Health big data accurate analysis and management system based on artificial intelligence

Abstract

The invention relates to the technical field of artificial intelligence, in particular to an artificial intelligence-based health big data accurate analysis and management system, which comprises a data acquisition module, a data preprocessing module, a data storage module, a data analysis and mining module, a health management scheme generation module, an information push and interaction module and a user management module. The data acquisition module is used for collecting health data from different sources; according to the system, efficient processing and intelligent analysis of mass health data are realized, the health conditions of individuals and groups are evaluated and insighted through an accurate algorithm, doctors, researchers and public health departments are helped to more accurately understand health trends and disease occurrence laws, so that personalized medical suggestions and early warning information are provided, and the health management efficiency is improved. The early diagnosis rate and the treatment effect of diseases are improved, the medical resource configuration is optimized, and the medical cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an artificial intelligence-based health big data precision analysis and management system. Background Art

[0002] With the continuous advancement of medical technology and people's increasing attention to health, the sources of health data have become more and more extensive. In traditional health management, whether it is personal self-health management or medical staff's health assessment of patients, they often rely on experience and judgment. With the development of computing technology and artificial intelligence, machine learning and deep learning algorithms in the field of artificial intelligence have made significant progress, and precision medicine based on big data has become possible. Artificial intelligence technology has powerful data processing capabilities and can cope with the complexity and scale of health big data.

[0003] Although existing artificial intelligence systems can provide support in disease prediction, risk assessment, and patient management, existing systems often face challenges in the diversity of data sources and the integration of data quality. They lack in-depth analysis of individual differences among patients and find it difficult to achieve comprehensive, accurate analysis and personalized management of health big data. Therefore, this paper proposes a health big data accurate analysis and management system based on artificial intelligence. Summary of the invention

[0004] In view of this, the present invention provides a health big data precision analysis and management system based on artificial intelligence to solve or alleviate the technical problems existing in the prior art and at least provide a beneficial option.

[0005] The technical solution of the present invention is implemented as follows: an artificial intelligence-based health big data precision analysis and management system, including a data acquisition module, a data preprocessing module, a data storage module, a data analysis and mining module, a health management program generation module, an information push and interaction module and a user management module;

[0006] The data collection module is used to collect health data from different sources;

[0007] The data preprocessing module is used to perform cleaning, conversion and normalization preprocessing operations on the collected raw data;

[0008] The data storage module is used to store the pre-processed health big data;

[0009] The data analysis and mining module is used to perform in-depth analysis and mining of pre-processed health big data using artificial intelligence algorithms;

[0010] The health management program generation module is used to generate a health management program;

[0011] The information push and interaction module is used to push the generated health management plan and analysis report information to the user;

[0012] The user management module is responsible for managing user information in the system.

[0013] Further preferably, in the data acquisition module, connections are established with wearable devices through various interfaces, and Bluetooth and Wi-Fi wireless communication technologies are used to receive physiological data uploaded by the device in real time, and connect with the electronic medical record system of the medical institution to obtain detailed medical records of the patient with the help of data interface standards. In addition, data actively input by users is received, allowing users to fill in their own dietary structure, living habits, and daily emotional state information, and integrating data measured by health monitoring instruments.

[0014] Further preferably, in the data preprocessing module, by writing data cleaning rules, duplicate, erroneous or illogical data records are removed, and missing values ​​are filled by using mean filling, median filling, and adjacent value interpolation methods; standardization and normalization algorithms are used to unify data of different dimensions, and at the same time, encoding conversion operations are performed on the data.

[0015] Further preferably, in the data storage module, a distributed storage architecture is used for data storage, and data encryption technology is used to encrypt and access data by establishing a data table structure and an index mechanism to query and retrieve data.

[0016] Further preferably, in the data analysis and mining module, artificial intelligence algorithms are used to conduct in-depth analysis and mining of preprocessed and stored data, classification algorithms in machine learning are used to predict diseases, classification models are trained based on a large amount of labeled historical health data, the association patterns between different health characteristics and disease outcomes in the model learning data, regression algorithms are used to perform correlation analysis and trend prediction of health indicators, and convolutional neural networks in deep learning are used to extract features from medical imaging data and assist in disease diagnosis.

[0017] Preferably, logistic regression is used for binary disease prediction, and the basic formula of the logistic regression model is:

[0018]

[0019] P(Y=1|X) means that given the feature vector X (containing various health-related features), the event Y=1 represents the probability of disease occurrence;

[0020] For multi-classification disease prediction, it can be extended to multinomial logistic regression, and the formula is as follows:

[0021]

[0022] Where K represents the total number of disease categories, k represents a specific type of disease (k = 1, 2...K), z k It is also a linear combination of feature vectors X, but with different coefficients for different categories k, similar to the calculation method of z in the above two-category classification:

[0023] z k =β k0 +β k1 x1+β k2 x2+…+β kn x n

[0024] Determine all β kj (j=0, 1…n; k=1, 2, K) coefficients to obtain the predicted probability P(Y=k|X) of different types of diseases.

[0025] Further preferably, in the health management program generation module, based on the results output by the data analysis and mining module, combined with the health knowledge graph and the preset health management rule library, natural language processing technology is used to generate a personalized health management program.

[0026] Further preferably, in the information push and interaction module, a bridge of communication between the system and the user is built. By establishing a Web interface and a mobile APP user-side entrance and utilizing message push technology, health management plans, health analysis reports, and early warning information are pushed to corresponding users. An interactive function is set on the user side, and the knowledge base and analysis module are called to generate reply content and feed it back to the user.

[0027] Further preferably, in the user management module, user information is managed by allocating different data access permissions and system function usage permissions according to different user roles.

[0028] The embodiment of the present invention has the following advantages due to the adoption of the above technical solution:

[0029] The present invention realizes efficient processing and intelligent analysis of massive health data, evaluates and provides insights into the health status of individuals and groups through precise algorithms, and helps doctors, researchers and public health departments to more accurately understand health trends and disease occurrence patterns, thereby providing personalized medical advice and early warning information, improving the early diagnosis rate and treatment effect of diseases, optimizing the allocation of medical resources, and reducing medical costs.

[0030] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0033] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0034] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0035] like Figure 1 As shown, the embodiment of the present invention provides a health big data precision analysis and management system based on artificial intelligence, including a data acquisition module, a data preprocessing module, a data storage module, a data analysis and mining module, a health management program generation module, an information push and interaction module and a user management module;

[0036] Data collection module, used to collect health data from different sources;

[0037] The data preprocessing module is used to clean, convert and normalize the collected raw data;

[0038] A data storage module, used to store pre-processed health big data;

[0039] Data analysis and mining module, which is used to conduct in-depth analysis and mining of pre-processed health big data using artificial intelligence algorithms;

[0040] A health management plan generation module is used to generate a health management plan;

[0041] Information push and interaction module, used to push generated health management plans and analysis report information to users;

[0042] The user management module is responsible for managing user information in the system.

[0043] In one embodiment, in the data acquisition module, connections are established with wearable devices through various interfaces, and Bluetooth and Wi-Fi wireless communication technologies are used to receive physiological data uploaded by the device in real time, and the data is connected to the electronic medical record system of the medical institution. The patient's detailed medical records are obtained with the help of data interface standards. In addition, data actively input by users is received, allowing users to fill in their own dietary structure, living habits, and daily emotional state information, and integrate data measured by health monitoring instruments; by collecting data from multiple channels, the information islands between different health data are broken, so that the system can obtain a full perspective on personal health status, and provide rich and detailed basic information for subsequent accurate analysis and management, avoiding one-sided analysis results due to single data.

[0044] In one embodiment, in the data preprocessing module, data cleaning rules are written to remove duplicate, erroneous or illogical data records, and missing values ​​are filled by using mean filling, median filling, and adjacent value interpolation methods; standardization and normalization algorithms are used to unify data of different dimensions, and at the same time, encoding conversion operations are performed on the data; after preprocessing, the data becomes more regular and accurate, and the interference with subsequent analysis models helps to improve the accuracy and stability of the model, making the health analysis results based on these data more reliable.

[0045] In one embodiment, in the data storage module, a distributed storage architecture is used for data storage. By establishing a data table structure and an index mechanism, data query and retrieval are performed, and data encryption technology is used to encrypt data access. Reasonable storage structure and index design enable rapid location and acquisition of relevant data when specific user data needs to be called or data analysis is required, thereby improving the operating efficiency of the entire system. Encryption and access control measures ensure the privacy and integrity of user health data.

[0046] In one embodiment, in the data analysis and mining module, artificial intelligence algorithms are used to conduct in-depth analysis and mining of preprocessed and stored data, classification algorithms in machine learning are used to predict diseases, classification models are trained based on a large amount of labeled historical health data, and the association patterns between different health characteristics and disease outcomes in the model learning data are used. Regression algorithms are used to perform correlation analysis and trend prediction of health indicators, and convolutional neural networks in deep learning are used to extract features from medical imaging data and assist in disease diagnosis. With the help of artificial intelligence's learning and pattern recognition capabilities, hidden health-related laws and relationships are mined from massive and complex data, and disease prediction, risk assessment, and potential health problems are discovered more accurately, providing a scientific basis for subsequent health management.

[0047] In one embodiment, logistic regression is used for binary disease prediction, and the basic formula of the logistic regression model is:

[0048]

[0049] P(Y=1|X) means that given the feature vector X (containing various health-related features), the event Y=1 represents the probability of disease occurrence;

[0050] For multi-classification disease prediction, it can be extended to multinomial logistic regression, and the formula is as follows:

[0051]

[0052] Where K represents the total number of disease categories, k represents a specific type of disease (k = 1, 2...K), z k It is also a linear combination of feature vectors X, but with different coefficients for different categories k, similar to the calculation method of z in the above two-category classification:

[0053] z k =β k0 +β k1 x1+β k2 x2+…+β kn x n

[0054] Determine all β kj (j=0, 1…n; k=1, 2, K) coefficients to obtain the predicted probability P(Y=k|X) of different types of diseases.

[0055] In one embodiment, in the health management plan generation module, based on the results output by the data analysis and mining module, combined with the health knowledge graph and the preset health management rule base, natural language processing technology is used to generate a personalized health management plan; a management plan tailored to each user that meets their own health status and risk characteristics is provided, so that users can clearly understand their own health problems and the specific measures that should be taken, thereby increasing users' enthusiasm for actively participating in health management and effectively helping users improve their health status.

[0056] In one embodiment, in the information push and interaction module, a bridge of communication between the system and the user is built. By establishing a Web interface and a mobile APP user-side entrance, the health management plan, health analysis report, and early warning information are pushed to the corresponding users using message push technology. An interactive function is set on the user side, and the knowledge base and analysis module are called to generate reply content and feedback to the user; ensuring that users can obtain important health-related information in the first place, whether it is a health risk warning or a new management suggestion, thereby improving the timeliness of health management.

[0057] In one embodiment, in the user management module, user information management allocates different data access rights and system function usage rights according to different user roles; strict permission control prevents improper access and leakage of user health data and protects the privacy rights and interests of each user.

[0058] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of various changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An artificial intelligence-based health big data precision analysis and management system, characterized by: It includes data acquisition module, data preprocessing module, data storage module, data analysis and mining module, health management program generation module, information push and interaction module and user management module; The data collection module is used to collect health data from different sources; The data preprocessing module is used to perform cleaning, conversion and normalization preprocessing operations on the collected raw data; The data storage module is used to store the pre-processed health big data; The data analysis and mining module is used to perform in-depth analysis and mining of pre-processed health big data using artificial intelligence algorithms; The health management program generation module is used to generate a health management program; The information push and interaction module is used to push the generated health management plan and analysis report information to the user; The user management module is responsible for managing user information in the system.

2. According to claim 1, the artificial intelligence-based health big data accurate analysis and management system is characterized by: In the data acquisition module, connections are established with wearable devices through various interfaces, and Bluetooth and Wi-Fi wireless communication technologies are used to receive physiological data uploaded by the device in real time, and to connect with the electronic medical record system of the medical institution. With the help of data interface standards, detailed medical records of patients are obtained. In addition, data actively input by users is received, allowing users to fill in their own dietary structure, living habits, and daily emotional state information, and integrating data measured by health monitoring instruments.

3. According to claim 1, the artificial intelligence-based health big data accurate analysis and management system is characterized by: In the data preprocessing module, data cleaning rules are written to remove duplicate, erroneous or illogical data records. Missing values ​​are filled by mean filling, median filling and adjacent value interpolation. Standardization and normalization algorithms are used to unify data of different dimensions, and at the same time, encoding conversion operations are performed on the data.

4. The artificial intelligence-based health big data accurate analysis and management system according to claim 1, characterized in that: In the data storage module, a distributed storage architecture is used for data storage, and data table structure and index mechanism are established to query and retrieve data, and data encryption technology is used to encrypt and access data.

5. The artificial intelligence-based health big data accurate analysis and management system according to claim 1, characterized in that: In the data analysis and mining module, artificial intelligence algorithms are used to conduct in-depth analysis and mining of preprocessed and stored data, classification algorithms in machine learning are used to predict diseases, classification models are trained based on a large amount of labeled historical health data, and the association patterns between different health characteristics and disease outcomes in the model learning data are used. Regression algorithms are used to perform correlation analysis and trend prediction of health indicators, and convolutional neural networks in deep learning are used to extract features from medical imaging data and assist in disease diagnosis.

6. The artificial intelligence-based health big data accurate analysis and management system according to claim 1, characterized in that: Logistic regression is used for binary disease prediction. The basic formula of the logistic regression model is: P(Y=1|X) means that given the feature vector X (containing various health-related features), the event Y=1 represents the probability of disease occurrence; For multi-classification disease prediction, it can be extended to multinomial logistic regression, and the formula is as follows: Where K represents the total number of disease categories, k represents a specific type of disease (k = 1, 2...K), z k It is also a linear combination of feature vectors X, but with different coefficients for different categories k, similar to the calculation method of z in the above two-category classification: z k =b k0 +b k1 x1+b k2 x2+…+b kn x n Determine all β kj (j=0, 1…n; k=1, 2, K) coefficients to obtain the predicted probability P(Y=k|X) of different types of diseases.

7. The artificial intelligence-based health big data accurate analysis and management system according to claim 1, characterized in that: In the health management program generation module, based on the results output by the data analysis and mining module, combined with the health knowledge graph and the preset health management rule library, natural language processing technology is used to generate a personalized health management program.

8. The artificial intelligence-based health big data accurate analysis and management system according to claim 1, characterized in that: In the information push and interaction module, a bridge of communication between the system and the user is built. By establishing a Web interface and a mobile APP user-side entrance and utilizing message push technology, health management plans, health analysis reports, and early warning information are pushed to the corresponding users. An interactive function is set up on the user side, and the knowledge base and analysis module are called to generate reply content and feed it back to the user.

9. The artificial intelligence-based health big data accurate analysis and management system according to claim 1, characterized in that: In the user management module, user information is managed by allocating different data access permissions and system function usage permissions according to different user roles.

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