Big data cardiovascular disease risk monitoring system

Abstract

The invention relates to the technical field of cardiovascular diseases, and discloses a big data cardiovascular disease risk monitoring system, which comprises a data acquisition module, a data preprocessing module, a data analysis module, a risk assessment module and a result display module, the data acquisition module is used for collecting basic information, clinical data, lifestyle data and environmental data of a patient, the data preprocessing module is used for cleaning, converting and integrating the collected data, and the data analysis module is used for mining and analyzing the preprocessed data by applying a big data analysis technology. According to the big data cardiovascular disease risk monitoring system, the cardiovascular disease risk can be evaluated more comprehensively, the evaluation accuracy is improved through the data preprocessing and big data analysis technology, the result display module displays the evaluation result in a visual mode, a user can understand and apply the evaluation result conveniently, and the user experience is improved. Personalized health suggestions and treatment schemes can be provided according to specific conditions of patients.

Description

Technical Field

[0001] The present invention relates to the technical field of cardiovascular diseases, and in particular to a big data cardiovascular disease risk monitoring system. Background Art

[0002] Cardiovascular and cerebrovascular diseases are a general term for cardiovascular and cerebrovascular diseases, and generally refer to ischemic or hemorrhagic diseases of the heart, brain and systemic tissues caused by hyperlipidemia, blood viscosity, atherosclerosis, hypertension, etc. Cardiovascular and cerebrovascular diseases are common diseases that seriously threaten the health of humans, especially those over 50 years old, and are characterized by high morbidity, high disability and high mortality.

[0003] At present, with the acceleration of the pace of modern life and the intensification of population aging, cardiovascular disease has become one of the major health problems worldwide, posing a serious threat to human life and health. In order to effectively manage and prevent cardiovascular disease, accurate risk assessment and monitoring are particularly important. Traditional cardiovascular disease risk assessment mainly relies on doctors to make subjective judgments based on patients' physical examination reports, medical history and other information. This method is not only time-consuming and laborious, but also easily limited by doctors' experience and knowledge level, making it difficult to ensure the accuracy and consistency of the assessment results. Therefore, there is an urgent need for a cardiovascular disease risk monitoring system based on big data technology. Summary of the invention

[0004] The purpose of the present invention is to provide a big data cardiovascular disease risk monitoring system to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: including a data acquisition module, a data preprocessing module, a data analysis module, a risk assessment module and a result display module;

[0006] The data collection module is used to collect basic information, clinical data, lifestyle data and environmental data of the patient;

[0007] The data preprocessing module is used to clean, convert and integrate the collected data;

[0008] The data analysis module is used to mine and analyze the pre-processed data using big data analysis technology;

[0009] The risk assessment module is used to assess the patient's cardiovascular disease risk level based on the analysis results;

[0010] The result display module is used to display the evaluation results to the user in an intuitive manner.

[0011] The data collection module can collect various types of information about patients, providing a rich data foundation for subsequent analysis. The data preprocessing module cleans, converts and integrates the collected data to ensure data quality and availability. The data analysis module uses advanced big data analysis technology to explore the potential value in the data and provide support for risk assessment. The risk assessment module accurately assesses the patient's cardiovascular disease risk level based on the analysis results. Finally, the result display module presents the assessment results to the user in an intuitive way, so that patients and doctors can clearly understand the patient's risk status. This system covers many aspects of the patient's data and can more comprehensively assess the risk of cardiovascular disease. The data preprocessing and big data analysis technology improve the accuracy of the assessment. The result display module displays the assessment results in an intuitive way, which is easy for users to understand and apply, and can provide personalized health advice and treatment plans based on the patient's specific situation.

[0012] Preferably, the data acquisition module includes a sensor device, a medical record system interface and a user information input interface, and the sensor device is used to collect the patient's physiological data in real time, such as heart rate, blood pressure, blood sugar, etc.

[0013] Preferably, the medical record system interface is used to obtain information such as the patient's past medical history, diagnosis results and treatment plans, and the user information input interface is used for the patient to input lifestyle and environment-related information, such as diet, exercise, smoking status, work pressure, etc.

[0014] By setting up a medical record system interface and connecting with the hospital's electronic medical record system or other medical record systems, the system can obtain information such as the patient's past medical history, diagnosis results, and treatment plans, which are crucial for assessing the patient's overall health status and disease risk;

[0015] The user information input interface allows patients to input information related to their lifestyle and environment, such as diet, exercise, smoking, work pressure, etc. The input information helps the system to have a more comprehensive understanding of the patient's living habits and potential risks.

[0016] Preferably, the data preprocessing module includes a data cleaning unit, a data conversion unit and a data integration unit, and the data cleaning unit is used to remove duplicate data, correct erroneous data and process missing values.

[0017] Preferably, the data conversion unit is used to convert the data into a unified format and standard for subsequent analysis, and the data integration unit is used to integrate data from different data sources to form a complete data set.

[0018] The data conversion unit converts data into a unified format and standard, ensuring the consistency and analyzability of the data, and facilitating subsequent in-depth research. The data integration unit organically integrates data from multiple data sources to construct a complete data set, so that data analysis can more comprehensively and accurately reflect the actual situation.

[0019] Preferably, the data analysis module includes a data mining algorithm and a machine learning model, wherein the data mining algorithm is used to discover potential patterns and associations from a large amount of data, and the machine learning model is used to classify, predict and perform regression analysis on the data to assess risk factors for cardiovascular disease.

[0020] Preferably, the risk assessment module includes a risk assessment model and a risk level classification standard. The risk assessment model comprehensively assesses the patient's cardiovascular disease risk based on the results of the data analysis module, combined with clinical guidelines and expert experience. The risk level classification standard divides the patient's risk level into three levels: low risk, medium risk and high risk.

[0021] Preferably, the result display module includes a visualization interface and a report generation unit. The visualization interface displays the patient's cardiovascular disease risk assessment results in the form of charts, graphs, etc., so that the patient and the doctor can intuitively understand the patient's risk status. The report generation unit is used to generate a detailed risk assessment report, including the patient's basic information, risk factor analysis, assessment results and recommended measures.

[0022] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0023] The present invention can widely collect various types of information of patients through the data acquisition module, providing a rich data foundation for subsequent analysis; clean, convert and integrate the collected data through the data preprocessing module to ensure the quality and availability of the data; use advanced big data analysis technology through the data analysis module to explore the potential value in the data and provide support for risk assessment; accurately assess the patient's cardiovascular disease risk level based on the analysis results through the risk assessment module; finally, the result display module presents the assessment results to the user in an intuitive manner so that the patient and the doctor can clearly understand the patient's risk status; the system covers various aspects of the patient's data and can more comprehensively assess the risk of cardiovascular disease; improves the accuracy of the assessment through data preprocessing and big data analysis technology; the result display module displays the assessment results in an intuitive manner for easy understanding and application by the user, and can provide personalized health advice and treatment plans based on the patient's specific circumstances. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is the overall flow chart of the present invention;

[0025] Figure 2 This is a data collection flow chart of the present invention;

[0026] Figure 3 This is a data preprocessing flow chart of the present invention;

[0027] Figure 4 A flow chart is shown for the results of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] The present invention provides the following technical solutions:

[0030] See also Figure 1 , Figure 2 , Figure 3 and Figure 4 ,Big data cardiovascular disease risk monitoring system, including data acquisition module, data preprocessing module, data analysis module, risk assessment module and result display module;

[0031] The data collection module is used to collect basic information, clinical data, lifestyle data, and environmental data of patients;

[0032] The data preprocessing module is used to clean, convert and integrate the collected data;

[0033] The data analysis module is used to mine and analyze the pre-processed data using big data analysis technology;

[0034] The risk assessment module is used to assess the patient's cardiovascular disease risk level based on the analysis results;

[0035] The result display module is used to display the evaluation results to users in an intuitive way.

[0036] Through the above technical solution, the data acquisition module can widely collect various types of information about patients, providing a rich data foundation for subsequent analysis. The data preprocessing module cleans, converts and integrates the collected data to ensure the quality and availability of the data. The data analysis module uses advanced big data analysis technology to explore the potential value in the data and provide support for risk assessment. The risk assessment module accurately assesses the patient's cardiovascular disease risk level based on the analysis results. Finally, the result display module presents the assessment results to the user in an intuitive way, so that patients and doctors can clearly understand the patient's risk status. This system covers many aspects of the patient's data and can more comprehensively assess the risk of cardiovascular disease. Through data preprocessing and big data analysis technology, the accuracy of the assessment is improved. The result display module displays the assessment results in an intuitive way, which is easy for users to understand and apply, and can provide personalized health advice and treatment plans based on the patient's specific situation.

[0037] The data acquisition module includes sensor equipment, medical record system interface and user information input interface. The sensor equipment is used to collect the patient's physiological data in real time, such as heart rate, blood pressure, blood sugar, etc.

[0038] Through the above technical solutions, the basic information, clinical data, lifestyle data and environmental data of patients are collected. Sensor equipment can collect important physiological data of patients in real time (such as wearable devices) and monitor the physiological data of patients in real time, such as heart rate, blood pressure, blood sugar, etc. These data are transmitted to the system wirelessly to provide real-time and accurate data support for risk assessment.

[0039] The medical record system interface is used to obtain information such as the patient's past medical history, diagnosis results and treatment plans. The user information input interface is used for patients to input information related to lifestyle and environment, such as diet, exercise, smoking status, work pressure, etc.

[0040] Through the above technical solution, by setting up the medical record system interface and connecting with the hospital's electronic medical record system or other medical record system, the system can obtain information such as the patient's past medical history, diagnosis results and treatment plan, which is crucial for assessing the patient's overall health status and disease risk;

[0041] The user information input interface allows patients to input information related to their lifestyle and environment, such as diet, exercise, smoking, work pressure, etc. The input information helps the system to have a more comprehensive understanding of the patient's living habits and potential risks.

[0042] The data preprocessing module includes a data cleaning unit, a data conversion unit and a data integration unit. The data cleaning unit is used to remove duplicate data, correct erroneous data and process missing values.

[0043] Through the above technical solution, the data preprocessing module cleans, converts and integrates the collected data to ensure the accuracy and consistency of the data. The data cleaning unit improves the quality and accuracy of the data by removing duplicate data, correcting erroneous data and processing missing values.

[0044] The data conversion unit is used to convert data into a unified format and standard for subsequent analysis, and the data integration unit is used to integrate data from different data sources to form a complete data set.

[0045] Through the above technical solution, the data conversion unit converts the data into a unified format and standard, ensuring the consistency and analyzability of the data, providing convenience for subsequent in-depth research, while the data integration unit organically integrates data from multiple data sources to construct a complete data set, so that data analysis can more comprehensively and accurately reflect the actual situation.

[0046] The data analysis module includes data mining algorithms and machine learning models. Data mining algorithms are used to discover potential patterns and associations from large amounts of data, and machine learning models are used to perform classification, prediction and regression analysis on data to assess risk factors for cardiovascular disease.

[0047] Through the above technical solutions, big data analysis technology is used to mine and analyze the pre-processed data to discover potential risk factors. Various data mining algorithms (such as association rule mining, cluster analysis, etc.) are used to discover potential patterns and associations from large amounts of data. These patterns and associations help to reveal the risk factors and incidence patterns of cardiovascular disease. By using machine learning technology to classify, predict and regress data, and by training machine learning models, the system can accurately identify risk factors for cardiovascular disease and predict the possibility of patients getting the disease in the future.

[0048] The risk assessment module includes a risk assessment model and a risk level classification standard. The risk assessment model conducts a comprehensive assessment of the patient's cardiovascular disease risk based on the results of the data analysis module, combined with clinical guidelines and expert experience. The risk level classification standard divides the patient's risk level into three levels: low risk, medium risk and high risk.

[0049] Through the above technical solution, the risk assessment model is based on the results of the data analysis module, combined with clinical guidelines and expert experience, to comprehensively assess the patient's cardiovascular disease risk. At the same time, the risk assessment model will consider multiple factors, such as age, gender, family medical history, lifestyle habits, etc., to obtain a comprehensive risk assessment result, and then divide the patient's risk level into three levels: low risk, medium risk and high risk through risk level classification standards, which will help doctors and patients to understand the patient's risk status more intuitively and take corresponding preventive measures.

[0050] The result display module includes a visualization interface and a report generation unit. The visualization interface displays the patient's cardiovascular disease risk assessment results in the form of charts, graphs, etc., so that patients and doctors can intuitively understand the patient's risk status. The report generation unit is used to generate a detailed risk assessment report, including the patient's basic information, risk factor analysis, assessment results and recommended measures.

[0051] Through the above technical solution, the patient's cardiovascular disease risk assessment results are displayed in the form of charts, graphs, etc. This intuitive method helps patients and doctors better understand the risk status and formulate corresponding health management plans. By generating a detailed risk assessment report, including the patient's basic information, risk factor analysis, assessment results and recommended measures, patients can be provided with personalized health advice and treatment plans to help them better manage their health status.

[0052] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations may be made to the embodiments without departing from the principles and spirit thereof, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A big data cardiovascular disease risk monitoring system, comprising a data acquisition module, a data preprocessing module, a data analysis module, a risk assessment module and a result display module; the characteristics are: The data collection module is used to collect basic information, clinical data, lifestyle data and environmental data of the patient; The data preprocessing module is used to clean, convert and integrate the collected data; The data analysis module is used to mine and analyze the pre-processed data using big data analysis technology; The risk assessment module is used to assess the patient's cardiovascular disease risk level based on the analysis results; The result display module is used to display the evaluation results to the user in an intuitive manner.

2. The big data cardiovascular disease risk monitoring system according to claim 1, characterized in that: The data acquisition module includes a sensor device, a medical record system interface and a user information input interface. The sensor device is used to collect the patient's physiological data in real time, such as heart rate, blood pressure, blood sugar, etc.

3. The big data cardiovascular disease risk monitoring system according to claim 2, characterized in that: The medical record system interface is used to obtain information such as the patient's past medical history, diagnosis results and treatment plans, and the user information input interface is used for the patient to input lifestyle and environment related information, such as diet, exercise, smoking status, work pressure, etc.

4. The big data cardiovascular disease risk monitoring system according to claim 1, characterized in that: The data preprocessing module includes a data cleaning unit, a data conversion unit and a data integration unit. The data cleaning unit is used to remove duplicate data, correct erroneous data and process missing values.

5. The big data cardiovascular disease risk monitoring system according to claim 4, characterized in that: The data conversion unit is used to convert data into a unified format and standard for subsequent analysis, and the data integration unit is used to integrate data from different data sources to form a complete data set.

6. The big data cardiovascular disease risk monitoring system according to claim 1, characterized in that: The data analysis module includes a data mining algorithm and a machine learning model. The data mining algorithm is used to discover potential patterns and associations from a large amount of data, and the machine learning model is used to classify, predict and regress the data to assess the risk factors of cardiovascular disease.

7. The big data cardiovascular disease risk monitoring system according to claim 1, characterized in that: The risk assessment module includes a risk assessment model and a risk level classification standard. The risk assessment model comprehensively assesses the patient's cardiovascular disease risk based on the results of the data analysis module, combined with clinical guidelines and expert experience. The risk level classification standard divides the patient's risk level into three levels: low risk, medium risk and high risk.

8. The big data cardiovascular disease risk monitoring system according to claim 1, characterized in that: The result display module includes a visualization interface and a report generation unit. The visualization interface displays the patient's cardiovascular disease risk assessment results in the form of charts, graphs, etc., so that the patient and the doctor can intuitively understand the patient's risk status. The report generation unit is used to generate a detailed risk assessment report, including the patient's basic information, risk factor analysis, assessment results and recommended measures.

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