WO2017152636A1 - 基于医疗大数据的疾病预警系统及方法 - Google Patents

基于医疗大数据的疾病预警系统及方法 Download PDF

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Publication number
WO2017152636A1
WO2017152636A1 PCT/CN2016/104129 CN2016104129W WO2017152636A1 WO 2017152636 A1 WO2017152636 A1 WO 2017152636A1 CN 2016104129 W CN2016104129 W CN 2016104129W WO 2017152636 A1 WO2017152636 A1 WO 2017152636A1
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disease
weather
information
medical
patient
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PCT/CN2016/104129
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English (en)
French (fr)
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张贯京
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深圳市前海安测信息技术有限公司
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Publication of WO2017152636A1 publication Critical patent/WO2017152636A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L65/00—Network arrangements, protocols or services for supporting real-time applications in data packet communication
    • H04L65/40—Support for services or applications
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Definitions

  • the present invention relates to the field of medical big data processing, and in particular, to a disease warning system and method based on medical big data.
  • Big data technology can accelerate medical conjecture and discover the transformation of medical practice: With the growing private and public medical data, big data technology helps people store and manage medical big data and from large volume, high complexity The value of the data will be extracted, and related medical technologies and products will continue to emerge, which will likely open up a new golden generation for the medical industry.
  • the current medical data analysis system does not consider the influence of weather factors in the analysis and processing of medical big data, nor does it warn patients according to weather conditions, which reduces the accuracy of medical data.
  • the main object of the present invention is to provide a disease warning system and method based on medical big data, aiming at solving the technical problem of not being able to warn patients through weather conditions during medical big data processing.
  • the present invention provides a disease warning system based on medical big data, which runs in a cloud server, and the cloud server communicates with a hospital information system, a client terminal, and a weather letter through a communication network.
  • the medical big data-based disease early warning system includes: an information acquisition module, configured to acquire a medical information package from the hospital information system, cut the medical information package into multiple medical data, and analyze each pen a disease date in the medical data; an information association module, configured to acquire historical weather information corresponding to the date of illness in each medical data from the weather information platform, and to compare each medical data with the corresponding historical weather information Correlation; a data analysis module, configured to analyze medical data associated with the historical weather information to obtain a patient affected by weather factors; and a disease prediction module, configured to acquire weather forecast information from the weather information platform, according to the The weather forecast information determines whether the patient is affected by the weather is greater than the disease impact factor; the disease warning module is configured to generate the early warning information and send the warning information to the client terminal corresponding to the patient when the patient is greater than the disease influence factor.
  • the medical data includes a patient name, a patient's name, a diseased day, a disease name, a disease cause, a drug name, a drug quantity, a doctor name, a doctor's office, a fee, and a patient's contact information.
  • the historical weather information includes a location, a temperature, a wind direction, a weather condition, and an air quality.
  • the warning information includes a patient name, weather forecast information, a disease in the same weather condition, and a precaution to prevent the disease.
  • the data analysis module is further configured to: classify medical data associated with historical weather information to obtain a disease name corresponding to each historical weather information and a quantity of each disease name; according to each disease name Calculate the weight of the disease affected by weather factors; when the weight exceeds the preset value, the disease is determined to be affected by weather factors; and the patient affected by weather factors is retrieved from the medical data according to the name of the disease affected by the weather factor .
  • the present invention also provides a disease warning method based on medical big data, which is applied to a cloud server, wherein the cloud server is connected to a hospital information system, a client terminal, and a weather information platform through a network, and the method includes the following steps:
  • the hospital information system acquires a medical information package, and cuts the medical information package into a plurality of medical data; analyzes a sick date in each medical data; and obtains a sick date in each medical data from the weather information platform Corresponding historical weather information, and correlating each medical data with the corresponding historical weather information; analyzing medical data associated with historical weather information to obtain patients affected by weather factors; obtaining weather from the weather information platform Forecasting information; determining, according to the weather forecast information, whether the patient is affected by the weather is greater than a disease influencing factor;
  • the disease impact factor ⁇ generates warning information and sends it to the client terminal corresponding to the patient.
  • the medical data includes a patient name, a patient's name, a diseased day, a disease name, a disease cause, a drug name, a drug quantity, a doctor name, a doctor's office, a fee, and a patient's contact information.
  • the historical weather information includes a location, a temperature, a wind direction, a weather condition, and an air quality.
  • the warning information includes a patient name, weather forecast information, a disease in the same weather condition, and a precaution to prevent the disease.
  • the analyzing the medical data associated with the historical weather information to obtain the patient affected by the weather factor comprises the following steps: classifying the medical data associated with the historical weather information to obtain each historical weather information. Corresponding disease name and the number of each disease name; calculating the weight of the disease affected by weather factors according to the number of each disease name; when the weight exceeds the preset value, the disease is determined to be affected by weather factors; Disease names affected by weather factors retrieve medical data from patients affected by weather factors.
  • the medical big data-based disease early warning system and method of the present invention adopts the above technical solutions, and the technical effects brought by the following are:
  • the medical big data can be associated with historical weather information, and the medical information associated with historical weather information is large.
  • the data analysis process is used to obtain the patients affected by the weather factors, and the patients affected by the weather factors and the early warnings reduce the risk of the patient being affected again by the weather factors.
  • FIG. 1 is a schematic diagram of an application environment of a preferred embodiment of a medical big data based disease early warning system according to the present invention
  • FIG. 2 is a block diagram showing a preferred embodiment of a medical big data based disease early warning system according to the present invention
  • FIG. 3 is a flow chart of a preferred embodiment of the medical big data based disease early warning method of the present invention.
  • step S34 in FIG. 3 is a detailed flowchart of step S34 in FIG. 3.
  • FIG. 1 is a schematic diagram of an application environment of a preferred embodiment of a medical big data based disease early warning system according to the present invention.
  • the medical big data-based disease early warning system 10 operates on the cloud server 1.
  • the cloud server 1 is communicably connected to one or more hospital information systems 2 (illustrated by taking two examples in FIG. 1) through the communication communication network 3 to obtain medical information packets from the hospital information system connection 2, and The medical information packet is cut into a plurality of medical data. Since the medical information package is obtained from a plurality of hospital information systems 2, the medical data acquired by the cloud server 1 is medical data (Big Data) composed of a plurality of medical data.
  • Medical Data Medical Data
  • the medical data includes, but is not limited to, patient name, patient's age, disease, disease name, cause of illness, drug name, number of drugs, name of doctor, department of visit, cost, and contact information of patient (eg, electronic Email address, mobile number, instant messaging account ⁇ ⁇ ⁇ ,.
  • the communication network 3 may be a wired communication network or a wireless communication network.
  • the communication network 3 is preferably a wireless communication network, including but not limited to, a GSM network, a GPRS network, a CDMA network, a TD-SCDMA network, a WiMAX network, a TD-LTE network, an FDD-LTE network, and the like. transporting network.
  • the cloud server 1 communicates with one or more client terminals 4 (illustrated by taking two examples in FIG. 1) through the communication network 3, and transmits medical data corresponding to the patient to the client terminal corresponding to the patient. 4.
  • the cloud server 1 may also filter and filter the medical data to meet non-compliant, incomplete and repeated redundant data, and analyze and process the filtered medical data.
  • the processed medical data (for example, medical data associated with the weather information) is transmitted to the corresponding client terminal 4 via the communication network 3.
  • the cloud server 1 is communicatively coupled to the weather information platform 5 via the communication network 3 for acquiring weather information from the weather information platform 5.
  • the weather information platform 5 is configured to provide weather information, including, but not limited to, location, temperature (highest temperature and minimum temperature), wind direction (for example, southerly winds, northerly winds, etc.), weather conditions (for example, weather conditions such as fine weather, light rain, heavy rain, snow, heavy snow, etc.), and air quality (for example, PM 2.5 values).
  • the cloud server 1 is a cloud platform or a server in the cloud platform, and the data transmission capability and the data storage capability of the cloud server 1 can better manage and/or assist the hospital.
  • the medical data processing and transmission of the information system 2 and the client terminal 4 facilitates the patient to understand his or her medical data.
  • the client terminal 4 can be, but is not limited to, a smartphone, a tablet, a personal digital assistant (PDA), a personal computer, an electronic signboard, and the like, any other suitable portable electronic device.
  • PDA personal digital assistant
  • FIG. 2 is a schematic diagram of a preferred embodiment of a medical big data based disease early warning system of the present invention.
  • the medical big data-based disease early warning system 10 is applied to the cloud server 1, and the cloud server 1 includes, but is not limited to, a medical big data-based disease early warning system 10, a storage unit 11, and a processing unit 12. And communication unit 13.
  • the storage unit 11 may be a read only storage unit ROM, an electrically erasable storage unit EEPRO M, a flash storage unit FLASH or a solid hard disk.
  • the processing unit 12 may be a central processing unit (CPU), a microcontroller (MCU), a data processing chip, or an information processing unit having a data processing function.
  • CPU central processing unit
  • MCU microcontroller
  • data processing chip or an information processing unit having a data processing function.
  • the communication unit 13 is a wireless communication interface with remote wireless communication functions, for example, supports communication technologies such as GSM, GPRS, WCDMA, CDMA, TD-SCDMA, WiMAX, TD-LTE, FDD-LT E. Communication interface.
  • communication technologies such as GSM, GPRS, WCDMA, CDMA, TD-SCDMA, WiMAX, TD-LTE, FDD-LT E. Communication interface.
  • the medical big data-based disease early warning system 10 includes, but is not limited to, an information acquisition module 10 1 , an information association module 102 , a data analysis module 103 , a disease prediction module 104 , and a disease warning module 105 .
  • the module referred to in the present invention refers to a series of computer program instruction segments which can be executed by the processing unit 12 of the cloud server 1 and which can perform a fixed function, which are stored in the storage unit 11 of the cloud server 1.
  • the information acquiring module 101 is configured to acquire a medical information package from the hospital information system 2, and cut the medical information package into multiple medical data.
  • the hospital information system 2 provides a data import interface (for example, an application program interface, API), and the device or system that accesses the data import interface can obtain medical data from the hospital information system 2.
  • Information acquisition module Block 101 invokes an API interface provided by the hospital information system 2 to obtain a packet.
  • the medical data is sent to the cloud server, and the encryption and decryption algorithm is adopted (for example, the MD5 encryption and decryption algorithm, the RSA encryption and decryption algorithm, and the DES plus The decryption algorithm, the DSA encryption and decryption algorithm, the AES encryption and decryption algorithm, etc.) first encrypt the medical information packet, then transmit it to the cloud server 1 and then use a symmetric decryption algorithm to decrypt, and finally cut the medical information packet into multiple medical treatments. data.
  • the encryption and decryption algorithm for example, the MD5 encryption and decryption algorithm, the RSA encryption and decryption algorithm, and the DES plus The decryption algorithm, the DSA encryption and decryption algorithm, the AES encryption and decryption algorithm, etc.
  • the information obtaining module 101 is further configured to filter each medical data to filter out redundant data that does not meet requirements, incompleteness, and repetition, and parse the diseased date in each medical data that is filtered. Specifically, the information acquisition module 101 parses the date of illness of each medical data from each medical data according to a common date format. For example, "2012-03-08" or "2012.03.08” is used as a date format to resolve the date of illness in each medical data.
  • the information association module 102 is configured to acquire historical weather information corresponding to the diseased period in each medical data from the weather information platform 5, and associate each medical data with the corresponding historical weather information. .
  • the weather information platform 5 provides an API interface, and the device or system accessing the API interface can obtain historical weather information from the weather information platform 5.
  • the information association module 102 invokes an API interface provided by the weather information platform 5 to obtain historical weather information.
  • the information association module 102 invokes the API interface provided by the weather information platform 5 and sends the disease date to the weather information platform 5, and the weather information platform 5 searches for the disease date with the disease date as a key. Corresponding historical weather information is transmitted back to the cloud server 1.
  • the data analysis module 103 is configured to analyze medical data associated with the historical weather information to obtain a patient affected by weather factors.
  • the manner of analyzing the medical data associated with the weather information to obtain the patient affected by the weather factor is as follows: (1)
  • the data analysis module 103 classifies the medical data associated with the historical weather information to obtain The name of the disease corresponding to the historical weather information and the number of each disease name. For example, in the case of heavy snow, there are four diseases in the medical data, namely, a cold, a fever, an asthma, and a fracture. Among them, there are fifty-eight colds, twenty fevers, and fifteen asthmas.
  • the data analysis module 103 calculates the weight of the disease affected by the weather factor according to the number of each disease name. For the sake of simplicity, the weights calculated in this embodiment are equal to the number of each disease name, that is, if the number of asthma is fifteen In the pen, the weight of asthma affected by weather factors is fifteen; (3) When the weight exceeds a preset value, the data analysis module 103 determines that the disease is affected by weather factors.
  • the data analysis module 103 determines that the asthma is affected by the weather factor; (4) the data analysis module 103 is The name of the disease affected by the weather factor retrieves the patient affected by the weather factor in the medical data, for example, using the "asthma" as a key to retrieve the patient with asthma in the medical data.
  • a preset value for example, the number of asthma is 15, the preset value is 10
  • the disease prediction module 104 is configured to acquire weather forecast information from the weather information platform 5.
  • the weather forecast information includes weather information in a future preset interval, for example, weather information for the next week.
  • the disease prediction module 104 is further configured to determine, according to the weather forecast information, whether the weather affected by the weather factor is greater than the disease impact factor.
  • the disease influencing factor refers to the severity of the patient's exposure to the weather and can be quantified by a numerical threshold. For example, if the severity of the disease is set to a range of 1 to 10, the disease influencing factor can be set to 6.
  • the patient When the patient is affected by the weather by more than 5 (for example, between 5 and 10), the patient is more affected by weather factors; when the patient is affected by the weather less than 5 (for example, between 1 and 6), the patient The degree of impact due to weather factors is relatively minor.
  • the disease warning module 105 is configured to generate an early warning information and send it (for example, according to a contact manner of a patient in the medical data) to the patient affected by the weather factor when the patient is greater than the disease influence factor ⁇ Corresponding client terminal 4.
  • the warning information includes, but is not limited to, a patient name, weather forecast information, a disease in the same weather condition, and a precaution to prevent the disease.
  • the warning information may be sent to the client terminal 4 in the form of text, voice and/or audio and video.
  • the warning message is as follows: "Respected XXX, cold weather will occur in the next few days, the minimum temperature will drop below zero, and the lowest temperature in the past will drop to zero.
  • FIG. 3 it is a flow chart of a preferred embodiment of the medical big data based disease early warning method of the present invention.
  • the medical big data-based disease early warning method is applied to the cloud server 1, The method includes the following steps:
  • Step S31 The information acquiring module 101 acquires a medical information package from the hospital information system 2, and cuts the medical information package into multiple medical data.
  • the hospital information system 2 provides a data import interface (for example, an application program interface, API), and a device or system that accesses the data import interface can obtain medical information from the hospital information system 2. data.
  • the information obtaining module 101 invokes an API interface provided by the hospital information system 2 to acquire a packet and cut the medical information packet into a plurality of medical data.
  • Step S32 the information acquisition module 101 parses the date of illness in each medical data.
  • the information obtaining module 101 first filters each medical data to filter out redundant data that does not meet requirements, incompleteness, and repetition, and then parses each of the filtered medical data according to a common date format.
  • the date of illness of the pen medical data For example, "2012-03-08" or "2012.03.08” is used as a date format to resolve the date of illness in each medical data.
  • Step S33 the information association module 102 is configured to acquire historical weather information corresponding to the disease date in each medical data from the weather information platform 5, and associate each medical data with the corresponding historical weather information.
  • the weather information platform 5 provides an API interface, and the device or system accessing the API interface can obtain historical weather information from the weather information platform 5.
  • the information association module 102 invokes an API interface provided by the weather information platform 5 to obtain historical weather information.
  • the information association module 102 calls the API interface provided by the weather information platform 5 and sends the disease date to the weather information platform 5, and the weather information platform 5 searches for the disease with the disease date as a key.
  • the historical weather information corresponding to the date is transmitted back to the cloud server 1.
  • Step S34 the data analysis module 103 analyzes the medical data associated with the historical weather information to obtain a patient affected by weather factors.
  • the specific refinement step of step S34 includes steps S341 to S344.
  • Step S35 the disease prediction module 104 acquires weather forecast information from the weather information platform 5.
  • the weather forecast information includes weather information in a future preset interval, for example, weather information for the next week.
  • Step S36 the disease prediction module 104 determines, according to the weather forecast information, whether the weather affected by the weather factor is greater than the disease impact factor.
  • the disease influencing factor refers to the severity of the patient's exposure to the weather and can be quantified using a numerical threshold, for example, assuming that the disease is severely affected. If the severity is set between 1 and 10, the disease impact factor can be set to 6. When the patient is affected by the weather by more than 5 (for example, between 5 and 10), the patient is more affected by weather factors; when the patient is affected by the weather less than 5 (for example, between 1 and 6), the patient The degree of impact due to weather factors is relatively minor. If the patient is affected by the weather more than the disease influence factor, step S37 is performed; if the patient is affected by the weather less than or equal to the disease influence factor, the process ends.
  • Step S37 the disease warning module 105 generates the warning information and transmits (for example, according to the contact information of the patient in the medical data) to the client terminal 4 corresponding to the patient affected by the weather factor.
  • the warning information includes, but is not limited to, a patient name, weather forecast information, a disease in the same weather condition, and a precaution to prevent the disease.
  • the warning information may be sent to the client terminal 4 in the form of text, voice and/or audio and video.
  • the warning message is as follows: "Respected XXX, cold weather will occur in the next few days, the minimum temperature will drop below zero, and the lowest temperature in the past will drop to zero.
  • FIG. 4 is a detailed flowchart of step S34 in FIG.
  • the specific refinement step of step S34 includes the following steps:
  • Step S341 the data analysis module 103 classifies the medical data associated with the historical weather information to obtain the disease name and the number of each disease name corresponding to each historical weather information.
  • the medical data For example, in the case of heavy snow, there are four diseases in the medical data, namely, a cold, a fever, an asthma, and a fracture. Among them, there are fifty-eight colds, twenty fevers, and fifteen asthmas. There are 27 strokes and fractures.
  • Step S342 the data analysis module 103 calculates the weight of the disease affected by the weather factor according to the number of each disease name.
  • the weights in this embodiment are equal to the number of each disease name, that is, if the number of asthma is fifteen, the weight of asthma affected by weather factors is fifteen.
  • Step S343 when the weight exceeds a preset value, the data analysis module 103 determines that the disease is affected by weather factors. In this embodiment, if the weight exceeds a preset value (for example, the number of asthma is 15, the preset value is 10), the data analysis module 103 determines that the asthma is affected by the weather factor. [0051] Step S344, the data analysis module 103 searches for the patient affected by the weather factor in the medical data according to the name of the disease affected by the weather factor, for example, using the "asthma" as a key to retrieve the asthmatic data in the medical data. patient.
  • a preset value for example, the number of asthma is 15, the preset value is 10
  • the medical big data-based disease early warning system and method of the present invention adopts the above technical solutions, and the technical effects brought by the following are:
  • the medical big data can be associated with historical weather information, and the medical information associated with historical weather information is large.

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Abstract

本发明提供一种基于医疗大数据的疾病预警系统及方法，该方法包括步骤：从医院信息系统获取医疗信息包，将医疗信息包切割成多笔医疗数据；解析每笔医疗数据中的患病日期；从天气信息平台获取每笔医疗数据中的患病日期所对应的历史天气信息，并将每笔医疗数据与所对应的历史天气信息关联；对历史天气信息所关联的医疗数据进行分析以获得受天气因素影响的患者；从天气信息平台获取天气预报信息以判断患者受天气影响程度是否大于疾病影响因子；当患者大于疾病影响因子时，生成预警信息并发送给患者所对应的客户终端。本发明通过医疗大数据分析处理以得到受天气因素影响的患者并对患者及时预警，降低了患者受天气因素影响而再次患病的风险。

Description

基于医疗大数据的疾病预警系统及方法 技术领域
[0001] 本发明涉及医疗大数据处理领域， 尤其涉及一种基于医疗大数据的疾病预警系 统及方法。
背景技术
[0002] 近年来随着互联网、 云计算、 移动通信和物联网等的迅猛发展， 无所不在的移 动设备、 RFID、 无线传感器每分每秒都在产生数据， 数以亿计用户的互联网服 务吋吋刻刻在产生巨量的交互， 要处理的数据量巨大， 数据一直都在以每年 50% 的速度增长， 而业务需求和竞争压力对数据处理的实吋性、 有效性又提出了更 高要求， 传统的常规技术手段根本无法应付， 因此， 大数据技术 （Big Data) 成 为近来的一个技术热点， 引起了广泛的重视。
[0003] 通过大数据技术可以加速医学的猜想、 发现到医疗实践的转化： 借助于不断增 长的私密和公幵医疗数据， 大数据技术帮助人们存储管理好医疗大数据并从大 体量、 高复杂的数据中提取价值， 相关的医疗技术、 产品将不断涌现， 将有可 能给医疗行业幵拓一个新的黄金吋代。
[0004] 然而， 现阶段的医疗数据分析系统在针对医疗大数据进行分析处理吋， 并没有 考虑天气因素的影响， 也不会根据天气情况对患者进行预警， 降低了医疗大数 据的准确性。
技术问题
[0005] 本发明的主要目的在于提供一种基于医疗大数据的疾病预警系统及方法， 旨在 解决现有对医疗大数据处理过程中无法通过天气情况对患者进行预警的技术问 题。
问题的解决方案
技术解决方案
[0006] 为实现上述目的， 本发明提供了一种基于医疗大数据的疾病预警系统， 运行于 云服务器中， 所述云服务器通过通信网络与医院信息系统、 客户终端及天气信 息平台连接， 所述基于医疗大数据的疾病预警系统包括： 信息获取模块， 用于 从所述医院信息系统获取医疗信息包， 将所述医疗信息包切割成多笔医疗数据 ， 以及解析每笔医疗数据中的患病日期； 信息关联模块， 用于从所述天气信息 平台获取每笔医疗数据中的患病日期所对应的历史天气信息， 并将每笔医疗数 据与所对应的历史天气信息关联； 数据分析模块， 用于对所述历史天气信息所 关联的医疗数据进行分析以获得受天气因素影响的患者； 疾病预测模块， 用于 从所述天气信息平台获取天气预报信息， 根据所述天气预报信息判断所述患者 受天气影响程度是否大于疾病影响因子； 疾病预警模块， 用于当所述患者大于 所述疾病影响因子吋， 生成预警信息并发送给所述患者所对应的客户终端。
[0007] 优选的， 所述医疗数据包括患者姓名、 患者年齢、 患病吋间、 疾病名称、 患病 原因、 药品名称、 药品数量、 医生姓名、 就诊科室、 费用以及患者的联系方式
[0008] 优选的， 所述历史天气信息包括地点、 温度、 风向、 天气情况以及空气质量。
[0009] 优选的， 所述预警信息包括患者名称、 天气预报信息、 相同天气情况下所患的 疾病及预防所患的疾病的注意事项。
[0010] 优选的， 所述数据分析模块还用于： 将历史天气信息所关联的医疗数据进行分 类得到每种历史天气信息对应的疾病名称及每种疾病名称的数量； 根据每种疾 病名称的数量计算该疾病受天气因素影响的权重； 当所述权重超过预设值吋， 则认定该疾病受天气因素影响； 根据受天气因素影响的疾病名称在医疗数据中 检索出受天气因素影响的患者。
[0011] 本发明还提供了一种基于医疗大数据的疾病预警方法， 应用于云服务器中， 所 述云服务器通过网络与医院信息系统、 客户终端及天气信息平台连接， 该方法 包括步骤： 从所述医院信息系统获取医疗信息包， 将所述医疗信息包切割成多 笔医疗数据； 解析每笔医疗数据中的患病日期； 从所述天气信息平台获取每笔 医疗数据中的患病日期所对应的历史天气信息， 并将每笔医疗数据与所对应的 历史天气信息关联； 对历史天气信息所关联的医疗数据进行分析以获得受天气 因素影响的患者； 从所述天气信息平台获取天气预报信息； 根据所述天气预报 信息判断所述患者受天气影响程度是否大于疾病影响因子； 当所述患者大于所 述疾病影响因子吋， 生成预警信息并发送给所述患者所对应的客户终端。
[0012] 优选的， 所述医疗数据包括患者姓名、 患者年齢、 患病吋间、 疾病名称、 患病 原因、 药品名称、 药品数量、 医生姓名、 就诊科室、 费用以及患者的联系方式
[0013] 优选的， 所述历史天气信息包括地点、 温度、 风向、 天气情况以及空气质量。
[0014] 优选的， 所述预警信息包括患者名称、 天气预报信息、 相同天气情况下所患的 疾病及预防所患的疾病的注意事项。
[0015] 优选的， 所述对历史天气信息所关联的医疗数据进行分析以获得受天气因素影 响的患者包括如下步骤： 将所述历史天气信息所关联的医疗数据进行分类得到 每种历史天气信息对应的疾病名称及每种疾病名称的数量； 根据每种疾病名称 的数量计算该疾病受天气因素影响的权重； 当所述权重超过预设值吋， 则认定 该疾病受天气因素影响； 根据受天气因素影响的疾病名称在医疗数据中检索出 受天气因素影响的患者。
发明的有益效果
有益效果
[0016] 本发明所述基于医疗大数据的疾病预警系统及方法采用上述技术方案， 带来的 技术效果为： 能够对医疗大数据与历史天气信息关联， 将对历史天气信息所关 联的医疗大数据分析处理以得到受天气因素影响的患者， 并对所述受天气因素 影响的患者及吋预警， 降低了患者受天气因素影响而再次患病的风险。
对附图的简要说明
附图说明
[0017] 图 1是本发明基于医疗大数据的疾病预警系统优选实施例的应用环境示意图； [0018] 图 2是本发明基于医疗大数据的疾病预警系统优选实施例的模块示意图；
[0019] 图 3是本发明基于医疗大数据的疾病预警方法优选实施例的流程图；
[0020] 图 4是图 3中步骤 S34的细化流程图。
[0021] 本发明目的的实现、 功能特点及优点将结合实施例， 参照附图做进一步说明。
实施该发明的最佳实施例 本发明的最佳实施方式
[0022] 为更进一步阐述本发明为达成预定发明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， 详细 说明如下。 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。
[0023] 参照图 1所示， 图 1是本发明基于医疗大数据的疾病预警系统优选实施例的应用 环境示意图。 在本实施例中， 所述基于医疗大数据的疾病预警系统 10运行于云 服务器 1。 所述云服务器 1通过通信通信网络 3与一个或多个医院信息系统 2 (图 1 中以两个为例进行说明） 通信连接， 以从所述医院信息系统连接 2获取医疗信息 包， 并将所述医疗信息包切割成多笔医疗数据。 由于医疗信息包是从多个医院 信息系统 2中获得， 因此云服务器 1获取的医疗数据是具有多种医疗数据构成的 医疗大数据 （Big Data) 。 所述医疗数据包括， 但不限于， 患者姓名、 患者年齢 、 患病吋间、 疾病名称、 患病原因、 药品名称、 药品数量、 医生姓名、 就诊科 室、 费用及患者的联系方式 （例如， 电子邮箱地址、 手机号码、 即吋通信账号 Ι π Λ∑Ι、。
[0024] 所述通信网络 3可以是有线通信网络或无线通信网络。 在本实施例中， 所述通 信网络 3优选为无线通信网络， 包括但不限于， GSM网络、 GPRS网络、 CDMA 网络、 TD-SCDMA网络、 WiMAX网络、 TD-LTE网络、 FDD-LTE网络等无线传 输网络。
[0025] 所述云服务器 1通过所述通信网络 3与一个或多个客户终端 4 (图 1中以两个为例 进行说明） 通信连接， 将患者对应的医疗数据发送给患者对应的客户终端 4。 在 其它实施例中， 所述云服务器 1还可以对所述医疗数据进行筛选过滤掉不符合要 求的、 不完整的及重复的冗余数据， 并将筛选后的医疗数据进行分析处理并将 分析处理后的医疗数据 （例如， 与天气信息关联后的医疗数据） 通过通信网络 3 发送给对应的客户终端 4。
[0026] 所述云服务器 1通过所述通信网络 3与天气信息平台 5通信连接， 用于从所述天 气信息平台 5获取天气信息。 具体地说， 所述天气信息平台 5用于提供天气信息 ， 所述天气信息包括， 但不限于， 地点、 温度 （最高气温及最低气温） 、 风向 (例如， 偏南风、 偏北风等） 、 天气情况 （例如， 天晴、 小雨、 大雨、 雪、 大 雪等天气情况） 、 及空气质量 （例如， PM 2.5值） 等信息。
[0027] 需要说明的是， 所述云服务器 1是一种云平台或云平台中的一台服务器， 通过 云服务器 1的数据传输能力及数据存储能力， 可以更好地管理及 /或协助医院信息 系统 2及客户终端 4的医疗数据处理与传输， 有利于患者了解自身的医疗数据。 所述客户终端 4可以是， 但不限于， 智能手机、 平板电脑、 个人数字助理 （Perso nal Digital Assistant, PDA) 、 个人电脑、 电子看板等其它任意合适的便携式电 子设备。
[0028] 参照图 2所示， 图 2是本发明基于医疗大数据的疾病预警系统的优选实施例的模 块示意图。 在本实施例中， 所述基于医疗大数据的疾病预警系统 10应用于云服 务器 1， 该云服务器 1包括， 但不仅限于， 基于医疗大数据的疾病预警系统 10、 存储单元 11、 处理单元 12及通信单元 13。
[0029] 所述的存储单元 11可以为一种只读存储单元 ROM, 电可擦写存储单元 EEPRO M、 快闪存储单元 FLASH或固体硬盘等。
[0030] 所述的处理单元 12可以为一种中央处理器 （Central Processing Unit, CPU) 、 微控制器 （MCU) 、 数据处理芯片、 或者具有数据处理功能的信息处理单元。
[0031] 所述的通信单元 13为一种具有远程无线通讯功能的无线通讯接口， 例如， 支持 GSM、 GPRS、 WCDMA、 CDMA、 TD-SCDMA、 WiMAX、 TD-LTE、 FDD-LT E等通讯技术的通讯接口。
[0032] 所述的基于医疗大数据的疾病预警系统 10包括， 但不局限于， 信息获取模块 10 1、 信息关联模块 102、 数据分析模块 103、 疾病预测模块 104及疾病预警模块 105 。 本发明所称的模块是指一种能够被所述云服务器 1的处理单元 12执行并且能够 完成固定功能的一系列计算机程序指令段， 其存储在所述云服务器 1的存储单元 11中。
[0033] 所述信息获取模块 101用于从所述医院信息系统 2获取医疗信息包， 并将所述医 疗信息包切割成多笔医疗数据。 具体而言， 所述医院信息系统 2提供数据导入接 口 （例如， 应用程序接口， Application Program Interface, API) ， 接入数据导入 接口的设备或系统均可从所述医院信息系统 2中获取医疗数据。 所述信息获取模 块 101调用所述医院信息系统 2提供的 API接口以获取信息包。 需要说明的是， 由 于所述医疗信息属于隐私信息， 为了确保信息安全， 所述医疗数据发送给云服 务器 1吋， 会通过加解密算法 （例如， MD5加解密算法、 RSA加解密算法、 DES 加解密算法、 DSA加解密算法、 AES加解密算法等） 先对医疗信息包进行加密处 理， 之后传输给所述云服务器 1再采用对称的解密算法进行解密， 最后将医疗信 息包切割成多笔医疗数据。
[0034] 所述信息获取模块 101还用于对每笔医疗数据进行筛选过滤掉不符合要求、 不 完整及重复的冗余数据， 并解析筛选的每笔医疗数据中的患病日期。 具体地说 ， 所述信息获取模块 101根据通用的日期格式从每笔医疗数据中解析出每笔医疗 数据的患病日期。 例如， 以" 2012-03-08"或" 2012.03.08"作为日期格式解析出每笔 医疗数据中的患病日期。
[0035] 所述信息关联模块 102用于从所述天气信息平台 5获取每笔医疗数据中的患病曰 期所对应的历史天气信息， 并将每笔医疗数据与所对应的历史天气信息关联。 具体地说， 所述天气信息平台 5提供 API接口， 接入该 API接口的设备或系统都可 以从所述天气信息平台 5中获取历史天气信息。 所述信息关联模块 102调用所述 天气信息平台 5提供的 API接口以获取历史天气信息。 所述信息关联模块 102调用 所述天气信息平台 5提供的 API接口并发送所述患病日期给天气信息平台 5， 天气 信息平台 5以所述患病日期为关键字， 检索所述患病日期对应的历史天气信息以 回传给所述云服务器 1。
[0036] 所述数据分析模块 103用于对所述历史天气信息所关联的医疗数据进行分析以 获得受天气因素影响的患者。 在本实施例中， 所述对天气信息所关联的医疗数 据进行分析以获得受天气因素影响的患者的方式如下： （1) 数据分析模块 103 将历史天气信息所关联的医疗数据进行分类得到每种历史天气信息对应的疾病 名称及每种疾病名称的数量。 举例而言， 大雪的天气情况下， 所述医疗数据中 有四种疾病， 分别为感冒、 发烧、 哮喘及骨折， 其中， 感冒有五十八笔、 发烧 有二十笔、 哮喘有十五笔及骨折有二十七笔； （2) 数据分析模块 103根据每种 疾病名称的数量计算该疾病受天气因素影响的权重。 为了简化起见， 在本实施 例所述计算的权重等于每种疾病名称的数量， 也就是说， 假如哮喘的数量十五 笔， 则哮喘受天气因素影响的权重为十五； （3) 当所述权重超过预设值吋， 数 据分析模块 103确定该疾病受天气因素影响。 在本实施例中， 若所述权重超过预 设值 （例如哮喘的数量为 15， 预设值为 10) ， 数据分析模块 103则认定哮喘受天 气因素影响； （4) 数据分析模块 103根据受天气因素影响的疾病名称在医疗数 据中检索出受天气因素影响的患者， 例如， 以"哮喘"作为关键字， 在医疗数据中 检索出患哮喘的患者。
[0037] 所述疾病预测模块 104用于从所述天气信息平台 5获取天气预报信息。 所述天气 预报信息包括未来预设吋间段内的天气信息， 例如， 未来一周的天气信息。 所 述疾病预测模块 104还用于根据所述天气预报信息判断所述受天气因素影响的患 者受天气影响程度是否大于疾病影响因子。 所述疾病影响因子是指患者受天气 影响的严重程度， 可以用一个数字阈值来进行量化， 例如， 假定疾病影响严重 程度设定的范围为 1至 10之间， 则可设定疾病影响因子为 6。 当患者受天气影响 程度大于 5 (例如 5至 10之间） 吋， 则患者受天气因素影响的影响程度较为严重 ； 当患者受天气影响程度小于 5 (例如 1至 6之间） 吋， 则患者受天气因素影响的 影响程度较为轻微。
[0038] 所述疾病预警模块 105用于当所述患者大于所述疾病影响因子吋， 生成预警信 息并发送 （例如， 根据医疗数据中患者的联系方式进行发送） 给受天气因素影 响的患者所对应的客户终端 4。 所述预警信息包括， 但不限于， 患者名称、 天气 预报信息、 相同天气情况下所患的疾病、 预防所患的疾病的注意事项等。 所述 预警信息可以以文字、 语音及 /或影音的方式发送给客户终端 4。 举例而言， 所述 预警信息为如下文字"尊敬的 XXX， 未来几天将出现寒冷天气， 最低气温降到零 度以下， 以往最低气温降到零度吋， 您出现过哮喘且在第一人民医院及第二人 民医院各就医过一次， 还请注意加强哮喘预防， 以下为预防措施， 请参考： （1 ) 避免到人群聚集； （2) 出门吋， 请携带 N95型号口罩； （3) 注意保暖， 尤其 确保头部、 胸背和足部保暖以免着凉； （4) 注意携带支气管扩张剂、 吸入剂、 雾化剂等药物。 最后， 祝您身体健康！ 。 "
[0039] 参照图 3所示， 是本发明基于医疗大数据的疾病预警方法的优选实施例的流程 图。 在本实施例中， 所述的基于医疗大数据的疾病预警方法应用于云服务器 1， 该方法包括以下步骤：
[0040] 步骤 S31， 信息获取模块 101从所述医院信息系统 2获取医疗信息包， 并将所述 医疗信息包切割成多笔医疗数据。 具体地说， 所述医院信息系统 2提供数据导入 接口 （例如， 应用程序接口， Application Program Interface, API) ， 接入该数据 导入接口的设备或系统都可以从所述医院信息系统 2中获取医疗数据。 所述信息 获取模块 101调用所述医院信息系统 2提供的 API接口以获取信息包， 并将医疗信 息包切割成多笔医疗数据。
[0041] 步骤 S32， 信息获取模块 101解析每笔医疗数据中的患病日期。 本实施例中， 信 息获取模块 101先对每笔医疗数据进行筛选过滤掉不符合要求、 不完整及重复的 冗余数据， 再根据通用的日期格式从筛选后的每笔医疗数据中解析出每笔医疗 数据的患病日期。 例如， 以" 2012-03-08"或" 2012.03.08"作为日期格式解析出每笔 医疗数据中的患病日期。
[0042] 步骤 S33， 信息关联模块 102用于从所述天气信息平台 5获取每笔医疗数据中的 患病日期所对应的历史天气信息， 并将每笔医疗数据与所对应的历史天气信息 关联。 具体地说， 所述天气信息平台 5提供 API接口， 接入该 API接口的设备或系 统都可以从所述天气信息平台 5中获取历史天气信息。 所述信息关联模块 102调 用所述天气信息平台 5提供的 API接口以获取历史天气信息。 所述信息关联模块 1 02调用所述天气信息平台 5提供的 API接口并发送所述患病日期给天气信息平台 5 ， 天气信息平台 5以所述患病日期为关键字， 检索所述患病日期对应的历史天气 信息以回传给所述云服务器 1。
[0043] 步骤 S34， 数据分析模块 103对所述历史天气信息所关联的医疗数据进行分析以 获得受天气因素影响的患者。 在本实施例中， 步骤 S34的具体细化步骤包括步骤 S341至 S344。
[0044] 步骤 S35， 疾病预测模块 104从所述天气信息平台 5获取天气预报信息。 所述天 气预报信息包括未来预设吋间段内的天气信息， 例如， 未来一周的天气信息。
[0045] 步骤 S36， 疾病预测模块 104根据所述天气预报信息判断所述受天气因素影响的 患者受天气影响程度是否大于疾病影响因子。 所述疾病影响因子是指患者受天 气影响的严重程度， 可以用一个数字阈值来进行量化， 例如， 假定疾病影响严 重程度设定的范围为 1至 10之间， 则可设定疾病影响因子为 6。 当患者受天气影 响程度大于 5 (例如 5至 10之间） 吋， 则患者受天气因素影响的影响程度较为严 重； 当患者受天气影响程度小于 5 (例如 1至 6之间） 吋， 则患者受天气因素影响 的影响程度较为轻微。 若所述患者受天气影响程度大于疾病影响因子， 则执行 步骤 S37 ; 若所述患者受天气影响程度小于等于疾病影响因子， 则流程结束。
[0046] 步骤 S37， 疾病预警模块 105生成预警信息并发送 （例如， 根据医疗数据中患者 的联系方式进行发送） 给受天气因素影响的患者所对应的客户终端 4。 所述预警 信息包括， 但不限于， 患者名称、 天气预报信息、 相同天气情况下所患的疾病 、 预防所患的疾病的注意事项等。 所述预警信息可以以文字、 语音及 /或影音的 方式发送给客户终端 4。 举例而言， 所述预警信息为如下文字"尊敬的 XXX， 未 来几天将出现寒冷天气， 最低气温降到零度以下， 以往最低气温降到零度吋， 您出现过哮喘且在第一人民医院及第二人民医院各就医过一次， 还请注意加强 哮喘预防， 以下为预防措施， 请参考： （1) 避免到人群聚集； （2) 出门吋， 请携带 N95型号口罩； （3) 注意保暖， 尤其确保头部、 胸背和足部保暖以免着 凉； （4) 注意携带支气管扩张剂、 吸入剂、 雾化剂等药物。 最后， 祝您身体健 康！ 。 "
[0047] 如图 4所示， 图 4是图 3中步骤 S34的细化流程图。 在本实施例中， 所述步骤 S34 的具体细化步骤包括如下步骤：
[0048] 步骤 S341， 数据分析模块 103将历史天气信息所关联的医疗数据进行分类得到 每种历史天气信息对应的疾病名称及每种疾病名称的数量。 举例而言， 大雪的 天气情况下， 所述医疗数据中有四种疾病， 分别为感冒、 发烧、 哮喘及骨折， 其中， 感冒有五十八笔、 发烧有二十笔、 哮喘有十五笔及骨折有二十七笔。
[0049] 步骤 S342， 数据分析模块 103根据每种疾病名称的数量计算该疾病受天气因素 影响的权重。 为了简化起见， 在本实施例所述权重等于每种疾病名称的数量， 也就是说， 假如哮喘的数量十五笔， 则哮喘受天气因素影响的权重为十五。
[0050] 步骤 S343， 当所述权重超过预设值吋， 数据分析模块 103确定该疾病受天气因 素影响。 在本实施例中， 若所述权重超过预设值 （例如哮喘的数量为 15， 预设 值为 10) ， 数据分析模块 103则认定哮喘受天气因素影响。 [0051] 步骤 S344， 数据分析模块 103根据受天气因素影响的疾病名称在医疗数据中检 索出受天气因素影响的患者， 例如， 以"哮喘"作为关键字， 在医疗数据中检索出 患哮喘的患者。
[0052] 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流程变换， 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
[0053] 本发明所述基于医疗大数据的疾病预警系统及方法采用上述技术方案， 带来的 技术效果为： 能够对医疗大数据与历史天气信息关联， 将对历史天气信息所关 联的医疗大数据分析处理以得到受天气因素影响的患者， 并对所述受天气因素

Claims

权利要求书
[权利要求 1] 一种基于医疗大数据的疾病预警系统， 运行于云服务器中， 所述云服 务器通过通信网络与医院信息系统、 客户终端及天气信息平台连接， 其特征在于， 所述基于医疗大数据的疾病预警系统包括： 信息获取模 块， 用于从所述医院信息系统获取医疗信息包， 将所述医疗信息包切 割成多笔医疗数据， 以及解析每笔医疗数据中的患病日期； 信息关联 模块， 用于从所述天气信息平台获取每笔医疗数据中的患病日期所对 应的历史天气信息， 并将每笔医疗数据与所对应的历史天气信息关联 ； 数据分析模块， 用于对所述历史天气信息所关联的医疗数据进行分 析以获得受天气因素影响的患者； 疾病预测模块， 用于从所述天气信 息平台获取天气预报信息， 根据所述天气预报信息判断所述患者受天 气影响程度是否大于疾病影响因子； 疾病预警模块， 用于当所述患者 大于所述疾病影响因子吋， 生成预警信息并发送给所述患者所对应的 客户终端。
[权利要求 2] 如权利要求 1所述的基于医疗大数据的疾病预警系统， 其特征在于， 所述医疗数据包括患者姓名、 患者年齢、 患病吋间、 疾病名称、 患病 原因、 药品名称、 药品数量、 医生姓名、 就诊科室、 费用以及患者的 联系方式。
[权利要求 3] 如权利要求 1所述的基于医疗大数据的疾病预警系统， 其特征在于， 所述历史天气信息包括地点、 温度、 风向、 天气情况以及空气质量。
[权利要求 4] 如权利要求 1所述的基于医疗大数据的疾病预警系统， 其特征在于， 所述预警信息包括患者名称、 天气预报信息、 相同天气情况下所患的 疾病以及预防所患的疾病的注意事项。
[权利要求 5] 如权利要求 1至 4任一项所述的基于医疗大数据的疾病预警系统， 其特 征在于， 所述数据分析模块还用于： 将历史天气信息所关联的医疗数 据进行分类得到每种历史天气信息对应的疾病名称及每种疾病名称的 数量； 根据每种疾病名称的数量计算该疾病受天气因素影响的权重； 当所述权重超过预设值吋， 则认定该疾病受天气因素影响； 根据受天 气因素影响的疾病名称在医疗数据中检索出受天气因素影响的患者。
[权利要求 6] 一种基于医疗大数据的疾病预警方法， 应用于云服务器中， 所述云服 务器通过网络与医院信息系统、 客户终端及天气信息平台连接， 其特 征在于， 所述基于医疗大数据的疾病预警方法包括步骤： 从所述医院 信息系统获取医疗信息包， 将所述医疗信息包切割成多笔医疗数据； 解析每笔医疗数据中的患病日期； 从所述天气信息平台获取每笔医疗 数据中的患病日期所对应的历史天气信息， 并将每笔医疗数据与所对 应的历史天气信息关联； 对历史天气信息所关联的医疗数据进行分析 以获得受天气因素影响的患者； 从所述天气信息平台获取天气预报信 息； 根据所述天气预报信息判断所述患者受天气影响程度是否大于疾 病影响因子； 当所述患者大于所述疾病影响因子吋， 生成预警信息并 发送给所述患者所对应的客户终端。
[权利要求 7] 如权利要求 6所述的基于医疗大数据的疾病预警方法， 其特征在于， 所述医疗数据包括患者姓名、 患者年齢、 患病吋间、 疾病名称、 患病 原因、 药品名称、 药品数量、 医生姓名、 就诊科室、 费用以及患者的 联系方式。
[权利要求 8] 如权利要求 6所述的基于医疗大数据的疾病预警方法， 其特征在于， 所述历史天气信息包括地点、 温度、 风向、 天气情况以及空气质量。
[权利要求 9] 如权利要求 6所述的基于医疗大数据的疾病预警方法， 其特征在于， 所述预警信息包括患者名称、 天气预报信息、 相同天气情况下所患的 疾病以及预防所患的疾病的注意事项。
[权利要求 10] 如权利要求 6至 9任一项所述的基于医疗大数据的疾病预警方法， 其特 征在于， 所述对历史天气信息所关联的医疗数据进行分析以获得受天 气因素影响的患者的步骤包括如下步骤： 将所述历史天气信息所关联 的医疗数据进行分类得到每种历史天气信息对应的疾病名称及每种疾 病名称的数量； 根据每种疾病名称的数量计算该疾病受天气因素影响 的权重； 当所述权重超过预设值吋， 则认定该疾病受天气因素影响； 根据受天气因素影响的疾病名称在医疗数据中检索出受天气因素影响 的患者。
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