CN115440328A - Intelligent chronic disease management system based on big data - Google Patents
Intelligent chronic disease management system based on big data Download PDFInfo
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- CN115440328A CN115440328A CN202110612675.1A CN202110612675A CN115440328A CN 115440328 A CN115440328 A CN 115440328A CN 202110612675 A CN202110612675 A CN 202110612675A CN 115440328 A CN115440328 A CN 115440328A
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/60—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H80/00—ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
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Abstract
The invention belongs to the field of medical technology and clinical research, and particularly relates to an intelligent chronic disease management system based on big data. The method comprises the following steps: the user information acquisition module is used for acquiring chronic disease information and user personal information and sending the information to the information intelligent processing module; the intelligent information processing module is used for screening chronic disease information and user personal information, removing abnormal values and sending the abnormal values to the training data generation module; the training data generation module is used for training the screened information through machine learning to generate training data and sending the training data to the clustering association analysis module; the cluster association analysis module is used for carrying out cluster analysis on the training data to obtain a plurality of cluster centers and carrying out association analysis on all the cluster centers to obtain a frequent item set between personal activities and chronic diseases; and the output display module is used for obtaining the strong association rule between the personal activity and the chronic disease through the frequent item set and visualizing the strong association rule.
Description
Technical Field
The invention belongs to the field of medical technology and clinical research, and particularly relates to an intelligent chronic disease management system based on big data.
Background
The existing chronic disease management system cannot independently acquire patient chronic disease information and personal living habit information, effectively screens the information and intelligently screens the information, so that deviation exists in partial patient information, data of the chronic disease management system is inaccurate, and efficient management of health files of patients created by the patient chronic disease management system cannot be realized.
Disclosure of Invention
The invention aims to improve the efficiency of a chronic disease management system and provides an intelligent chronic disease management system based on big data. The invention realizes intelligent pretreatment of the patient chronic disease information and the personal life habit information, and screens the information; the system can realize the independent management of the chronic disease information, and the relation between the chronic diseases and the life habits is insufficient.
The technical scheme adopted by the invention for realizing the purpose is as follows:
intelligent chronic disease management system based on big data, including user information acquisition module, information intelligence processing module, training data generation module, cluster correlation analysis module and output display module, wherein:
the user information acquisition module is used for acquiring chronic disease information and user personal activity information and sending the information to the information intelligent processing module;
the intelligent information processing module is used for screening the chronic disease information and the user personal activity information, removing abnormal values and sending the abnormal values to the training data generating module;
the training data generation module is used for training the screened information through machine learning to generate training data and sending the training data to the clustering association analysis module;
the cluster association analysis module is used for carrying out cluster analysis on the training data to obtain a plurality of cluster centers and carrying out association analysis on all the cluster centers to obtain a frequent item set between the personal activity information and the chronic disease information;
and the output display module is used for obtaining the strong association rule between the personal activity information and the chronic disease information through the frequent item set and visualizing the strong association rule.
The intelligent chronic disease management system is loaded in a management server, and a user carries out man-machine interaction operation with the intelligent chronic disease management system on a browser through a 5G network.
The chronic disease information includes: clinic diagnosis and treatment information, hospitalization diagnosis and treatment information and physical examination information.
The user personal information includes: file information, medical history information.
Abnormal values in the chronic disease information and the user personal information are removed by a Z-Score standardization method.
And performing cluster analysis on the training data by adopting a K-means algorithm.
The cluster center represents personal activity information or chronic disease information.
The intelligent chronic disease management method based on big data comprises the following steps:
the user information acquisition module acquires chronic disease information and user personal information and sends the information to the information intelligent processing module;
the intelligent information processing module screens the chronic disease information and the personal activity information of the user, removes abnormal values and sends the abnormal values to the training data generation module;
the training data generation module trains the screened information through machine learning to generate training data and sends the training data to the clustering association analysis module;
the clustering correlation analysis module carries out clustering analysis on the training data to obtain a plurality of clustering centers, and carries out correlation analysis on all the clustering centers to obtain a frequent item set between the personal activity information and the chronic disease information;
and the output display module obtains a strong association rule between the personal activity information and the chronic disease information through the frequent item set and visualizes the strong association rule.
The intelligent chronic disease management system based on big data comprises a memory and a processor; the memory for storing a computer program; the processor is used for realizing the intelligent chronic disease management method based on big data when executing the computer program.
A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, implements a big-data based intelligent chronic disease management method.
The invention has the following beneficial effects and advantages:
1. the invention realizes intelligent pretreatment of patient chronic disease information and personal life habit information, screens real and reliable information, and avoids interference of useless and false information on the chronic disease management process.
2. The method and the device build the self-adaptive model through machine learning and deep learning to train the screened user chronic disease information and the personal life habit information.
3. The present invention achieves management by creating a health profile for each patient.
4. The invention can independently acquire the patient chronic disease information and the personal life habit information, screen the information and obtain the training data through the self-adaptive model, thereby carrying out cluster association analysis and realizing the individuation of the patient chronic disease management system.
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FIG. 1 is an overall architecture diagram of an intelligent chronic disease management system;
FIG. 2 is a block diagram of a chronic disease management system;
fig. 3 is a chronic disease management system architecture diagram.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
In order to achieve the above purpose, the intelligent chronic disease management system based on big data provided herein includes a system overall architecture as shown in fig. 1, an operating system of a health file management system for chronic disease management adopts a Linux operating system, adopts a PHP development programming language, provides a management server loaded with Apache software, provides a database server loaded with Mysql software, and uses a 5G network to transmit data to increase the speed. A Chronic Disease Management System (CDMS) is connected to the EHR and mobile applications on the patient device. The CDMS may perform health data analysis, alerts, and process information (by sending notifications to patients and receiving messages thereof). It is a central system that can be built to accommodate multiple chronic diseases and support specific mobile applications for each individual disease. The user includes doctor and patient two kinds, provides two sets of different user systems, can use cell-phone, PC multi-end to log in, and the web page can imbed the WeChat simultaneously, facilitates the use and scans the two-dimensional code interface. The chronic disease management system comprises the following five modules as shown in fig. 2:
the system comprises a user information acquisition module, a health record management system and a health record management module, wherein the user information acquisition module is mainly used for acquiring user chronic disease information and user personal living habit information, and the information acquired by the module is transmitted to the health record management system in real time to store a result;
the information intelligent processing module is mainly used for intelligently preprocessing the user information acquisition module, screening reliable information and intelligently intervening the real reliable information;
the training data generation module is mainly used for training data of the user information subjected to the information intelligent preprocessing module through machine learning and deep learning to generate corresponding training data;
the cluster association analysis module is mainly used for carrying out cluster analysis on the training data generated by the training data generation module according to time and space to obtain a plurality of corresponding clusters, and then carrying out association analysis on the plurality of clusters to obtain a frequent item set between personal activities and diseases, wherein the frequent item set comprises personal activity information such as work and rest time, eating habits and the like; chronic disease information such as blood pressure values continuously monitored daily by hypertensive patients, blood glucose conditions regularly monitored by diabetic patients, and the like;
the calculation and output display module is mainly used for calculating to obtain a strong rule result set between the personal activities and the diseases based on a frequent item set between the personal activities and the diseases, visualizing and outputting and displaying the strong rule result set between the personal activities and the diseases, transmitting the results displayed by the module to a health file management system in real time, and storing the results, wherein the strong rule result set comprises the association between the chronic diseases and the personal activities, for example, the diabetes patients need to monitor blood sugar regularly, and the daily eating habits of the patients can directly influence the level of the blood sugar;
the health file management system comprises a browser, a management server and a database server, residents use the browser to be in network connection with the management server through a 5G network, administrators use the browser to be in network connection with the management server through the 5G network, the management server is in network connection with the database server, and the management server is internally loaded with the health file management system for chronic disease management;
the health file management system for chronic disease management comprises a resident service information module, a resident health file module, a chronic disease management module and a system management module; the resident service information module comprises browsing crowd information and browsing follow-up visit records; the resident health record module comprises resident family information and resident special information; the chronic disease management module comprises management information and trend information of chronic diseases such as hypertension, diabetes, coronary heart disease and the like; the system management module comprises user authority management and newly added user management;
the chronic disease management system relates to various links of prevention, nursing, education, management, service and the like of chronic diseases, can be summarized into four major parts of health education, professional management, remote management and health monitoring, and is structurally shown in fig. 3.
1. Health education
The method is characterized in that a perfect user health file is established by relying on user information management of a chronic disease management system, and the user chronic disease health file can be established by two schemes:
(1) The scheme relies on a perfect electronic medical record system, includes integration of all data of hospitals, community clinics, health centers and medical databases, and also includes detection of missing data and abnormal data of users, and abnormal values are determined, and Z-Score standardization is used for unifying the data to a comparable interval. The formula is as follows:
in equation 1, x represents a value of a variable, μ represents a mean value of an overall sample, and σ represents a standard deviation of the overall sample data. Using this method, data can be efficiently converted to the same magnitude and if the absolute value of the normalized data is greater than some threshold, it can be treated as an outlier. The threshold value is generally set to be 2.5, 3.0 or 3.5, and the selection of the threshold value is considered through specific conditions. If the missing value or the abnormal value is detected, the patient can be informed of self-supplementing data through a mobile phone short message.
(2) The user creates the archive by himself: and (4) the patient scans the codes through the mobile phone terminal to fill in the filing information, and filing is completed, wherein the file content comprises health files and chronic disease files. The information includes archive information (name, age, address, identification card, telephone, occupation, etc.), medical history information (past history, allergy history, family history, operation history, etc.), special information (such as customer category), etc., the numbers of the patient and the relatives thereof can be maintained, and a default number can be set. Meanwhile, the information is combined with the electronic medical record information, and the information comprises clinic diagnosis and treatment information (including clinic records, clinic diagnoses, examinations, and the like) of the hospital, hospital diagnosis and treatment information (including admission registration, medical advice information, discharge knots, operation records, hospital expenses, examination reports, and the like), physical examination information (including physical examination registration, physical examination records, and physical examination reports), and the integrated management of basic information and diagnosis and treatment data of patients is realized. Meanwhile, autonomous device access is supported, the patient completes the collection of weight, height and blood pressure data by self, and the detection data can be transmitted to the follow-up visit record of the outpatient service in real time. This also facilitates the supplementation and correction of missing data and anomalous data.
Data is loaded into a central clinical data warehouse and analyzed there, creating a specialized chronic disease analysis system with a data warehouse that is supported by OLAP multidimensional datasets and visualizes software logical graphs through reports, charts and dashboards. Physicians create personal management programs in EHRs or CDMS based on the health status, complications, allergies, physical activity levels, nutritional habits, etc. of a particular patient. The patient receives the management plan through the mobile application and follows the recommendations and directions. The patient records his blood pressure, diet, sleep, mood, medication intake, etc. and sends it to the caregiver. This data allows the CDMS to monitor the health of the patient and inform the doctor or patient of information about the interference changes. The doctor or the system may then suggest to the patient to schedule appointments to discuss changes in the management plan.
According to the result of chronic disease evaluation, different education contents are sent according to different types of diseases of patients, including risk factors for good onset of diseases, types of diseases, evolution and prognosis of diseases, treatment of critical conditions of diseases and the like. The user can download from the system knowledge base template, and can also edit the propaganda and education content by the self-definition of an administrator. The system knowledge base integrates various related knowledge of the cause, type, mind, movement, rehabilitation and the like of the chronic diseases, and provides health education support for the chronic disease patients to learn and know the related knowledge of the chronic diseases. And the chronic disease patient can self-control and correct bad and unscientific living habits and risk factors through health education, and the management of the chronic diseases is better promoted by matching with the management scheme of a doctor consciously.
2. Professional management
(1) The sick users are psychologically guided by combining the illness states of the users, and self-defined short message contents including science popularization of the illness states and encouragement and blessing of the users are customized, so that positive effects on management can be achieved.
(2) The patient can record the daily medication condition and the illness state of the patient in the system, the administrator can set medication reminding rules according to the specific condition of the patient, and the system automatically reminds the patient by medication according to the rules.
3. Health monitoring
Information such as vital signs, examination, inspection and health analysis tables required by the state of an illness of a chronic disease patient can be set with warning values to remind background management personnel to pay attention, and management is carried out through diversified doctor-patient interaction modes. The system supports the access of different types of intelligent terminals, such as a blood glucose meter, a sphygmomanometer, a weight scale, an ear thermometer, an oximeter, a fetal heart monitor and the like, and can provide a remote monitoring function for patients with chronic diseases. Meanwhile, a periodic physical examination plan is customized for each user, the user is reminded to go to a regular physical examination organization or a hospital for physical examination by short messages on time, and a physical examination report is uploaded to a system.
4. Remote management
(1) Intelligent diet management: combining the actual condition of the patient, a reasonable diet plan is set for the chronic patient. The system automatically calculates the nutrient content and calorie of food ingested by the patient according to a food nutrient model and a calorie model, can compare the nutrient content and calorie with a scheme formulated by an expert, and starts to execute after being determined by an administrator. The system may automatically adjust the regimen after the patient's condition has changed. For example, a diabetic makes a certain amount of protein and cholesterol required to be taken every day according to the disease condition degree, under the condition of continuous blood sugar monitoring, unstable blood sugar control and slight rise are found, and a background can automatically adjust a diet scheme according to the condition, so that the automatic management of diseases is realized, and the diet of a chronic patient is scientifically and reasonably controlled.
(2) Person-specific exercise management: the principle is that the medicine is different from person to person and has the same amount of force, it is progressive and constant. Patients with good physical conditions can jogging, rope skipping, stair climbing, mountain climbing, bicycle riding, swimming, and rhythmic exercise. The system provides a proper motion management scheme suggestion according to the disease type of the patient and the actual physical condition of the patient, and an administrator can edit and modify the motion management scheme suggestion and directly transmit motion data to a system server in real time in a wireless mode by combining with a motion instrument. Does not need complex computer operation and reduces the operation difficulty of patients. The administrator regularly adjusts the motion scheme according to real-time motion data, so that the motion of the patient is more scientific and reasonable, excessive and insufficient motion is prevented, and powerful help is provided for chronic disease management.
In addition, a real-time communication interface between a doctor and a patient is provided, the patient can conveniently inquire the state of an illness and a management scheme, automatic follow-up visits are supported, and the follow-up time can be customized. Through the configuration of the automatic follow-up rule, when a patient meets the follow-up requirement, the system automatically sends a follow-up questionnaire and a form to a mobile phone terminal (APP and WeChat) of the patient, and the patient can fill in the questionnaire and the form information to complete the automatic follow-up without the intervention of a nurse. The system supports various follow-up visit modes of discharge follow-up visit, clinic follow-up visit, transfer follow-up visit, filing follow-up visit, contract patient follow-up visit and physical examination patient follow-up visit, and supports follow-up visit modes such as short messages, weChat, APP, telephone and the like.
Claims (10)
1. Intelligent chronic disease management system based on big data, which is characterized by comprising a user information acquisition module, an information intelligent processing module, a training data generation module, a cluster association analysis module and an output display module, wherein:
the user information acquisition module is used for acquiring chronic disease information and user personal activity information and sending the information to the information intelligent processing module;
the intelligent information processing module is used for screening chronic disease information and user personal activity information, removing abnormal values and sending the abnormal values to the training data generation module;
the training data generation module is used for training the screened information through machine learning to generate training data and sending the training data to the clustering association analysis module;
the cluster association analysis module is used for carrying out cluster analysis on the training data to obtain a plurality of cluster centers and carrying out association analysis on all the cluster centers to obtain a frequent item set between the personal activity information and the chronic disease information;
and the output display module is used for obtaining the strong association rule between the personal activity information and the chronic disease information through the frequent item set and visualizing the strong association rule.
2. The big-data based intelligent chronic disease management system of claim 1, wherein the intelligent chronic disease management system is loaded in a management server, and a user performs man-machine interaction operation with the management server through a 5G network on a browser.
3. The big-data based intelligent chronic disease management system of claim 1, wherein the chronic disease information comprises: clinic diagnosis and treatment information, hospitalization diagnosis and treatment information and physical examination information.
4. The intelligent big data-based chronic disease management system of claim 1, wherein the user personal information comprises: file information, medical history information.
5. The big data based intelligent chronic disease management system of claim 1, wherein outliers in the chronic disease information and the user personal information are removed by a Z-Score normalization method.
6. The intelligent big-data-based chronic disease management system of claim 1, wherein the clustering analysis of the training data employs a K-means algorithm.
7. The big-data based intelligent chronic disease management system of claim 1, wherein the cluster center represents personal activity information or chronic disease information.
8. The intelligent chronic disease management method based on big data is characterized by comprising the following steps:
the user information acquisition module acquires chronic disease information and user personal information and sends the information to the information intelligent processing module;
the intelligent information processing module screens the chronic disease information and the personal activity information of the user, removes abnormal values and sends the abnormal values to the training data generation module;
the training data generation module trains the screened information through machine learning to generate training data and sends the training data to the clustering association analysis module;
the clustering association analysis module carries out clustering analysis on the training data to obtain a plurality of clustering centers, and carries out association analysis on all the clustering centers to obtain a frequent item set between the personal activity information and the chronic disease information;
and the output display module obtains the strong association rule between the personal activity information and the chronic disease information through the frequent item set and visualizes the strong association rule.
9. The intelligent chronic disease management system based on big data is characterized by comprising a memory and a processor; the memory for storing a computer program; the processor, when executing the computer program, is configured to implement the big-data based intelligent chronic disease management method of claim 8.
10. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, implements the big-data based intelligent chronic disease management method according to claim 8.
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