User data management system

Abstract

The invention discloses a user data management system. The system comprises a plurality of clients and a server, wherein the clients and the server establish network communication; the server is used for managing the medical data uploaded by the user side; 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 a user side and carrying out dimension modeling after the medical data is summarized; the data encryption module is used for creating access authority for the user side and re-encrypting the modeled medical data; the data analysis module is used for performing data analysis on the medical data uploaded by the user side; and the data sharing module is used for sharing the medical data to other data centers. By means of the scheme, safety management and analysis are conducted on the user data, and a user can conduct query conveniently.

Description

Technical Field

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

[0002] With the promotion of digital medicine, more and more Internet medical platforms have begun to focus on Internet medical services, digital marketing services, academic promotion and content popularization. In order to collect case information extensively, medical platforms have begun to collect patients' medical information, such as patients' examination reports, historical medical records, etc. By collecting these users' medical data, it is convenient for medical students to study and conduct academic research on some cases, and it is also convenient for relevant patients to understand the symptoms.

[0003] However, as more and more medical data are uploaded to the platform, while it facilitates our daily lives, there is also a risk of user privacy leakage. At the same time, storing a large amount of invalid data brings a considerable burden to the server, and the lack of effective analysis of the data leads to a low degree of data development. Faced with these huge amounts of collected data, there is a lack of effective management methods. Summary of the invention

[0004] The present invention 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 invention is implemented by the following scheme: A user data management system, characterized in that it comprises: a plurality of user terminals and a server, wherein the user terminals establish network communication with the server; the server is used to manage the medical data uploaded by the user terminals;

[0006] The server includes: a data modeling module, a data encryption module, a data analysis module, and a data sharing module;

[0007] The data modeling module is used to regularly collect medical data uploaded by the user end and perform dimensional modeling after aggregating the medical data;

[0008] The data encryption module is used to create access rights for the user terminal and re-encrypt the modeled medical data;

[0009] The data analysis module is used to analyze the medical data uploaded by the user;

[0010] The data sharing module is used to share medical data with other data centers.

[0011] The dimensional modeling includes:

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

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

[0014] The sub-items in the medical service are used as the granularity of the fact table, the attributes in the medical process are used as the dimension table connected to the fact table, and the measurement values ​​of the medical process are used as the facts in the fact table;

[0015] After constructing the fact table and dimension table of medical data, remove redundant data.

[0016] The dimensional modeling also includes:

[0017] After the modeling is completed, the data modeling module cleans up the unused data;

[0018] 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;

[0019] When there are new query requirements, the data modeling module collects medical data from the user side and creates new fact tables and dimension tables.

[0020] The re-encrypting 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 by both parties; each different medical service is encrypted to form an encryption block, and the user sends a query request to the server through the user terminal; the server provides different access rights for different user terminals, and searches for the corresponding encryption block based on the query request of the user terminal, and determines whether the encryption block is within the scope of the access right of the user terminal. If it is within the scope of the access right, the encryption block is sent to the user terminal for decryption; if it is not within the scope of the access right, the server feedback that there is no access right is given to the user terminal.

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

[0023] Aggregate multiple sets of medical data with the same type of disease. One piece of medical data contains one piece of disease data and multiple pieces of related data. Standardize multiple sets of medical data so that different related data contained in each set of medical data have the same scale.

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

[0025] The Pearson correlation coefficient was constructed for each common factor and the disease data. If the absolute value of the Pearson correlation coefficient was greater than 0.6, the common factor was retained; if it was less than 0.6, the corresponding common factor was deleted;

[0026] Each common factor that remains is given a practical explanation based on its relationship with the symptoms.

[0027] The construction of the Pearson correlation coefficient includes:

[0028]

[0029] Among them, n groups of disease data with the same disease are summarized, n represents the number of groups, x i ,y i Represents the data corresponding to the case data of group i and any common factor, X and Y represent x i ,y i The left side of the equation represents the Pearson correlation coefficient, and the numerator on the right side of the equation represents x i With y i The covariance of x i With y i Standard deviation, x and y are the means of X and Y respectively.

[0030] The data sharing module comprises:

[0031] Establishing a data flow channel between the server and other data centers and exchanging data, wherein the data flow channel includes a dedicated network, a leased network, and a virtual private network;

[0032] The data exchange methods include: electronic or digital file transmission, data exchange through portable storage devices, data exchange through email, database sharing or database transaction information exchange, and data exchange based on network or cloud services.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The technical solution of the embodiment of the present invention is to install a data modeling module, a data encryption module, a data analysis module and a data sharing module inside the server, and maximize the utilization of data through mathematical modeling and mathematical analysis methods. At the same time, the medical data is encrypted and protected to prevent the user privacy from being leaked. Finally, sharing is established with other data centers, which solves the problems of insufficient user privacy protection, excessive invalid data, insufficient data development, etc.

[0035] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0037] Figure 1 A schematic diagram of the structure of a user data management system provided in Embodiment 1 of the present invention. DETAILED DESCRIPTION

[0038] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0039] Embodiment 1

[0040] Figure 1 This is a schematic diagram of the structure of a user data management system provided by the first embodiment of the present invention. This embodiment is applicable to the situation of managing user medical data. Figure 1 As shown, the system specifically includes: multiple user terminals and servers, and the user terminals establish network communication with the servers; the server is used to manage the medical data uploaded by the user terminals, and the server includes: a data modeling module 10, a data encryption module 20, a data analysis module 30, and a data sharing module 40.

[0041] Specifically, the user of the user terminal can be a patient or a doctor. The user terminal can collect relevant data and enter it into the server based on the patient's medical records or examination records. The user terminal can also collect symptom sharing uploaded by the doctor, and enter it into the server after confirming the accuracy and validity of the data.

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

[0043] The data modeling module 10 is used to regularly collect medical data uploaded by the user end and perform dimensional modeling after the medical data is aggregated.

[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, and reduce associated queries. The dimensional model is mainly composed of two basic elements: fact table and dimension table.

[0045] The dimension table is the entrance to the fact table. The rich dimension attributes provide the ability to analyze and cut 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 table. Some characteristics provide descriptive information, and some characteristics specify how to summarize the fact table data to provide useful information for analysts. The dimension table contains a hierarchical structure of characteristics that help summarize data. For example, for the fact of prescribing medicine for a certain disease, its dimension table can contain relevant dimensions such as patients, drugs, and medical devices.

[0046] Fact table is short for fact data table, which contains a large amount of data, and these data can be summarized and recorded, such as the patient's age, gender, price of medicines, etc.

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

[0048] Dimensional modeling includes:

[0049] The server pre-processes the medical data uploaded by all users, obtains different medical services according to different query requirements, and builds 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 one measurement value;

[0051] The sub-items in the medical business are used as the granularity of the fact table, the attributes in the medical process are used as the dimension table connected to the fact table, and the measurement values ​​of the medical process are used as the facts in the fact table;

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

[0053] Dimensional modeling also includes:

[0054] After the modeling is completed, the data modeling module cleans up the unused data;

[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 are new query requirements, the data modeling module collects medical data from the user side and creates new fact tables and dimension tables.

[0057] Data preprocessing can be done by data standardization, the purpose of which is to eliminate the differences between features, facilitate the features to learn weights wholeheartedly, and improve the generalization ability of the model. Through standardization, different feature variables can have the same scale.

[0058] Take the example of a patient querying the recovery period of a certain disease: the patient enters the query of the recovery period of a certain disease on the user end, and the corresponding medical service is the recovery period of a certain disease. The recovery period in the medical data of multiple patients with the disease is counted. The granularity can refer to the recovery period in the medical data, and the dimension can include both patients and drugs. The patient is further divided into the next level dimension: age, gender, medical history, blood sugar, blood lipids, 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. The fact refers to the specific numerical range of the recovery period.

[0059] In addition, multiple medical facts can be integrated into a fact table, with various sub-items in the patient's medical form as granularity and the corresponding sub-item values ​​as facts. For example, take a doctor's query on the physical indicators of patients with a certain disease as an example: the doctor enters the query on the user side for the physical indicators of patients with a certain disease, and the corresponding medical business is the physical indicators of patients with a certain disease. The medical data of multiple patients with the disease are counted. The granularity can refer to the blood sugar, blood lipids, blood pressure and other data of each patient. The dimension can include both patients and drugs. 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. The fact refers to the value of blood sugar, blood lipids, blood pressure and other data.

[0060] The granularity, dimensions, and determination of facts 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 row. Granularity is the coarseness and fineness of data statistics under the same dimension. Database granularity refers to the level of refinement or integration of data stored in the data unit of the data warehouse. The higher the refinement, the smaller the granularity level; conversely, the lower the refinement, the larger the granularity level. All measurement values ​​in the fact table must have the same granularity.

[0062] A dimension is a collection of attributes that provides information about fact data in one or more multidimensional cubes.

[0063] Facts refer to measurements based on a business event behavior. They are indivisible indicators in the business definition and nouns with clear business meanings, such as blood pressure values, blood sugar values, etc.

[0064] The data encryption module 20 is used to create access rights for the user terminal and re-encrypt the modeled medical data.

[0065] Re-encrypt the modeled medical data, including:

[0066] An encrypted channel is established between the client and the server through the SSL / TLS protocol to encrypt the data communicated by both parties; each different medical service is encrypted to form an encryption block, and the user sends a query request to the server through the user terminal; the server provides different access rights for different user terminals, and searches for the corresponding encryption block based on the query request of the user terminal, and determines whether the encryption block is within the scope of the access right of the user terminal. If it is within the scope of the access right, the encryption block is sent to the user terminal for decryption; if it is not within the scope of the access right, the server feedback that there is no access right is given to the user terminal.

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

[0068] The grouping unit is used to encrypt the medical services into encryption blocks, which may be to encrypt one medical service into one encryption block, or to encrypt multiple medical services into one encryption block. For example, all medical services related to a case, including the recovery period of patients with the disease, the number of patients, and other medical services, are packaged into one encryption block.

[0069] The data encryption unit is used to convert medical services into encrypted blocks through encryption functions. When users call medical services, they need to use decryption functions to decrypt the corresponding encrypted blocks to ensure the security of patient privacy data.

[0070] The authority management unit is used to set different access rights for different users. For example, corresponding viewing authority ranges can be set for patients, doctors, hospital institutions and official institutions.

[0071] The data analysis module 30 is used to analyze the medical data uploaded by the user.

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

[0073] Aggregate multiple sets of medical data with the same type of disease. One piece of medical data contains one piece of disease data and multiple pieces of related data. Standardize multiple sets of medical data so that different related data contained in each set of medical data have the same scale.

[0074] After the standardization process, multiple common factors and special factors are selected; the common factors refer to the relevant data shared by multiple groups of medical data, and the special factors refer to the relevant data unique to each group of medical data;

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

[0076] Each common factor that remains is given a practical explanation based on its relationship with the symptoms.

[0077] For ease of understanding, let's take the case where the data of a class of diseases contains a common factor and a special factor. In fact, there are one or more common factors and one or more special factors:

[0078] Y i = l i F i +ε i

[0079] Among them, Y i Represents the i-th group of disease data, referring to specific parameter values, such as blood sugar values, F i Represents the common related data in the i-th group of medical data, which can be some related data, such as blood oxygen content, etc. i represents the coefficient / load, ε i Represents the relevant data unique to the i-th group of medical data.

[0080] It should be noted that factor analysis is a type of dimensionality reduction in unsupervised machine learning and 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 the linear combination of observed variables, the common variation of observed variables can be explained, and a meaningful explanation of the observed variables can be made to reduce the number of variables. The basic idea is to group variables according to the size of the correlation, so that the correlation between variables in the same group is high, but the variables in different groups are unrelated or have low correlation, and each group of variables represents a basic structure, namely 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] Construct the Pearson correlation coefficient, including:

[0083]

[0084] Among them, n groups of disease data with the same disease are summarized, n represents the number of groups, x i ,y i Represents the data corresponding to the case data of group i and any common factor, X and Y represent x i ,y iThe left side of the equation represents the Pearson correlation coefficient, and the numerator on the right side of the equation represents x i With y i The covariance of x i With y i Standard deviation, and are the means of X and Y respectively.

[0085] By adopting the method of factor analysis, from the perspective of symptoms, we can prevent related symptoms early, find the causes of symptoms, find effective treatments, etc. From the perspective of drugs, we can find out which diseases various drugs have certain effects on, and which manufacturer produces the same drug with better effects, etc. In addition, medical personnel can refer to the physical conditions of patients with different symptoms and various indicator data to find the best treatment plan, etc.

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

[0087] Data sharing module, including:

[0088] Establishing a data flow channel between the server and other data centers and exchanging data, wherein the data flow channel includes a dedicated network, a leased network, and a virtual private network;

[0089] The data exchange methods include: electronic or digital file transmission, data exchange through portable storage devices, data exchange through email, database sharing or database transaction information exchange, and data exchange based on network or cloud services.

[0090] Data sharing can enable more people to make full use of existing data resources, reduce duplication of work and corresponding costs such as information collection and data collection, and focus on data development and research. In general, it can reduce medical costs, enhance medical query capabilities, improve efficiency, centrally access data to reduce duplicate data sets, and promote communication, cooperation and connection between medical organizations.

[0091] In addition, the degree of data sharing also reflects the level of information development in a region. The higher the degree of data sharing, the higher the level of information development. To achieve data sharing, first, a set of unified and legal data exchange standards should be established to standardize data formats so that users can adopt the prescribed data standards as much as possible; second, corresponding data use management methods should be established to achieve true information sharing.

[0092] A user data management system provided in the first embodiment of the present invention is equipped with a data modeling module, a data encryption module, a data analysis module and a data sharing module inside the server. Through mathematical modeling and mathematical analysis methods, it is convenient for users to conduct medical inquiries and maximize the utilization of medical data. At the same time, the medical data is encrypted and protected to prevent the user's privacy from being leaked. Finally, sharing is established with other data centers to strengthen cooperation between medical groups.

[0093] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A user data management system, characterized in that: include: Multiple client terminals and servers, wherein the client terminals establish network communication with the servers; The server is used to manage the medical data uploaded by the user; The server includes: a data modeling module, a data encryption module, a data analysis module, and a data sharing module; The data modeling module is used to regularly collect medical data uploaded by the user end and perform dimensional modeling after aggregating the medical data; The data encryption module is used to create access rights for the user terminal and re-encrypt the modeled medical data; The data analysis module is used to analyze the medical data uploaded by the user; The data sharing module is used to share medical data with other data centers.

2. The system according to claim 1, characterized in that The dimensional modeling includes: The server pre-processes the medical data uploaded by all users, obtains different medical services according to different query requirements, and builds fact tables and dimension tables for each different medical service; The medical service includes multiple sub-items and multiple attributes, and each sub-item corresponds to one measurement value; The sub-items in the medical service are used as the granularity of the fact table, the attributes in the medical process are used as the dimension table connected to the fact table, and the measurement values ​​of the medical process are used as the facts in the fact table; After constructing the fact table and dimension table of medical data, remove redundant data.

3. The system according to claim 2, characterized in that The dimensional modeling also includes: After the modeling is completed, the data modeling module cleans up the unused data; 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; When there are new query requirements, the data modeling module collects medical data from the user side and creates new fact tables and dimension tables.

4. The system according to claim 3, characterized in that The re-encrypting of the modeled medical data includes: An encrypted channel is established between the client and the server through the SSL / TLS protocol to encrypt the data communicated by both parties; each different medical service is encrypted to form an encryption block, and the user sends a query request to the server through the user terminal; the server provides different access rights for different user terminals, and searches for the corresponding encryption block based on the query request of the user terminal, and determines whether the encryption block is within the scope of the access right of the user terminal. If it is within the scope of the access right, the encryption block is sent to the user terminal for decryption; if it is not within the scope of the access right, the server feedback that there is no access right is given to the user terminal.

5. The system according to claim 1, characterized in that The data analysis module uses a factor analysis method to analyze the medical data uploaded by the user, including: Aggregate multiple sets of medical data with the same type of disease. One piece of medical data contains one piece of disease data and multiple pieces of related data. Standardize multiple sets of medical data so that different related data contained in each set of medical data have the same scale. After the standardization process, multiple common factors and special factors are selected; the common factors refer to the relevant data shared by multiple groups of medical data, and the special factors refer to the relevant data unique to each group of medical data; The Pearson correlation coefficient was constructed for each common factor and the disease data. If the absolute value of the Pearson correlation coefficient was greater than 0.6, the common factor was retained; if it was less than 0.6, the corresponding common factor was deleted; Each common factor that remains is given a practical explanation based on its relationship with the symptoms.

6. The system according to claim 5, characterized in that The construction of the Pearson correlation coefficient includes: Among them, n groups of disease data with the same disease are summarized, n represents the number of groups, x i ,y i Represents the data corresponding to the case data of group i and any common factor, X and Y represent x i ,y i The left side of the equation represents the Pearson correlation coefficient, and the numerator on the right side of the equation represents x i With y i The covariance of x i With y i Standard deviation, and are the means of X and Y respectively.

7. The system according to claim 1, characterized in that The data sharing module comprises: Establishing a data flow channel between the server and other data centers and exchanging data, wherein the data flow channel includes a dedicated network, a leased network, and a virtual private network; The data exchange methods include: electronic or digital file transmission, data exchange through portable storage devices, data exchange through email, database sharing or database transaction information exchange, and data exchange based on network or cloud services.

Images