A user data management system

Abstract

The application discloses a kind of user data management systems.The system includes: a plurality of user terminals and server, and the user terminal and server establish network communication;The server is used to manage the medical data uploaded by user terminal;The server includes: data modeling module, data encryption module, data analysis module, data sharing module;The data modeling module is used to periodically collect the medical data uploaded by user terminal, and medical data is modeled after being summarized;The data encryption module is used to create access permission for user terminal, and the medical data after modeling is re-encrypted;The data analysis module is used to analyze the medical data uploaded by user terminal;The data sharing module is used to share medical data to other data centers.Through the above scheme, the security management and analysis of user data are realized, and it is convenient for users to query.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of digital management, and in particular to a user data management system. BACKGROUND

[0002] With the promotion of digital medical treatment, more and more internet medical platforms begin to focus on internet medical services, digital marketing services, academic promotion and content popularization. In order to widely collect case information, medical platforms begin to collect medical information of patients, such as examination reports and historical diagnosis and treatment records of patients. Through the collection of medical data of users, medical students can learn and conduct academic research on some cases, and relevant patients can understand the disease.

[0003] However, as more and more medical data are uploaded to the platform, there is a risk of user privacy leakage while facilitating our daily life. Meanwhile, storing a large amount of invalid data brings a considerable burden to the server, and the data development degree is low due to the lack of effective analysis of the data. In the face of these huge collected data, there is a lack of an effective management method. SUMMARY

[0004] The present application provides a user data management system to solve the problems of insufficient user privacy protection, redundant invalid data, insufficient data development, etc.

[0005] The present application adopts the following scheme: a user data management system, characterized in that it comprises: a plurality of user terminals and a server, the user terminals and the server establish network communication; the server is used for managing medical data uploaded by the user terminals;

[0006] The server comprises a data modeling module, a data encryption module, a data analysis module and a data sharing module.

[0007] The data modeling module is used for regularly collecting medical data uploaded by the user terminals, and performing dimension modeling after the medical data are summarized.

[0008] The data encryption module is used for creating access permissions for the user terminals, and re-encrypting the modeled medical data.

[0009] The data analysis module is used for analyzing the medical data uploaded by the user terminals.

[0010] The data sharing module is used for sharing the medical data with other data centers.

[0011] The dimension modeling comprises:

[0012] The server pre-processes the medical data uploaded by all user terminals, obtains different medical services according to different query requirements, and constructs fact tables and dimension tables for each different medical service;

[0013] The medical service includes multiple sub-items and multiple attributes, and one sub-item corresponds to one measurement value;

[0014] The sub-item in the medical service is used as the granularity of the fact table, the attribute in the medical process is used as the dimension table connected with the fact table, and the measurement value of the medical process is used as the fact in the fact table;

[0015] After the fact table and the dimension table of the medical data are constructed, the redundant data is removed.

[0016] The dimension modeling further includes:

[0017] After the modeling is completed, the data modeling module cleans the data that is not utilized;

[0018] When new medical data is uploaded to the server through the user terminal, the data modeling module updates the data by adding the fact table and the dimension table;

[0019] When there is a new query requirement, the data modeling module collects the medical data of the user terminal and establishes new fact tables and dimension tables.

[0020] The re-encryption of the modeled medical data includes:

[0021] An encrypted channel is established between the client and the server through the SSL / TLS protocol to encrypt the data communicated between the two parties; each different medical service is encrypted to form an encrypted block, and the user sends a query requirement to the server through the user terminal; the server provides different access permissions for different user terminals, and according to the query request of the user terminal, finds the corresponding encrypted block, judges whether the encrypted block is within the access permission range of the user terminal, if it is within the access permission range, the encrypted block is sent to the user terminal for decryption; if it is not within the access permission range, the user terminal is fed back that there is no access permission.

[0022] The data analysis module uses a factor analysis method to analyze the medical data uploaded by the user terminal, including:

[0023] Multiple groups of medical data with the same type of disease are summarized, one medical data includes one disease data and multiple related data, and the multiple groups of medical data are standardized to make the different related data contained in each group of medical data have the same scale;

[0024] After standardization, a plurality of common factors and special factors are selected; the common factors refer to relevant data shared by a plurality of groups of medical data, and the special factors refer to relevant data specific to each group of medical data;

[0025] Pearson correlation coefficients are respectively constructed for each common factor and the disease data, if the absolute value of the Pearson correlation coefficient is greater than 0.6, the common factor is retained; if less than 0.6, the corresponding common factor is deleted.

[0026] According to the relationship between each retained common factor and the disease, each retained common factor is given an actual explanation.

[0027] The Pearson correlation coefficient is constructed, comprising:

[0028]

[0029] Wherein, n groups of disease data with the same type of disease are summarized, n represents the number of groups, x i , y i represent the data corresponding to any common factor of the ith group of case data respectively, X, Y represent the set of x i , y i respectively, the left side of the equation represents the Pearson correlation coefficient, the numerator part of the right side of the equation represents the covariance of x i and y i , and the denominator part represents the standard deviation of x i and y i , x and y are the mean values of X and Y respectively.

[0030] The data sharing module comprises:

[0031] The data flow channel between the server and other data centers is established, and data exchange is carried out, the data flow channel comprises a dedicated network, a leased network and a virtual private network;

[0032] The data exchange method comprises: electronic or digital file transmission, data exchange through a portable storage device, data exchange through an email, database sharing or database transaction information exchange, and network or cloud service-based data exchange.

[0033] Compared with the prior art, the beneficial effects of the present application are:

[0034] The technical scheme of the embodiment of the present application loads a data modeling module, a data encryption module, a data analysis module and a data sharing module in the server, uses mathematical modeling and mathematical analysis methods to maximize the use of data, encrypts the medical data to prevent user privacy from being leaked, and finally establishes sharing with other data centers, thereby solving the problems of insufficient user privacy protection, excessive invalid data and insufficient data development.

[0035] It is to be understood that the details described in this section are not intended to identify key or critical elements of the embodiments of the application or to limit the scope of the application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort based on these drawings.

[0037] Figure 1 A structural schematic diagram of a user data management system provided for the embodiment one of the present application. DETAILED DESCRIPTION

[0038] The present application will be further described below in conjunction with the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application. In addition, it should be noted that, for the convenience of description, only the parts related to the present application are shown in the drawings, but not all the structures.

[0039] Embodiment one

[0040] Figure 1 A structural schematic diagram of a user data management system provided for the embodiment one of the present application. The present embodiment can be applied to the case of managing user medical data. As shown in the figure, the system specifically comprises: a plurality of user terminals and a server, the user terminals and the server establish network communication; the server is used for managing the medical data uploaded by the user terminals, and the server comprises: a data modeling module 10, a data encryption module 20, a data analysis module 30, and a data sharing module 40. Figure 1

[0041] Specifically, the user of the user terminal can be a patient or a doctor, and the user terminal can collect and record relevant data in the server according to the medical records or examination records of the patient; the user terminal can also collect the disease sharing uploaded by the doctor, and record in the server terminal after confirming the accuracy and effectiveness of the data.

[0042] The server terminal stores a large amount of medical data, which can be queried by the user terminal, such as: the recovery period of a certain disease, the number of patients with a certain disease in a certain area, the number of users of a certain drug, etc.

[0043] The data modeling module 10 is used for periodically collecting the medical data uploaded by the user terminals, and performing dimension modeling after the medical data is summarized. ​

[0044] It should be noted that dimensional modeling is a data modeling method, the main goal of modeling is to improve data retrieval efficiency, improve data query efficiency, reduce the associated query. Dimensional model is mainly composed of two basic elements of fact table and dimension table.

[0045] The dimension table is the entrance to the fact table, and the rich dimension properties give the analysis cutting ability of the fact table. The dimension table can be regarded as a window for users to analyze data. The dimension table contains the characteristics of the fact records in the fact data table. Some characteristics provide descriptive information, and some characteristics specify how to aggregate fact data table data to provide useful information for analysts. The dimension table contains a hierarchical structure of features that help aggregate data. For example, for the fact of prescribing a drug for a certain disease, the dimension table can include patient, drug, medical device, and other related dimensions.

[0046] The fact table is a short name for the fact data table, which contains a large amount of data, and these data can be aggregated and recorded. For example: patient age, gender, drug price and other data.

[0047] The data in the dimensional model is either dimension or fact. The purpose of dimensional modeling is to facilitate user data query.

[0048] Dimensional modeling includes:

[0049] The server preprocesses all user-end uploaded medical data, and according to different query requirements, obtains different medical services, and constructs fact tables and dimension tables for each different medical service;

[0050] Medical services include multiple sub-items and multiple attributes, and each sub-item corresponds to a measurement value;

[0051] The sub-item in the medical service is the granularity of the fact table, the attribute in the medical process is the dimension table connected with the fact table, and the measurement value of the medical process is the fact in the fact table;

[0052] After constructing the fact table and the dimension table of the medical data, the redundant data is removed.

[0053] Dimensional modeling also includes:

[0054] After modeling is completed, the data modeling module cleans up the data that is not used;

[0055] When new medical data is uploaded to the server through the user end, the data modeling module updates the data by adding fact tables and dimension tables;

[0056] When there is a new query requirement, the data modeling module collects the medical data of the user end and establishes new fact tables and dimension tables.

[0057] The data preprocessing can adopt data standardization processing, the purpose is to eliminate the difference between the characteristics, facilitate the characteristics of the heart and mind to learn weight, improve the generalization ability of the model. Through standardization processing, different characteristic variables can have the same scale.

[0058] Taking the patient's query of the recovery period of a certain disease as an example: the patient inputs the query of the recovery period of a certain disease on the user end, the corresponding medical business is the recovery period of a certain disease, and the recovery period of the medical data of a plurality of patients with the disease is counted, the granularity can refer to the recovery period in the medical data, the dimension can include the patient and the drug, the patient is further divided into the next level dimension: age, gender, medical history, blood sugar, blood fat, blood pressure, height, weight and other related data, the patient is further divided into the next level dimension: name, price, brand manufacturer and other related data, and the fact refers to the specific numerical range of the recovery period.

[0059] In addition, a plurality of medical facts can be integrated in a fact table, various subitems in the medical record of the patient are collectively taken as the granularity, and the values of the corresponding subitems are taken as the fact. For example, taking the doctor's query of the physical indicators of a patient with a certain disease as an example: the doctor inputs the query of the physical indicators of a patient with a certain disease on the user end, the corresponding medical business is the physical indicators of a patient with a certain disease, and the medical data of a plurality of patients with the disease is counted, the granularity can refer to the blood sugar, blood fat, blood pressure and other data of each patient, the dimension can include the patient and the drug, the patient is further divided into the next level dimension: age, gender, height, weight, medical history and other related data, the patient is further divided into the next level dimension: name, price, brand manufacturer and other related data, and the fact refers to the numerical value of the blood sugar, blood fat, blood pressure and other data.

[0060] The granularity, dimension and fact can be adjusted.

[0061] It should be noted that the granularity in the fact table is the business definition of the measurement event that generates the fact. The granularity is the degree of data statistics under the same dimension. The database granularity refers to the level of data refinement or integration in the data unit of the data warehouse. The higher the refinement degree, the smaller the granularity level; on the contrary, the lower the refinement degree, the larger the granularity level. All measurement values of the fact table must have the same granularity.

[0062] The dimension is a set of attributes, which is used to provide information about the fact data in one or more multidimensional data sets.

[0063] The fact refers to the measurement based on a certain business event behavior, which is an index that cannot be further divided in the business definition, and has a clear business meaning, such as blood pressure value, blood sugar value, etc.

[0064] The data encryption module 20 is used for creating access permission for the user end and re-encrypting the medical data after modeling.

[0065] The re-encryption of the modeled medical data includes:

[0066] An encrypted channel is established between the client and the server through the SSL / TLS protocol to encrypt the data communicated between the two parties; each different medical service is encrypted to form an encrypted block, and the user sends a query requirement to the server through the user end; the server provides different access permissions for different user ends, and according to the query request of the user end, finds the corresponding encrypted block, judges whether the encrypted block is within the access permission range of the user end, if it is within the access permission range, the encrypted block is sent to the user end for decryption; if it is not within the access permission range, the user end is fed back that there is no access permission.

[0067] The data encryption module is used for encrypting the medical data in the server to ensure the transmission security of the medical data and the privacy protection of the patient. The encryption module includes a data grouping unit, a data encryption unit and a permission management unit.

[0068] The grouping unit is used for encrypting medical services into encrypted blocks, which can be encrypting one medical service into one encrypted block or encrypting multiple medical services into one encrypted block. For example, all medical services related to a certain case, including the recovery period of the patient with the disease, the number of patients with the disease, etc. are packaged into one encrypted block.

[0069] The data encryption unit is used for converting medical services into encrypted blocks through an encryption function. The user needs to decrypt the corresponding encrypted block through a decryption function to ensure the security of the patient's private data.

[0070] The permission management unit is used for setting different access permissions for different users. For example, the viewing permission range of patients, doctors, hospital institutions and official institutions can be set.

[0071] The data analysis module 30 is used for data analysis of the medical data uploaded by the user end.

[0072] The data analysis module uses the factor analysis method to analyze the medical data uploaded by the user end, including:

[0073] Multiple groups of medical data with the same type of disease are summarized, one medical data contains one disease data and multiple related data, and multiple groups of medical data are standardized to make the different related data contained in each group of medical data have the same scale;

[0074] After standardization, multiple common factors and special factors are selected; the common factor refers to the related data common to multiple groups of medical data, and the special factor refers to the related data specific to each group of medical data;

[0075] The Pearson correlation coefficient is constructed between each common factor and the disease data. If the absolute value of the Pearson correlation coefficient is greater than 0.6, the common factor is retained; if it is less than 0.6, the corresponding common factor is deleted.

[0076] According to the relationship between each retained common factor and the disease, each retained common factor is given an actual explanation.

[0077] For ease of understanding, take a disease data of a disease category containing one common factor and one special factor as an example. In fact, there is one or more common factors and one or more special factors:

[0078] Y i =l i F i +ε i

[0079] Where Y i represents the ith group of disease data, and refers to specific parameter values such as blood glucose values, F i represents the common related data in the ith group of medical data, which can be related data such as blood oxygen content, l i represents the coefficient / load, and ε i represents the unique related data in the ith group of medical data.

[0080] It should be noted that factor analysis is a dimensionality reduction method in unsupervised machine learning, which is a data simplification technique. It aims to describe observable variables with a small number of latent variables or factors, also known as common factor analysis. Through linear combinations of observed variables, the common variation of observed variables is explained, and meaningful explanations of observed variables are made, reducing the number of variables. The basic idea is to group variables according to their correlation, so that variables within the same group have high correlation, but variables in different groups have no correlation or low correlation. Each group of variables represents a basic structure, i.e. a common factor.

[0081] The purpose of standardization in the data analysis module and the data modeling module is the same, which is to eliminate the differences between features.

[0082] The Pearson correlation coefficient is constructed, including:

[0083]

[0084] Where n groups of disease data with the same disease category are summarized, n represents the number of groups, x i , y i represent the data corresponding to any common factor of the ith group of case data, X, Y represent x i , y iThe set of x i covariance of x i and y i , and the denominator part represents the standard deviation of x i and y , respectively.

[0085] By using the method of factor analysis, from the perspective of disease, early prevention of related diseases can be carried out, and the causes of diseases and effective treatment methods can be found, etc. From the perspective of medicine, it can be found that which diseases various medicines have certain curative effect on, and which manufacturer's medicine has better curative effect, etc. In addition, medical staff can refer to the physical conditions of patients with different diseases, various index data, to find the best treatment plan, etc.

[0086] The data sharing module 40 is used to share medical data to other data centers.

[0087] The data sharing module comprises:

[0088] The data flow channel between the server and other data centers is established, and data exchange is carried out, and the data flow channel comprises a private network, a leased network and a virtual private network;

[0089] The data exchange method comprises: electronic or digital file transmission, data exchange through a portable storage device, data exchange through an email, database sharing or database transaction information exchange, and network or cloud service-based data exchange.

[0090] The realization of data sharing can make more people fully use the existing data resources, reduce the repeated labor and corresponding cost of data collection and data acquisition, and focus on data development and research. In general, it can reduce medical costs, enhance medical query ability, improve efficiency, concentrate on data access to reduce duplicate data sets, promote communication, cooperation and contact between medical organizations, and other benefits.

[0091] In addition, the degree of data sharing also reflects the information development level of an area, and the higher the degree of data sharing, the higher the information development level. To realize data sharing, first of all, a unified and legal data exchange standard should be established, and the data format should be standardized, so that users can use the specified data standard as much as possible; secondly, the corresponding data use management method should be established, so as to realize real information sharing.

[0092] The user data management system provided by the embodiment one of the application is internally equipped with a data modeling module, a data encryption module, a data analysis module and a data sharing module, through the method of mathematical modeling and mathematical analysis, the user can conveniently query in the medical field, the medical data is maximized to be used, meanwhile, the medical data is encrypted and protected, the user privacy is prevented from being leaked, finally, the sharing with other data centers is established, and the cooperation between medical groups is strengthened.

[0093] It should be noted that the above only describes the preferred embodiments of the present application and the technical principles applied. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and various obvious changes, re-adjustments and substitutions can be made by those skilled in the art without departing from the scope of the present application. Therefore, although the present application has been described in detail through the above embodiments, the present application is not limited to the above embodiments, and more other equivalent embodiments can be included without departing from the concept of the present application, and the scope of the present application is determined by the scope of the appended claims.

Claims

1. A user data management system, characterized by The application relates to a medical data management system, comprising: a plurality of user terminals and a server, the user terminals and the server establishing network communication; the server is used for managing medical data uploaded by the user terminals; the server comprises a data modeling module, a data encryption module, a data analysis module and a data sharing module; the data modeling module is used for regularly collecting medical data uploaded by the user terminals, and after the medical data are collected, dimension modeling is carried out, the dimension modeling comprising: the server pre-processes all the medical data uploaded by the user terminals, obtains different medical services according to different query requirements, constructs a fact table and a dimension table for each different medical service; the medical service comprises a plurality of sub-items and a plurality of attributes, and one sub-item corresponds to one measurement value; the sub-item in the medical service is used as the granularity of the fact table, the attributes in the medical process are used as the dimension table connected with the fact table, and the measurement value of the medical process is used as the fact in the fact table; after the fact table and the dimension table of the medical data are constructed, redundant data are removed; the dimension modeling further comprises: after the modeling is completed, the data modeling module cleans up unused data; when new medical data are uploaded to the server through the user terminals, the data modeling module updates the data by adding the fact table and the dimension table; when there is a new query requirement, the data modeling module collects the medical data of the user terminals and establishes new fact tables and dimension tables; the data encryption module is used for creating access permissions for the user terminals, and re-encrypting the modeled medical data, the re-encryption of the modeled medical data comprising: an encrypted channel is established between the client and the server through an SSL / TLS protocol, and the data of the two parties is encrypted; each different medical service is encrypted to form an encrypted block, a user sends a query requirement to the server through the user terminal; the server provides different access permissions for different user terminals, and according to the query request of the user terminal, the corresponding encrypted block is found, whether the encrypted block is within the access permission range of the user terminal is judged, if yes, the encrypted block is sent to the user terminal for decryption; if not, the user terminal is fed back that no access permission is available; the data analysis module is used for carrying out data analysis on the medical data uploaded by the user terminals; the data sharing module is used for sharing the medical data to other data centers.

2. The system of claim 1, wherein, the data analysis module adopts a factor analysis method to analyze the medical data uploaded by the user terminals, comprising: a plurality of groups of medical data with the same disease are collected, one medical data comprises one disease data and a plurality of related data, and the plurality of groups of medical data are standardized to make the different related data contained in each group of medical data have the same scale; after the standardization, a plurality of common factors and special factors are selected; the common factor refers to the related data shared by the plurality of groups of medical data, and the special factor refers to the related data specific to each group of medical data; a Pearson correlation coefficient is constructed between each common factor and the disease data, if the absolute value of the Pearson correlation coefficient is greater than 0.6, the common factor is retained; if less than 0.6, the corresponding common factor is deleted. According to the relationship between each remaining common factor and the disease, a practical explanation is given to each remaining common factor.

3. The system of claim 2, wherein, The Pearson correlation coefficient is constructed, including: wherein n groups of disease data with the same type of disease are summarized, n represents the number of groups, , respectively represent the data corresponding to the i-th group of case data and any common factor, X, Y respectively represent , the set, the left side of the equation represents the Pearson correlation coefficient, the numerator of the right side of the equation represents the covariance of and , the denominator represents the standard deviation of and , and are the mean values of X and Y respectively.

4. The system of claim 1, wherein, The data sharing module comprises: The data flow channel between the server and other data centers is established, and data exchange is carried out, and the data flow channel includes a dedicated network, a leased network and a virtual private network. The data exchange method comprises: electronic or digital file transmission, data exchange through a portable storage device, data exchange through an email, database sharing or database transaction information exchange, and network or cloud service based data exchange.

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