CN113921137A - Health detection and management method based on medical big data - Google Patents
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Abstract
The invention discloses a health detection and management method based on medical big data, which comprises the following steps: the health detection equipment acquires user medical data and establishes data communication with the cloud center server through the mobile intelligent platform; the cloud center server comprises a data processing module, a data analysis module and a data storage module; the data processing module is used for carrying out data cleaning, data specification and data segmentation on the collected data and outputting the processed data; the data analysis module carries out modeling analysis on the processed data, carries out medical data analysis and risk prediction on user individuals, and carries out big data statistics and analysis on user groups; the data storage module stores historical health data of the user and analysis and mining results; the data query module comprises authority-based identity security authentication and information query. The invention has the advantages of effectively improving the quality of the acquired data, protecting the privacy of the user and meeting different application requirements of different types of users on the medical data.
Description
Technical Field
The invention relates to a data management method, in particular to a health detection and management method based on medical big data.
Background
With the rapid development of medical science, life science and information technology, health detection and management based on medical big data are increasingly paid attention by people, and concepts such as precise medicine are generated. Medical information technology is a rapidly developing field. Currently, research on medical information technology mainly focuses on medical information acquisition technology, data acquisition process, information storage platform, information management technology, and the like, and focuses on efficiency and mode of medical data acquisition, storage, analysis, and management.
In the prior art, for example, a chinese patent with publication number CN103905549B discloses a health management system and method based on internet of things and cloud computing, which includes at least one health detection device, a mobile intelligent platform, a cloud center server, at least one doctor working platform, at least one family inquiry platform, and at least one client, where the client includes an emergency center client and an insurance client, the health detection device is connected with the mobile intelligent platform through a communication protocol, the mobile intelligent platform is connected with the cloud center server through a communication network, and the family inquiry platform, the doctor working platform, the emergency center client, and the insurance client are respectively connected with the cloud center server. The invention takes 'user-family-doctor' as a main participant, provides services such as health data examination, recording, visual display, data analysis and mining, doctor guidance, customized information push, positioning service, falling detection and early warning and the like for the user, also provides health service for pregnant women and infants, and provides monitoring service for endowment insurance and medical insurance.
In the prior art, for example, a chinese patent with publication number CN106997421B discloses an intelligent system for personalized medical information collection and health monitoring, which is characterized by comprising: the unified data processing platform is used for being responsible for unified analysis and scheduling of data; the system comprises a plurality of common user ends, wherein the common user ends transmit information of external health monitoring hardware through interfaces of the common user ends, and the personalized medical information acquisition and health monitoring system performs data sharing with a hospital information management system through the common user ends and information sharing interfaces in a data processing platform. An intelligent method for personalized medical information acquisition and health monitoring based on the system is also provided.
Disclosure of Invention
The invention provides a health detection and management method which can effectively improve the quality of acquired data, better analyze the data and meet different application requirements of users/family members-doctors-researchers on medical data while protecting the privacy of the users. It includes: the health detection equipment monitors and collects related health data of a common user in real time, and transmits the data to the cloud center server through the mobile intelligent platform after the health detection equipment collects medical data; the cloud center server performs data cleaning on the acquired medical data, and cleans abnormal data through a clustering algorithm to obtain cleaned data; storing the cleaned data in a user historical health database of a data storage module; performing data specification and data segmentation on the cleaned data, and outputting the processed data; modeling analysis is carried out on the processed data, and medical data analysis and risk prediction are carried out on individual users by combining historical health data of the users in the data storage module; storing the individual medical data and the analysis and risk prediction results of the user in a user analysis result database of a data storage module; carrying out big data statistics on historical health data of the user, and analyzing the health condition of a user group through modeling to obtain the health statistics and analysis results of the user group; storing the health statistics and analysis results of the user group in a group analysis result database of a data storage module; the method comprises the steps that all users are granted different inquiry authorities through identity security authentication, common users and user relatives are granted three-level authorities, doctor users are granted two-level authorities, and researcher users are granted one-level authorities; the users granted different query authorities can access the data storage module to perform authority-based data query.
The invention provides a health management system and a method based on big data, which take a user/family member-doctor-researcher as a participant, acquire medical data in real time through health detection equipment and establish data communication with a cloud center server through a mobile intelligent platform; considering that the data storage and data analysis are burdened by the fact that the real-time acquired data amount is huge and the data noise is excessive, the data quality can be enhanced after the acquired data are processed through the data processing module, data planning is reduced, preprocessing is performed for deep data analysis, medical data analysis and risk prediction are provided for users through the input data analysis module, and big data statistics and analysis are performed on user groups; the data query module provides different query results for users with different authorities through the authority-based identity security authentication, so that the privacy of the users is protected.
The method for the data processing module to carry out data cleaning on the collected data comprises the following steps: preliminary clustering of all data using k-means clustering algorithm generates cluster set C (C)1,C2,C3…Ck-1,Ck) The generated cluster is then re-optimized by minimizing cluster relevance loss: cost is Log (∑ S)i∈k∑j∈k,i≠jF(Ci,Cj))，
Because the data acquired by the data acquisition module has a plurality of quality problems, including redundancy, heterogeneity, inconsistency, incompleteness and the like, the clustering algorithm of the invention focuses on the classification of the acquired medical data into numerical data, such as heart rate values, body fat rates, body weight values, blood pressure values, blood sugar values and the like, and non-numerical data, such as user gender, tumor types, disease types and the like; by grouping similar data together to identify closely related data types and exclude erroneous data
The data query based on the authority is characterized in that: the ordinary users and the relative of the users can inquire the personal related information of the users by granting the third-level authority, the doctors can inquire all the related user information by granting the second-level information authority, the researchers can inquire all the anonymization information of the medical data by granting the first-level authority, and the users with different access authorities can acquire the related information by accessing the data storage module after completing identity security authentication.
The user personal related information comprises all the medical data of the user in the user historical health database, and the medical data analysis and risk prediction results of the user in the user analysis result database.
The doctor grants the secondary information authority to inquire all the associated user information, which is characterized in that: and establishing an associated user information list for the doctor who grants the secondary information authority, wherein the doctor can access personal related information of all users in all the associated user information lists.
The method is characterized in that a researcher grants a primary authority to inquire all anonymization information of medical data, and the method comprises the following steps: the medical data anonymization information comprises the result of big data statistics and analysis of the user group in the group analysis result database and the medical data anonymization processed information of all users in the user historical health database.
According to the invention, the data acquisition module is adopted, and comprises a health detection device for acquiring medical data and a mobile intelligent platform for establishing data communication with the cloud center server; the data processing module is used for carrying out data cleaning, data specification and data segmentation on the collected data and outputting the processed data; the data analysis module carries out modeling analysis on the processed data, carries out medical data analysis and risk prediction on user individuals, and carries out big data statistics and analysis on user groups; the data storage module stores historical health data of the user and analysis and mining results; the data query module comprises authority-based identity security authentication and information query. Therefore, the invention has the advantages of effectively improving the quality of the acquired data, better performing data analysis, protecting the privacy of the user and meeting different application requirements of the user and family members, doctors and researchers on the medical data.
Drawings
FIG. 1 is a flow chart of a health detection and management method based on medical big data according to the present invention;
FIG. 2 is a timing diagram of a method according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating a clustering algorithm comparing indexes related to numerical data according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating a clustering algorithm comparing non-numeric data correlation indicators according to an embodiment of the present invention;
fig. 5 is a schematic diagram illustrating a scenario according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples.
Example 1:
referring to fig. 1 to 3 and 5 of the present embodiment, a health detection and management method based on medical big data of the present embodiment includes that health detection equipment monitors and acquires relevant health data of a general user in real time, and the health detection equipment transmits the data to a cloud center server through a mobile intelligent platform after acquiring the medical data; the cloud center server performs data cleaning on the acquired medical data, and cleans abnormal data through a clustering algorithm to obtain cleaned data; storing the cleaned data in a user historical health database of a data storage module; performing data specification and data segmentation on the cleaned data, and outputting the processed data; modeling analysis is carried out on the processed data, and medical data analysis and risk prediction are carried out on individual users by combining historical health data of the users in the data storage module; storing the individual medical data and the analysis and risk prediction results of the user in a user analysis result database of a data storage module; carrying out big data statistics on historical health data of the user, and analyzing the health condition of a user group through modeling to obtain the health statistics and analysis results of the user group; storing the health statistics and analysis results of the user group in a group analysis result database of a data storage module; the method comprises the steps that all users are granted different inquiry authorities through identity security authentication, common users and user relatives are granted three-level authorities, doctor users are granted two-level authorities, and researcher users are granted one-level authorities; the users granted different query authorities can access the data storage module to perform authority-based data query.
The invention provides a health management system and a method based on big data, which take a user/family member-doctor-researcher as a participant, acquire medical data in real time through health detection equipment and establish data communication with a cloud center server through a mobile intelligent platform; considering that the data storage and data analysis are burdened by the fact that the real-time acquired data amount is huge and the data noise is excessive, the data quality can be enhanced after the acquired data are processed through the data processing module, data planning is reduced, preprocessing is performed for deep data analysis, medical data analysis and risk prediction are provided for users through the input data analysis module, and big data statistics and analysis are performed on user groups; the data query module provides different query results for users with different authorities through the authority-based identity security authentication, so that the privacy of the users is protected.
The method for the data processing module to carry out data cleaning on the collected data comprises the following steps: preliminary clustering of all data using k-means clustering algorithm generates cluster set C (C)1,C2,C3…Ck-1,Ck) The generated cluster is then re-optimized by cluster relevance loss: cost is Log (∑ S)i∈k∑j∈k,i≠jF(Ci,Cj))，
Because the data acquired by the data acquisition module has a plurality of quality problems, including redundancy, heterogeneity, inconsistency, incompleteness and the like, the clustering algorithm of the invention focuses on the classification of the acquired medical data into numerical data, such as blood pressure values, blood glucose values and the like, and non-numerical data, such as user gender, tumor types and the like; by grouping similar data together to identify closely related data types and exclude erroneous data.
Fig. 3 and fig. 4 show that, compared with the effect of the clustering algorithm of numerical data and non-numerical data in the prior art, F-scores and landed indexes are common evaluation indexes of the clustering algorithm, the higher the numerical value is, the more accurate the clustering algorithm is, the more effective the overall data quality can be improved, and control (Entropy value) indicates the overall clustering quality by measuring the distribution of the clustering centers, and the lower the Entropy value is, the better the clustering quality is. It can be seen from fig. 3 and 4 that the clustering quality of the present example is higher than that of the prior art for both the numerical class data and the non-numerical class data.
The data query based on the authority is characterized in that: the ordinary users and the relative of the users can inquire the personal related information of the users by granting the third-level authority, the doctors can inquire all the related user information by granting the second-level information authority, the researchers can inquire all the anonymization information of the medical data by granting the first-level authority, and the users with different access authorities can acquire the related information by accessing the data storage module after completing identity security authentication.
The user personal related information comprises all the medical data of the user in the user historical health database, and the medical data analysis and risk prediction results of the user in the user analysis result database.
The doctor grants the secondary information authority to inquire all the associated user information, which is characterized in that: and establishing an associated user information list for the doctor who grants the secondary information authority, wherein the doctor can access personal related information of all users in all the associated user information lists.
The method is characterized in that a researcher grants a primary authority to inquire all anonymization information of medical data, and the method comprises the following steps: the medical data anonymization information comprises the result of big data statistics and analysis of the user group in the group analysis result database and the medical data anonymization processed information of all users in the user historical health database.
After the users with different authorities are authenticated by the existing identity security authentication technology, the users with different authorities can access the information in the data storage module corresponding to the authorities. The invention provides the researcher authorization on the basis of the traditional technology, a medical researcher with a primary authority can inquire all anonymized data statistical results, different inquiry results are provided for users with different authorities, the requirements of different types of users are met, only personal related information needs to be accessed for common users and user relatives, all the related user information needs to be accessed for doctor users, all the medical information needs to be accessed for the medical researcher, and the anonymization processing is carried out on the medical information which can be accessed by the researcher in consideration of the requirement of protecting the privacy of the users.
Example 2:
this example further illustrates the technical solution based on example 1.
The whole health detection and management method based on medical big data comprises the following processes:
s10, the health detection equipment monitors and collects medical data related to the common user in real time, including but not limited to the heart rate value, the body fat rate, the body weight value, the blood pressure value, the blood sugar value, the sex, the tumor type, the disease type and the like of the user;
s20, the health detection equipment acquires medical data and transmits the data to the cloud center server through the mobile intelligent platform for data cleaning;
s21, the collected medical data is stored in the user historical health data of the data storage module after being subjected to data cleaning;
s30, further processing the data after the data cleaning, wherein the data processing can adopt the existing data reduction algorithm and data segmentation algorithm;
s31, the data after data processing is combined with the historical health data of the user in the data storage module to further analyze to obtain the health condition analysis and personal disease risk prediction results of the user individual;
s32, storing the health condition analysis results of the user individuals into a data storage module;
s40, big data statistics and analysis can be carried out on the health condition of the user group according to the historical health data of all users in the data storage module;
s41, storing the group health condition statistics and analysis results into a data storage module;
s50, all users pass identity security authentication before inquiring health data, ordinary users and user relatives passing identity security authentication are granted with three-level authority, doctors passing identity security authentication are granted with two-level authority, and researchers passing identity security authentication are granted with one-level authority;
s51, the user who obtains the corresponding authority through the identity security authentication can access the data storage module to inquire the data, the third-level authority can inquire the personal related information of the user, the second-level authority can inquire all the related user information, and the first-level authority can inquire all the anonymization information of the medical data.
While the invention has been described in connection with preferred embodiments, the invention is not limited by this description. Various modifications, substitutions, and alterations may be made to the objects set forth herein by those skilled in the art without departing from the spirit and scope of the invention. The scope of the invention should be determined from the following claims.
Claims (9)
1. A health detection and management based on medical big data is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
the health detection equipment monitors and collects related health data of a common user in real time, and transmits the data to the cloud center server through the mobile intelligent platform after the health detection equipment collects medical data;
the cloud center server performs data cleaning on the acquired medical data, and cleans abnormal data through a clustering algorithm to obtain cleaned data;
storing the cleaned data in a user historical health database of a data storage module;
performing data specification and data segmentation on the cleaned data, and outputting the processed data;
modeling analysis is carried out on the processed data, and medical data analysis and risk prediction are carried out on the user individuals by combining historical health data of the users in the data storage module;
storing the user individual medical data and the analysis and risk prediction results in a user analysis result database of a data storage module;
carrying out big data statistics on the historical health data of the user, and analyzing the health condition of a user group through modeling to obtain the health statistics and analysis results of the user group;
storing the health statistics and analysis results of the user group in a group analysis result database of a data storage module;
the method comprises the steps that all users are granted different inquiry authorities through identity security authentication, common users and user relatives are granted three-level authorities, doctor users are granted two-level authorities, and researcher users are granted one-level authorities;
the user granted with different inquiry authorities can access the data storage module to perform authority-based data inquiry.
2. The health detection and management method based on medical big data as claimed in claim 1, wherein: the method for the data processing module to carry out data cleaning on the collected data comprises the following steps: all data were clustered using k-means clustering algorithmThe preliminary clustering generates a cluster set C (C)1，C2，C3…Ck-1，Ck) The generated cluster is then re-optimized by minimizing cluster relevance loss: cost is Log (∑ S)i∈k∑j∈k，i≠jF(Ci，Cj))，
Where k is a custom integer parameter, ti∈CiRepresentation set CiNumerical data of inner, ni∈CiRepresenting non-numerical data, Simt (t), within the set C _ ii，tj) Representing numerical data tiAnd tjSimilarity of (2), Simn (n)i，nj) Representing non-numerical data niAnd njThe numerical similarity can be calculated by the existing numerical distance algorithm, the non-numerical data can be calculated by the existing text similarity algorithm, | CiI denotes cluster CiThe total number of intra data;
and finishing data cleaning based on the clustering center after the clustering group is generated by optimization.
3. The health detection and management method based on medical big data as claimed in claim 1, wherein: the data query based on the authority is characterized in that: the ordinary users and the relative of the users can inquire the personal related information of the users by granting the third-level authority, the doctors can inquire all the related user information by granting the second-level information authority, the researchers can inquire all the anonymization information of the medical data by granting the first-level authority, and the users with different access authorities can access the data storage module to acquire the related information after completing the identity security authentication.
4. The health detection and management method based on medical big data as claimed in claim 3, wherein: the method is characterized in that the user and the relative of the user can be granted three-level authority to inquire the personal related information of the user, and the method is characterized in that: the user personal related information comprises all personal medical data of the user in the user historical health database, and the personal medical data analysis and risk prediction results of the user in the user analysis result database.
5. The health detection and management method based on medical big data as claimed in claim 3, wherein: the doctor grants the secondary information authority to inquire all the associated user information, which is characterized in that: and establishing an associated user information list for the doctor granting the secondary information authority, wherein the doctor can access personal related information of all users in all the associated user information lists, the personal related information of the users comprises all personal medical data of the users in the user historical health database, and the personal medical data analysis and risk prediction results of the users in the user analysis result database.
6. The health detection and management method based on medical big data as claimed in claim 3, wherein: the researcher can inquire all medical data anonymization information by granting primary authority, and the method is characterized in that: the medical data anonymization information comprises the result of big data statistics and analysis of the user group in the group analysis result database and the medical data anonymization processed information of all users in the user historical health database.
7. A computer program for implementing the method of claim 1.
8. A storage medium storing a computer program according to claim 1.
9. A terminal device equipped with the computer program according to claim 8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115292391A (en) * | 2022-10-10 | 2022-11-04 | 山东宝德龙健身器材有限公司 | Rehabilitation training data analysis management system |
| CN115359906A (en) * | 2022-08-22 | 2022-11-18 | 温州城市智慧健康有限公司 | Intelligent health service system based on health big data |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103905549A (en) * | 2014-03-28 | 2014-07-02 | 成都悦图科技有限公司 | System and method for health management based on internet of things and cloud computing |
| CN104000562A (en) * | 2014-06-09 | 2014-08-27 | 深圳市中兴移动通信有限公司 | Health reminding system, health reminding method and health reminding device |
| CN106202405A (en) * | 2016-07-11 | 2016-12-07 | 中国人民大学 | A kind of compactedness Text Extraction based on text similarity relation |
| CN106997421A (en) * | 2016-01-25 | 2017-08-01 | 清华大学 | Personalized medicine information gathering and the intelligence system and method for health monitoring |
| CN109411088A (en) * | 2018-05-03 | 2019-03-01 | 广东健凯医疗有限公司 | A kind of user health big data shared platform |
| WO2021120588A1 (en) * | 2020-06-17 | 2021-06-24 | 平安科技（深圳）有限公司 | Method and apparatus for language generation, computer device, and storage medium |
-
2021
- 2021-10-15 CN CN202111202076.9A patent/CN113921137A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103905549A (en) * | 2014-03-28 | 2014-07-02 | 成都悦图科技有限公司 | System and method for health management based on internet of things and cloud computing |
| CN104000562A (en) * | 2014-06-09 | 2014-08-27 | 深圳市中兴移动通信有限公司 | Health reminding system, health reminding method and health reminding device |
| CN106997421A (en) * | 2016-01-25 | 2017-08-01 | 清华大学 | Personalized medicine information gathering and the intelligence system and method for health monitoring |
| CN106202405A (en) * | 2016-07-11 | 2016-12-07 | 中国人民大学 | A kind of compactedness Text Extraction based on text similarity relation |
| CN109411088A (en) * | 2018-05-03 | 2019-03-01 | 广东健凯医疗有限公司 | A kind of user health big data shared platform |
| WO2021120588A1 (en) * | 2020-06-17 | 2021-06-24 | 平安科技（深圳）有限公司 | Method and apparatus for language generation, computer device, and storage medium |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115359906A (en) * | 2022-08-22 | 2022-11-18 | 温州城市智慧健康有限公司 | Intelligent health service system based on health big data |
| CN115292391A (en) * | 2022-10-10 | 2022-11-04 | 山东宝德龙健身器材有限公司 | Rehabilitation training data analysis management system |
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