance actuarial system based on medical big data.
  • the medical big data-based insurance actuarial system includes a disease risk calculation module, an insurance actuarial module, and an insurance product output module, wherein:
  • the disease risk probability calculation module is configured to receive the vital sign information sent by the vital sign information collecting system as the first reference data, and receive the medical information sent by the medical information collecting system as the second reference data, and according to the first reference data. Calculating a disease risk probability of the insured person with the second reference data;
  • the actuarial module is used to create the insurance product by using the disease risk probability as one of the insurance factors
  • the insurance product output module is configured to output insurance information according to the insurance product created by the insurance actuarial module and generate an insurance policy.
  • the disease risk probability calculation module comprises a request sub-module, a receiving sub-module, an information extraction sub-module and a probability calculation sub-module, wherein: [0024] the request sub-module is configured to send a request instruction including a preset time range and user information to the physical information collection system and the medical information collection system;
  • the receiving sub-module is configured to receive the vital sign information of the user information sent by the vital sign information collecting system in a preset time range, and receive the user information sent by the medical information collecting system at a preset Medical information within the scope of the day;
  • the information extraction sub-module is configured to extract the abnormality information in the vital sign information and the medical information, where the abnormality information includes first probability information and/or a location of the physical sign information that exceeds preset physical information.
  • the second probability information of a certain type of disease in the medical information is configured to extract the abnormality information in the vital sign information and the medical information, where the abnormality information includes first probability information and/or a location of the physical sign information that exceeds preset physical information.
  • the probability calculation sub-module calculates the disease risk probability of the insured person according to the preset weight distribution ratio according to the first probability information and/or the second probability information.
  • the insurance factor includes a non-actuarial factor and an actuarial factor.
  • the disease risk probability has a weight of at least 20% of the weight of the actuarial factor.
  • the insurance information includes an insured object, a contracting scope, a premium, a guarantee amount, and insurance attribute information.
  • the medical macro data-based insurance actuarial method and system provided by the insured person uses the vital information and medical information of the insured as reference data to calculate the disease risk probability of the insured person, and the disease risk Probability as one of the insurance factors involved in insurance actuarial creation of insurance products, reducing the risk of export of insurance products.
  • FIG. 1 is a schematic structural view of a preferred embodiment of an operating environment of an insurance actuarial system based on medical big data according to the present invention
  • FIG. 2 is a schematic diagram of functional modules of a preferred embodiment of the disease risk probability calculation module of the present invention.
  • FIG. 3 is a schematic flow chart of an insurance actuarial method based on medical big data according to the present invention.
  • step S10 is a schematic flow chart of the refinement of step S10 in FIG. 3.
  • the present invention provides an insurance actuarial method and system based on medical big data, which collects vital signs data of an insured person's preset time and obtains an insured person from a medical health system.
  • the data of the diagnosis and treatment in the sputum are used to jointly predict the probability of the risk of a certain type of disease, so that the probability of the disease risk is used as an actuarial factor to calculate the output index of the insurance product and reduce the output risk of the insurance product.
  • FIG. 1 is a schematic structural diagram of a preferred embodiment of an operating environment of an insurance actuarial system based on medical big data according to the present invention.
  • the medical big data-based actuarial system 30 runs in the second server 3, and the second server 3 is connected to the vital sign collecting device 1 and the first server 2.
  • the vital sign information collecting device 1 includes a vital sign information collecting system 10, and the first server 2 includes a medical information collecting system 20.
  • the vital sign information collecting device 1 includes, but is not limited to, one or more of a blood pressure collecting device, a blood glucose collecting device, a blood lipid collecting device, a blood oxygen collecting device, and a weight collecting device.
  • the vital sign information collecting system 10 in the vital sign information collecting device 1 is configured to collect the vital sign information of the insured person in the preset time, and use the vital sign information as one of the medical big data to calculate the disease risk probability.
  • the data is sent to the second server 3.
  • the first server 2 is typically located at the information center of the medical facility and may be a separate server or part of a subsystem that may be part of a larger system.
  • the first server 2 can be one of a plurality of distributed servers that are far apart and communicate over a network (eg, an Internet network) or a private network (eg, a LAN).
  • the medical information collecting system 20 in the first server 2 is configured to collect medical information of the user, including but not limited to the user's name, disease, medical information, medication information, review information, etc. User visits.
  • the medical information is sent to the second server 3 as a second reference data for calculating a probability of disease risk.
  • the medical big data-based insurance actuarial system 30 includes a disease risk calculation module.
  • the disease risk probability calculation module 31 is configured to receive the vital sign information sent by the vital sign information collecting system 10 as the first reference data, and receive the medical information sent by the medical information collecting system 20 as the second reference data, and according to the The first reference data and the second reference data calculate a disease risk probability of the insured person.
  • FIG. 2 is a schematic diagram of functional modules of a preferred embodiment of the disease risk probability calculation module of the present invention.
  • the disease risk probability calculation module 31 includes a request sub-module 311, a receiving sub-module 312, an information extraction sub-module 313, and a probability calculation sub-module 314.
  • the request sub-module 311 is configured to send a request instruction including a preset time range and user information to the vital information collection system 10 and the medical information collection system 20;
  • the receiving sub-module 312 is configured to receive the vital sign information of the user information sent by the vital sign information collecting system 10 in a preset time range, and receive the user sent by the medical information collecting system 20 Medical information within the preset range of information.
  • the information extraction sub-module 313 is configured to extract the vital information and the abnormal information in the medical information.
  • the abnormality information includes first probability information exceeding the preset vital sign information in the vital sign information and/or second probability information of the certain medical disease in the medical information.
  • the vital sign information includes a plurality of vital signs information, for example, including blood pressure information, blood sugar information, heart rate information, weight information, etc., first calculating, respectively, that the plurality of physical sign information exceeds the corresponding preset physical sign information.
  • a probability information is divided into information, and the first probability information is generated according to the preset weight according to the first probability information.
  • the preset weight is determined according to the degree of influence of the insured person's vital sign information on the body, for example: the weight of the blood pressure information is 0.3, the first probability information of the blood pressure information is P11, and the weight of the blood glucose information is 0.3.
  • the first probability information sub-information of the blood glucose information is P12; the weight of the heart rate information is 0.2, the first probability information sub-information of the heart rate information is P13; the weight of the weight information is 0.2, and the first probability information sub-information of the weight information is P14.
  • the first probability information P1 P11 x0.3+P12x0.3+P13x0.2+P14x0.2 o
  • the certain type of disease refers to the insured person A collection of disease information in pre-insured disease insurance products.
  • the disease in the disease insurance products that the insured person is pre-insured The disease information collection includes 25 major diseases specified in the “Code of Practice for the Definition of Diseases for Major Sickness Insurance” formulated by the China Insurance Industry Association, and the probability information of the 25 major diseases is predicted based on the diagnosis information in the medical information.
  • the second probability information P2 is calculated based on the probability score information.
  • the probability calculation sub-module 314 calculates the disease risk probability of the insured person according to the preset weight distribution ratio according to the first probability information PI and/or the second probability information P2! 5 .
  • X and y can be configured by the insurance designer according to the actual situation.
  • the actuarial module 32 is configured to create an insurance product using the disease risk probability as one of the risk factors.
  • the insurance factor includes a non-actuarial factor and an actuarial factor.
  • Disease risk probability is one of the insurance factors, usually used in the creation of disease insurance products. This disease risk probability has an important impact on the output of disease insurance products. Therefore, according to the empirical value, the disease risk probability has at least the weight.
  • the actuarial factor has a weight of 20% to reduce the risk of exporting insurance products.
  • the actuarial factor described in this embodiment refers to the factor that needs to be considered in calculating the insurance premium of the insurance policy and evaluating the business rules to manage the risk.
  • Non-actuarial factors include factors that are not directly related to risk but still affect the pricing of the insurance policy. For example, the name of the insured person.
  • the insurance product output module 33 is configured to output insurance information according to the insurance product created by the insurance actuarial module 32 and generate an insurance policy.
  • the insurance information includes, but is not limited to, an insured object, a contract scope, a premium, a guarantee amount, and insurance attribute information, including but not limited to insurance rules, formulas, and terms.
  • the vital signs information and medical information of the insured person are used as reference data to calculate the disease risk probability of the insured person, and the disease risk probability is taken as one of the insurance factors to participate in the actuarial insurance to create an insurance product, and the insurance product is reduced. Output risk.
  • FIG. 3 is a schematic flow chart of the actuarial method based on medical big data of the present invention.
  • the medical macro data-based insurance actuarial method includes The following steps:
  • S10 receiving the vital sign information sent by the physical sign information collecting system as the first reference data, and receiving the medical information sent by the medical information collecting system as the second reference data, and calculating according to the first reference data and the second reference data. The probability of disease risk for the insured person;
  • the disease risk probability calculation module 31 receives the vital sign information sent by the vital sign information collecting system 10 as the first reference data, and receives the medical information sent by the medical information collecting system 20 as the second reference data, and according to the first The reference data and the second reference data calculate the disease risk probability of the insured person.
  • FIG. 4 is a schematic flowchart of the refinement of step S10 in the embodiment, where the step S10 includes, but is not limited to, steps S11 to S14:
  • S11 transmitting a request instruction including a preset time range and user information to the vital information collection system and the medical information collection system;
  • S12 The physical information of the user information sent by the physical sign information collecting system in the preset time range and the medical information sent by the medical information collecting system in the preset time range
  • the requesting sub-module 311 sends a request instruction including a preset inter-area range and user information to the vital sign information collecting system 10 and the medical information collecting system 20;
  • the user information sent by the receiving vital sign information collecting system 10 is pre- Setting the vital sign information within the range, receiving the medical information of the user information sent by the medical information collecting system 20 within a preset time range;
  • the information extraction sub-module 313 extracts the vital sign information and the abnormality information in the medical information; the abnormality information includes first probability information and/or a location of the vital sign information that exceeds the preset vital sign information.
  • the second probability information of a certain type of disease in the medical information.
  • the vital sign information includes a plurality of physical information, for example, including blood pressure information, blood sugar information, heart rate information, weight information, etc., first calculating first probability information that the plurality of vital sign information exceeds the corresponding preset vital sign information And dividing the information, and generating, according to the first probability information, the first probability information according to the preset weight.
  • the predetermined weight is determined according to the degree of influence of the physical information of the insured person on the body, for example: the weight of the blood pressure information is 0.3, the first probability information of the blood pressure information is P11; the weight of the blood glucose information is 0.3, the first probability information of the blood sugar information is P12; the weight of the heart rate information is 0.2, and the first probability information of the heart rate information is The information is P13; the weight of the weight information is 0.2, and the first probability information of the weight information is P14.
  • the first probability information P1 Pl lx0.3+P12x 0.3+P13x0.2+P14x0.2 according to the preset weight information and the first probability information minute information.
  • the certain type of disease refers to a disease information set in a disease insurance product that the insured person pre-insured.
  • the disease information collection in the disease insurance products pre-insured by the insured person includes the 25 major diseases specified in the “Code of Practice for the Definition of Diseases for Major Sickness Insurance” formulated by the China Insurance Industry Association, and the diagnosis is based on medical information.
  • the information predicts the probability score information of the 25 major diseases, and then calculates the second probability information P2 based on the probability score information.
  • S14 calculating, according to the first probability information and/or the second probability information, a disease risk probability of the insured person according to a preset weight distribution ratio;
  • the probability calculation sub-module 314 calculates the disease risk probability P of the insured person in combination with the first probability information P1 and the second probability information P2.
  • x and y can be configured by the insurance designer according to the actual situation.
  • S20 The actuarial module 32 creates the insurance product by using the disease risk probability as one of the insurance factors
  • the insurance factor further includes a non-actuarial factor and other actuarial factors.
  • the disease risk probability is one of the insurance factors. It is usually used after the creation of a disease insurance product. This disease risk probability has an important effect on the output of the disease insurance product. Therefore, according to the empirical value, the disease risk probability has at least a weight.
  • the actuarial factor has a weight of 20% to reduce the risk of exporting insurance products.
  • the actuarial factor described in this embodiment refers to the factor that needs to be considered in calculating the insurance premium of the insurance policy and evaluating the business rules to manage the risk.
  • Non-actuarial factors include factors that are not directly related to risk but still affect the pricing of insurance policies. For example, the name of the insured person.
  • the insurance product output module 33 outputs the insurance information according to the insurance product created by the insurance actuarial module 32 and generates an insurance policy.
  • the insurance information includes, but is not limited to, an insured object, a contract scope, a premium, a guarantee amount, and insurance attribute information
  • the insurance attribute information includes, but is not limited to, an insurance rule, a formula, and a clause.
  • the vital information and the medical information of the insured person are used as reference data to calculate the insured person's The probability of disease risk, taking the risk probability of disease as one of the insurance factors to participate in insurance actuarial creation of insurance products, reducing the risk of export of insurance products.
  • the medical macro data-based insurance actuarial method and system provided by the present invention use the vital information and medical information of the insured as reference data to calculate the disease risk probability of the insured person, and the disease risk Probability as one of the insurance factors involved in insurance actuarial creation of insurance products, reducing the risk of export of insurance products.

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Abstract

一种基于医学大数据的保险精算方法和系统。所述方法包括如下步骤：接收体征信息采集系统发送的体征信息作为第一参考数据，接收医疗信息采集系统发送的医疗信息作为第二参考数据，根据所述第一参考数据和所述第二参考数据计算参保人员的疾病风险概率（S10）；将所述疾病风险概率作为其中一个保险因子创建保险产品（S20）；根据所述保险产品输出保险信息并生成保险单（S30）。所述基于医学大数据的保险精算方法和系统，将参保人员的体征信息和医疗信息作为参考数据计算参保人员的疾病风险概率，将疾病风险概率作为其中一个保险因子参与保险精算创建保险产品，降低了保险产品的输出风险。

Description

发明名称：基于医学大数据的保险精算方法和系统 技术领域
[0001] 本发明涉及数据处理技术领域， 尤其涉及一种基于医学大数据的保险精算方法 和系统。
背景技术
[0002] 目前， 保险公司推出各种各样的保险产品， 保险销售人员根据保险产品的类别 以及特点定向地推销给不同的人群。 对于疾病类保险产品， 在最终确定保费以 及保额前， 需要参保人员进行必要的体检， 对于患某类疾病风险较高的参保人 员， 保费相对较高， 保额也会做调整， 即会根据参保人员的实际身体状况以及 预期风险来调整保费和保额。 现有的保险精算方法和系统虽然有参考参保人员 的体检报告， 但是没有将参保人员某段吋期内的体征数据和医疗信息结合起来 预测参保人员的疾病风险概率， 扩大了保险产品的输出风险。
技术问题
[0003] 本发明的主要目的在于提供一种基于医学大数据的保险精算方法和系统， 旨在 解决现有保险产品创建系统和方法没有将参保人员某段吋期内的体征数据和诊 疗信息结合起来预测参保人员的疾病风险概率， 导致保险产品输出风险大的问 题。
问题的解决方案
技术解决方案
[0004] 为实现上述目的， 本发明提供了一种基于医学大数据的保险精算方法。
[0005] 所述基于医学大数据的保险精算方法包括如下步骤：
[0006] S10: 接收体征信息采集系统发送的体征信息作为第一参考数据， 接收医疗信 息采集系统发送的医疗信息作为第二参考数据， 根据所述第一参考数据和所述 第二参考数据计算参保人员的疾病风险概率；
[0007] S20: 将所述疾病风险概率作为其中一个保险因子创建保险产品；
[0008] S30: 根据所述保险产品输出保险信息并生成保险单。 [0009] 优选地， 所述步骤 S10包括：
[0010] S11 : 发送包括预设吋间范围和用户信息的请求指令至体征信息采集系统和医 疗信息采集系统；
[0011] S12: 接收体征信息采集系统发送的所述用户信息在预设吋间范围内的体征信 息和所述医疗信息采集系统发送的所述用户信息在预设吋间范围内的医疗信息
[0012] S13: 抽取所述体征信息和所述医疗信息中的异常信息， 所述异常信息包括所 述体征信息中超出预设体征信息的第一概率信息和 /或所述医疗信息中患某类疾 病的第二概率信息；
[0013] S14: 根据第一概率信息和 /或第二概率信息按照预设的权重分配比例计算参保 人员的疾病风险概率。
[0014] 优选地， 所述保险因子包括非精算因子和精算因子。
[0015] 优选地， 所述疾病风险概率的权重至少占精算因子的权重的 20%。
[0016] 优选地， 所述保险信息包括被保对象、 承包范围、 保费、 保额以及保险属性信 息。
[0017]
[0018] 本发明还提供了一种基于医学大数据的保险精算系统。
[0019] 所述基于医学大数据的保险精算系统包括疾病风险计算模块、 保险精算模块以 及保险产品输出模块， 其中：
[0020] 所述疾病风险概率计算模块用于接收体征信息采集系统发送的体征信息作为第 一参考数据， 接收医疗信息采集系统发送的医疗信息作为第二参考数据， 并根 据所述第一参考数据和所述第二参考数据计算参保人员的疾病风险概率；
[0021] 所述保险精算模块用于将所述疾病风险概率作为其中一个保险因子创建保险产
P
ππ ;
[0022] 所述保险产品输出模块用于根据所述保险精算模块创建的保险产品输出保险信 息并生成保险单。
[0023] 优选地， 所述疾病风险概率计算模块包括请求子模块、 接收子模块、 信息抽取 子模块以及概率计算子模块， 其中： [0024] 所述请求子模块用于发送包括预设吋间范围和用户信息的请求指令至体征信息 采集系统和医疗信息采集系统；
[0025] 所述接收子模块用于接收所述体征信息采集系统发送的所述用户信息在预设吋 间范围内的体征信息， 接收所述医疗信息采集系统发送的所述用户信息在预设 吋间范围内的医疗信息；
[0026] 所述信息抽取子模块用于抽取所述体征信息和所述医疗信息中的异常信息， 所 述异常信息包括所述体征信息中超出预设体征信息的第一概率信息和 /或所述医 疗信息中患某类疾病的第二概率信息；
[0027] 所述概率计算子模块根据第一概率信息和 /或第二概率信息按照预设的权重分 配比例计算参保人员的疾病风险概率。
[0028] 优选地， 所述保险因子包括非精算因子和精算因子。
[0029] 优选地， 所述疾病风险概率的权重至少占精算因子的权重的 20%。
[0030] 优选地， 所述保险信息包括被保对象、 承包范围、 保费、 保额以及保险属性信 息。
发明的有益效果
有益效果
[0031] 相较于现有技术， 本发明提供的基于医学大数据的保险精算方法和系统， 将参 保人员的体征信息和医疗信息作为参考数据计算参保人员的疾病风险概率， 将 疾病风险概率作为其中一个保险因子参与保险精算创建保险产品， 降低了保险 产品的输出风险。
对附图的简要说明
附图说明
[0032] 图 1是本发明基于医学大数据的保险精算系统运行环境较佳实施例的结构示意 图；
[0033] 图 2是本发明疾病风险概率计算模块的较佳实施例功能模块示意图；
[0034] 图 3是本发明基于医学大数据的保险精算方法的流程示意图；
[0035] 图 4是图 3中步骤 S 10的细化流程示意图。 实施该发明的最佳实施例
本发明的最佳实施方式
[0036] 为更进一步阐述本发明为达成上述目的所采取的技术手段及功效， 以下结合附 图及较佳实施例， 对本发明的具体实施方式、 结构、 特征及其功效进行详细说 明。 应当理解， 此处所描述的具体实施例仅仅用以解释本发明， 并不用于限定 本发明。
[0037] 为实现本发明目的， 本发明提供了一种基于医学大数据的保险精算方法和系统 ， 通过采集参保人员预设吋间内的体征数据以及从医疗卫生系统中获取参保人 员预设吋间内的诊疗数据， 共同预测参保人员患某类疾病的风险的概率， 从而 将该疾病风险的概率作为精算因子， 计算保险产品的输出指数， 降低保险产品 的输出风险。
[0038] 参照图 1所示， 图 1是本发明基于医学大数据的保险精算系统运行环境较佳实施 例的结构示意图。
[0039] 在本实施例中， 所述基于医学大数据的保险精算系统 30运行于第二服务器 3中 ， 该第二服务器 3连接有体征信息采集设备 1以及第一服务器 2。 所述体征信息采 集设备 1包括体征信息采集系统 10， 所述第一服务器 2包括医疗信息采集系统 20 。 所述体征信息采集设备 1包括但不限于血压采集设、 血糖采集设备、 血脂采集 设备、 血氧采集设备、 体重采集设备中的一种或多种。 所述体征信息采集设备 1 中的体征信息采集系统 10用于采集参保人员在预设吋间内的体征信息， 并将体 征信息作为医学大数据中的其中一个计算疾病风险概率的第一参考数据发送至 第二服务器 3。 所述第一服务器 2通常设置在医疗机构的信息中心， 可以为一个 独立的服务器， 或为子系统的一部分， 该子系统可能是较大的系统的一部分。 例如， 第一服务器 2可为多个分布式服务器中的一个， 这些分布式服务器相隔甚 远并且通过网络 （例如 Internet网络） 或专用网络 （如 LAN) 通信。 所述第一服 务器 2中的医疗信息采集系统 20用于采集用户的医疗信息， 该医疗信息包括但不 限于用户的姓名、 患病吋间、 诊疗信息、 用药信息、 复诊信息等， 用于反应用 户就诊情况。 该医疗信息作为其中一个计算疾病风险概率的第二参考数据发送 至第二服务器 3。 [0040] 在本实施例中， 所述基于医学大数据的保险精算系统 30包括疾病风险计算模块
31、 保险精算模块 32以及保险产品输出模块 33。
[0041] 所述疾病风险概率计算模块 31用于接收体征信息采集系统 10发送的体征信息作 为第一参考数据， 接收所述医疗信息采集系统 20发送的医疗信息作为第二参考 数据， 并根据所述第一参考数据和所述第二参考数据计算参保人员的疾病风险 概率。
[0042] 参照图 2所示， 图 2为本发明疾病风险概率计算模块的较佳实施例功能模块示意 图。
[0043] 所述疾病风险概率计算模块 31包括请求子模块 311、 接收子模块 312、 信息抽取 子模块 313以及概率计算子模块 314。
[0044] 所述请求子模块 311用于发送包括预设吋间范围和用户信息的请求指令至体征 信息采集系统 10和医疗信息采集系统 20;
[0045] 所述接收子模块 312用于接收所述体征信息采集系统 10发送的所述用户信息在 预设吋间范围内的体征信息， 以及接收所述医疗信息采集系统 20发送的所述用 户信息在预设吋间范围内的医疗信息。
[0046] 所述信息抽取子模块 313用于抽取所述体征信息和所述医疗信息中的异常信息 。 所述异常信息包括所述体征信息中超出预设体征信息的第一概率信息和 /或所 述医疗信息中患某类疾病的第二概率信息。 具体地， 当所述体征信息若包括多 种体征信息吋， 例如包括血压信息、 血糖信息、 心率信息、 体重信息等， 则先 分别计算所述多种体征信息超出对应的预设体征信息的第一概率信息分信息， 再根据所述第一概率信息分信息按照预设的权重生成第一概率信息。 所述预设 的权重根据该参保人员的体征信息对其身体的影响程度确定， 例如： 血压信息 的权重为 0.3， 血压信息的第一概率信息分信息为 P11 ; 血糖信息的权重为 0.3， 血糖信息的第一概率信息分信息为 P12; 心率信息的权重为 0.2， 心率信息的第一 概率信息分信息为 P13; 体重信息的权重为 0.2， 体重信息的第一概率信息分信息 为 P14。 根据上述预设的权重信息和第一概率信息分信息， 第一概率信息 P1=P11 x0.3+P12x0.3+P13x0.2+P14x0.2o 具体地， 所述某类疾病指参保人员预参保的疾 病保险产品中的疾病信息集合。 例如， 参保人员预参保的疾病保险产品中的疾 病信息集合中包括中国保险行业协会制定的 《重大疾病保险的疾病定义使用规 范》 中所指定的 25种重大疾病， 则根据医疗信息中确诊信息预测患该 25种重大 疾病的概率分信息， 再根据概率分信息计算第二概率信息 P2。
[0047] 所述概率计算子模块 314根据第一概率信息 PI和 /或第二概率信息 P2按照预设的 权重分配比例计算参保人员的疾病风险概率！5。 例如， 根据临床实践， 体征信息 能够直接反应人体的疾病风险概率， 因此可以预先设定第一概率信息 P1的权重 分配比例为 x， 第二概率信息 P2的全化工分配比例为 y， 其中： X>y且X+y=l。 在 本实施例中， X和 y可以根据实际情况由保险设计人员进行配置。
[0048] 参考图 1所示， 所述保险精算模块 32用于将所述疾病风险概率作为其中一个保 险因子创建保险产品。 所述保险因子包括非精算因子和精算因子。 疾病风险概 率作为其中一个保险因子吋， 通常在创建疾病保险产品吋使用， 此吋疾病风险 概率对疾病保险产品的输出产生重要的影响作用， 因此， 根据经验值， 疾病风 险概率的权重至少占整个精算因子的权重的 20%， 以降低保险产品的输出风险。 本实施例所述精算因子指在计算保险保单的保险费和评估业务规则以管理风险 吋需要考虑的因子。 非精算因子包括不与风险直接相关但仍影响保险保单的定 价的因子。 例如， 参保人员的姓名。
[0049] 所述保险产品输出模块 33用于根据所述保险精算模块 32创建的保险产品输出保 险信息并生成保险单。 具体地， 所述保险信息包括但不限于， 被保对象、 承包 范围、 保费、 保额以及保险属性信息， 该保险属性信息包括但不限于保险规则 、 公式和条款。
[0050] 本发明实施例将参保人员的体征信息和医疗信息作为参考数据计算参保人员的 疾病风险概率， 将疾病风险概率作为其中一个保险因子参与保险精算创建保险 产品， 降低了保险产品的输出风险。
[0051]
[0052] 本发明的另外一个方面， 提供了一种与上述基于医学大数据的保险精算系统对 应的方法。
[0053] 参照图 3所示， 图 3是本发明基于医学大数据的保险精算方法的流程示意图。
[0054] 在本实施例中， 结合图 1和图 2所示， 所述基于医学大数据的保险精算方法包括 如下步骤：
[0055] S10: 接收体征信息采集系统发送的体征信息作为第一参考数据， 接收医疗信 息采集系统发送的医疗信息作为第二参考数据， 根据所述第一参考数据和所述 第二参考数据计算参保人员的疾病风险概率；
[0056] 具体地， 疾病风险概率计算模块 31接收体征信息采集系统 10发送的体征信息作 为第一参考数据， 接收医疗信息采集系统 20发送的医疗信息作为第二参考数据 ， 并根据所述第一参考数据和所述第二参考数据计算参保人员的疾病风险概率 。 参照图 4所示， 图 4为本实施例中步骤 S10的细化流程示意图， 所述步骤 S10包 括但不限于步骤 S11至步骤 S14:
[0057] S11 : 发送包括预设吋间范围和用户信息的请求指令至体征信息采集系统和医 疗信息采集系统；
[0058] S12: 接收体征信息采集系统发送的所述用户信息在预设吋间范围内的体征信 息和所述医疗信息采集系统发送的所述用户信息在预设吋间范围内的医疗信息
[0059] 具体地， 请求子模块 311发送包括预设吋间范围和用户信息的请求指令至体征 信息采集系统 10和医疗信息采集系统 20; 接收体征信息采集系统 10发送的所述 用户信息在预设吋间范围内的体征信息， 接收所述医疗信息采集系统 20发送的 所述用户信息在预设吋间范围内的医疗信息；
[0060] S13: 抽取所述体征信息和所述医疗信息中的异常信息；
[0061] 具体地， 信息抽取子模块 313抽取所述体征信息和所述医疗信息中的异常信息 ； 所述异常信息包括所述体征信息中超出预设体征信息的第一概率信息和 /或所 述医疗信息中患某类疾病的第二概率信息。 当所述体征信息若包括多种体征信 息吋， 例如包括血压信息、 血糖信息、 心率信息、 体重信息等， 则先分别计算 所述多种体征信息超出对应的预设体征信息的第一概率信息分信息， 再根据所 述第一概率信息分信息按照预设的权重生成第一概率信息。 所述预设的权重根 据该参保人员的体征信息对其身体的影响程度确定， 例如： 血压信息的权重为 0. 3， 血压信息的第一概率信息分信息为 P11 ; 血糖信息的权重为 0.3， 血糖信息的 第一概率信息分信息为 P12; 心率信息的权重为 0.2， 心率信息的第一概率信息分 信息为 P13; 体重信息的权重为 0.2， 体重信息的第一概率信息分信息为 P14。 根 据上述预设的权重信息和第一概率信息分信息， 第一概率信息 Pl=Pl lx0.3+P12x 0.3+P13x0.2+P14x0.2。 具体地， 所述某类疾病指参保人员预参保的疾病保险产 品中的疾病信息集合。 例如， 参保人员预参保的疾病保险产品中的疾病信息集 合中包括中国保险行业协会制定的 《重大疾病保险的疾病定义使用规范》 中所 指定的 25种重大疾病， 则根据医疗信息中确诊信息预测患该 25种重大疾病的概 率分信息， 再根据概率分信息计算第二概率信息 P2。
[0062] S14: 根据第一概率信息和 /或第二概率信息按照预设的权重分配比例计算参保 人员的疾病风险概率；
[0063] 具体地， 概率计算子模块 314结合第一概率信息 P1和第二概率信息 P2计算参保 人员的疾病风险概率 P。 例如， 根据临床实践， 体征信息能够直接反应人体的疾 病风险概率， 因此可以预先设定第一概率信息 P1的权重分配比例为 x， 第二概率 信息 P2的全化工分配比例为 y， 其中： X>y且X+y=l。 在本实施例中， x和 y可以根 据实际情况由保险设计人员进行配置。
[0064] S20: 保险精算模块 32将所述疾病风险概率作为其中一个保险因子创建保险产
P
ππ ;
[0065] 具体地， 所述保险因子还包括非精算因子和其他精算因子。 疾病风险概率 Ρ作 为其中一个保险因子吋， 通常在创建疾病保险产品吋使用， 此吋疾病风险概率 对疾病保险产品的输出产生重要的影响作用， 因此， 根据经验值， 疾病风险概 率的权重至少占精算因子的权重的 20%， 以降低保险产品的输出风险。 本实施例 所述精算因子指在计算保险保单的保险费和评估业务规则以管理风险吋需要考 虑的因子。 非精算因子包括不与风险直接相关但仍影响保险保单的定价的因子 。 例如， 参保人员的姓名。
[0066] S30: 保险产品输出模块 33根据保险精算模块 32创建的保险产品输出保险信息 并生成保险单。
[0067] 具体地， 所述保险信息包括但不限于， 被保对象、 承包范围、 保费、 保额以及 保险属性信息， 该保险属性信息包括但不限于保险规则、 公式和条款。
[0068] 本发明实施例将参保人员的体征信息和医疗信息作为参考数据计算参保人员的 疾病风险概率， 将疾病风险概率作为其中一个保险因子参与保险精算创建保险 产品， 降低了保险产品的输出风险。
[0069]
[0070] 以上仅为本发明的优选实施例， 并非因此限制本发明的专利范围， 凡是利用本 发明说明书及附图内容所作的等效结构或等效功能变换， 或直接或间接运用在 其他相关的技术领域， 均同理包括在本发明的专利保护范围内。
工业实用性
[0071] 相较于现有技术， 本发明提供的基于医学大数据的保险精算方法和系统， 将参 保人员的体征信息和医疗信息作为参考数据计算参保人员的疾病风险概率， 将 疾病风险概率作为其中一个保险因子参与保险精算创建保险产品， 降低了保险 产品的输出风险。

Claims

权利要求书
一种基于医学大数据的保险精算方法， 其特征在于， 所述基于医学大 数据的保险精算方法包括如下步骤： S10: 接收体征信息采集系统发 送的体征信息作为第一参考数据， 接收医疗信息采集系统发送的医疗 信息作为第二参考数据， 根据所述第一参考数据和所述第二参考数据 计算参保人员的疾病风险概率； S20: 将所述疾病风险概率作为其中 一个保险因子创建保险产品； S30: 根据所述保险产品输出保险信息 并生成保险单。
如权利要求 1所述的基于医学大数据的保险精算方法， 其特征在于， 所述步骤 S10包括： S11 : 发送包括预设吋间范围和用户信息的请求 指令至体征信息采集系统和医疗信息采集系统； S12: 接收体征信息 采集系统发送的所述用户信息在预设吋间范围内的体征信息和所述医 疗信息采集系统发送的所述用户信息在预设吋间范围内的医疗信息； S13: 抽取所述体征信息和所述医疗信息中的异常信息， 所述异常信 息包括所述体征信息中超出预设体征信息的第一概率信息和 /或所述 医疗信息中患某类疾病的第二概率信息； S14: 根据第一概率信息和 / 或第二概率信息按照预设的权重分配比例计算参保人员的疾病风险概 率。
如权利要求 1所述的基于医学大数据的保险精算方法， 其特征在于， 所述保险因子包括非精算因子和精算因子。
如权利要求 3所述的基于医学大数据的保险精算方法， 其特征在于， 所述疾病风险概率的权重至少占所述精算因子的权重的 20%。
如权利要求 1所述的基于医学大数据的保险精算方法， 其特征在于， 所述保险信息包括被保对象、 承包范围、 保费、 保额以及保险属性信 息。
一种基于医学大数据的保险精算系统， 其特征在于， 所述基于医学大 数据的保险精算系统包括疾病风险计算模块、 保险精算模块以及保险 产品输出模块， 其中： 所述疾病风险概率计算模块用于接收体征信息 采集系统发送的体征信息作为第一参考数据， 接收医疗信息采集系统 发送的医疗信息作为第二参考数据， 并根据所述第一参考数据和所述 第二参考数据计算参保人员的疾病风险概率； 所述保险精算模块用于 将所述疾病风险概率作为其中一个保险因子创建保险产品； 所述保险 产品输出模块用于根据所述保险精算模块创建的保险产品输出保险信 息并生成保险单。
如权利要求 6所述的基于医学大数据的保险精算系统， 其特征在于， 所述疾病风险概率计算模块包括请求子模块、 接收子模块、 信息抽取 子模块以及概率计算子模块， 其中： 所述请求子模块用于发送包括预 设吋间范围和用户信息的请求指令至体征信息采集系统和医疗信息采 集系统； 所述接收子模块用于接收所述体征信息采集系统发送的所述 用户信息在预设吋间范围内的体征信息， 以及接收所述医疗信息采集 系统发送的所述用户信息在预设吋间范围内的医疗信息； 所述信息抽 取子模块用于抽取所述体征信息和所述医疗信息中的异常信息， 所述 异常信息包括所述体征信息中超出预设体征信息的第一概率信息和 / 或所述医疗信息中患某类疾病的第二概率信息； 所述概率计算子模块 根据第一概率信息和 /或第二概率信息按照预设的权重分配比例计算 参保人员的疾病风险概率。
如权利要求 6所述的基于医学大数据的保险精算系统， 其特征在于， 所述保险因子包括非精算因子和精算因子。
如权利要求 8所述的基于医学大数据的保险精算系统， 其特征在于， 所述疾病风险概率的权重至少占精算因子的权重的 20%。
如权利要求 6所述的基于医学大数据的保险精算系统， 其特征在于， 所述保险信息包括被保对象、 承包范围、 保费、 保额以及保险属性信 息。
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