CN113284621A - Hierarchical management system and method for cardiovascular disease risk assessment - Google Patents
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Abstract
The invention belongs to the field of disease risk assessment, and discloses a hierarchical management system and a method for cardiovascular disease risk assessment, wherein the hierarchical management system for cardiovascular disease risk assessment comprises the following components: the system comprises an information acquisition module, a data analysis module, a data processing module, a comprehensive evaluation module and a database. The method comprises the steps of dividing each individual according to a preset hierarchical arrangement according to the danger level of each item of data of each individual; and the comprehensive evaluation module reads in the hierarchy information, synthesizes all the data information and provides the individual in each hierarchy with the treatment scheme corresponding to the hierarchy. And the database is mainly used for storing the data stored in each module and reading the data to pass through the database. The invention realizes accurate evaluation of the risk level of the cardiovascular disease of the patient according to the input risk factor data and carotid artery ultrasonic examination data, and divides the patient into different levels to realize risk stratification of the cardiovascular disease.
Description
Technical Field
The invention belongs to the field of disease risk assessment, and particularly relates to a hierarchical management system and a hierarchical management method for cardiovascular disease risk assessment.
Background
At present, with the improvement of living standard of people, the aging of people in China is high, and the probability of occurrence of cardiovascular diseases in people, particularly middle-aged and old people, is higher and higher. The occurrence of cardiovascular diseases is the result of the combined action of various risk factors, not only are the influencing factors diverse, but also cases such as hypertension, hyperlipidemia, diabetes and the like are continuously increased in China and become the leading causes of disease burden in China, bad life styles are continuously popular, and the prevention and management forms of cardiovascular diseases are more severe. Therefore, cardiovascular disease risk assessment and risk stratification are important bases for enhancing the primary prevention and health management of cardiovascular disease.
Through the above analysis, the problems and defects of the prior art are as follows:
at present, most of the cardiovascular disease prevention systems are not comprehensive.
The cardiovascular disease risk assessment provided by the prior art is high in cost and not easy to popularize.
The current technology is incomplete in cardiovascular disease risk assessment system, incomplete in data and not comprehensive.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a hierarchical management system and a hierarchical management method for cardiovascular disease risk assessment.
The invention is realized in such a way that a hierarchical management system and a method for cardiovascular disease risk assessment are provided, the system comprises several modules, which are respectively: the system comprises an information acquisition module, a data analysis module, a data processing module, a comprehensive evaluation module and a database;
the information acquisition module is mainly used for collecting at least one item of data information about cardiovascular diseases of each individual in the evaluated crowd and storing the data in the database for storage.
And the data analysis module is connected with the database, reads the collected information, is used for analyzing and grading each individual in the crowd according to at least one item of data according to the data items which are divided in advance, obtains the danger level corresponding to each item of data after mapping the grading and the preset danger threshold value in a one-to-one correspondence manner, and stores the data in the database.
The method for obtaining the danger level corresponding to each item of data comprises the following steps:
1) wavelet packet decomposition transformation is carried out on the collected information data to obtain wavelet coefficients under different scales, and the scale coefficient of a signal HH layer is set to be zero;
2) calculating the characteristic volatility index S of certain image data of cardiovascular diseasesf(ii) a The formula is as follows:
wherein
Represents the RMS value of certain image waveform data of cardiovascular diseases,
representing the absolute average of certain image waveform data of cardiovascular diseases;
3) calculating the characteristic peak value index of certain item of image data of the cardiovascular disease, wherein the formula is as follows:
wherein xmaxWhich is indicative of the peak of the waveform,
the representation represents a root mean square value;
4) calculating the characteristic pulse index of certain image data of cardiovascular diseases, wherein the formula is as follows:
wherein xmaxWhich is indicative of the peak of the waveform,
representing the absolute average of certain image waveform data of cardiovascular diseases;
5) calculating the characteristic kurtosis index of certain image data of cardiovascular diseases, wherein the calculation formula is as follows:
wherein
6) Calculating the characteristic margin index of certain image data of cardiovascular diseases, wherein the calculation formula is as follows:
wherein
7) Calculating a Teager energy operator of certain image data characteristic of the cardiovascular disease, wherein the calculation formula is as follows:
wherein t represents the data acquisition time,
αtis the offset angle before and after the time t;
8) calculating the standard deviation according to the following calculation formula:
9) the standard deviation of the mean was calculated as follows:
10) calculating the sample circle average value of the sample according to the following calculation formula:
wherein X is a number of the samples,
S＝∑isin(angle)C＝∑icos(angle)，
res＝arctan2(S，C)；
11) splicing wavelet hierarchical decomposition characteristics and waveform characteristics of the data of each detection point;
performing feature synchronization processing on DCCA networks of any two measuring points by using a depth multi-view, performing consistency feature fusion, and processing by using an end-to-end multi-view classifier;
predicting abnormal data of a certain image of an unknown cardiovascular disease, determining a risk level, and acquiring a risk level score for the acquired risk level by utilizing a preset scoring program;
and the data processing module is connected with the database, reads in the data obtained by the data analysis module, and divides each individual according to a preset hierarchical setting according to the danger level of each item of data of each individual.
And the comprehensive evaluation module reads in the hierarchy information, synthesizes all the data information and provides the individual in each hierarchy with the treatment scheme corresponding to the hierarchy.
And the database is mainly used for storing the data stored in each module and reading the data to pass through the database.
Further, the information collection module collects data including: risk factor data and carotid ultrasound examination data.
Further, the risk factors include: blood pressure data, blood lipid data, blood glucose index, obesity index, smoking index, drinking index, family cardiovascular disease genetic data, age data, mental health index, physical activity data.
Further, the carotid artery ultrasound examination data comprises: the carotid intima-media thickness (IMT for short, less than or equal to 0.9 is normal, intima thickening is 1.0-1.2, plate forming is 1.2-1.4, carotid stenosis is more than or equal to 1.5) can be evaluated as intimal thickening, plaque forming and carotid stenosis according to the IMT thickening degree; further, the degree of arterial stenosis can be further classified into mild, moderate and severe stenosis. Degree of carotid stenosis (< 50% mild, 50% -69% moderate, more than or equal to 70% severe).
Carotid plaque is complex in composition, is not completely identical in nature, is relatively stable in some cases, and is easily ruptured in some cases, and is called vulnerable plaque. The vulnerable plaque may break the fibrous cap under the action of emotional agitation, violent exercise, alcoholism, cold and other causes, so as to form acute thrombus or cause downstream vascular embolism. Therefore, after assessing the stenosis rate of the carotid artery, it is also necessary to identify the stability of the plaque. I.e. the plaque index.
Further, the information acquisition module comprises:
an acquisition unit for acquiring at least one item of data of potential cardiovascular diseases of each individual in the population;
and the first data transmission unit is connected with the acquisition unit and is used for transmitting the collected at least one item of data to a first data storage module of a database for storage.
Further, the data analysis module comprises:
the analysis unit is connected with the first data storage module and used for grading each individual in the crowd according to the at least one item of data and according to data items;
the mapping unit is connected with the analysis unit and used for mapping the scores with a preset danger threshold value to obtain a danger grade corresponding to each item of data;
and the second data transmission unit is connected with the mapping unit and is used for transmitting the danger level of each item of data of each individual to a database and storing the danger level in a data storage module.
The data analysis module scores the severity of the risk factors according to the risk factor data, and maps the scores with a preset risk threshold value to obtain a risk grade corresponding to the risk factor data: in this embodiment, the risk threshold of the risk factor data is 3 points, that is, when the score is lower than 3 points, the risk factor data corresponding to the score is classified into a low risk level, and when the score is not lower than 3 points, the risk factor data corresponding to the score is classified into a high risk level.
The data analysis module scores the severity of the carotid artery according to the carotid artery ultrasonic examination data, and maps the carotid artery ultrasonic examination data with a preset danger threshold value to obtain a danger level corresponding to the carotid artery ultrasonic examination data; in this embodiment, the risk threshold of the carotid artery ultrasound examination data includes a first risk threshold (1.0mm) and a second risk threshold (3 minutes), the first risk threshold corresponds to the carotid artery intima-media thickness, and the second risk threshold corresponds to the plaque index; when the thickness of the middle carotid intima layer is less than 1.0mm and the plaque index is less than 3 minutes, dividing carotid artery ultrasonic examination data into low-risk levels to indicate that the carotid artery ultrasonic examination data are normal; and when the thickness of the middle carotid intima-media layer is not less than 1.0mm and/or the plaque index is not less than 3, dividing the carotid artery ultrasonic examination data into high risk levels to indicate that the carotid artery ultrasonic examination data is abnormal.
Further, the data processing module comprises:
and the layering unit is connected with the second data storage module and is used for dividing each individual according to preset hierarchical setting according to the danger level of each item of data of each individual. Specifically, the corresponding patients are classified according to preset hierarchical settings according to the risk levels corresponding to the risk factor data and the risk levels corresponding to the carotid artery ultrasound examination data. The hierarchy includes: the patients corresponding to the risk factor data in the low risk level and the patients corresponding to the carotid artery ultrasound examination data are classified into the low risk level and the patients corresponding to the risk factor data in the high risk level and the patients corresponding to the carotid artery ultrasound examination data are classified into the medium/high risk level.
And the third data transmission unit is used for transmitting the hierarchy corresponding to each individual to a third data storage module of the database for storage.
Further, the comprehensive evaluation module comprises:
and the evaluation unit is connected with the third data storage module and is used for providing a treatment scheme for each individual according to the level of each individual and the pre-trained model. Providing follow-up instructions for patients in a low risk tier; guidance for pre-treatment is provided to patients in the intermediate/high risk tier.
And the fourth data transmission unit is used for transmitting the treatment scheme corresponding to each individual to a fourth data storage module of the database for storage.
Further, the database includes: the four data modules respectively store data transmitted by the information acquisition module, the data analysis module, the data processing module and the comprehensive evaluation module, the connection of each module is also a database, and the data reading is also performed through the database.
Further, a hierarchical management method for cardiovascular disease risk assessment includes:
collecting at least one item of data of potential cardiovascular diseases of each individual in the crowd and storing the data in a database;
reading database information, scoring each individual in the crowd according to data items according to the at least one item of data, mapping the score with a preset risk threshold value to obtain a risk grade corresponding to each item of data, and storing the risk grade in a database;
reading database information, dividing each individual according to a preset hierarchical setting according to the danger level of each item of data of each individual, and storing the divided individuals in a database;
and reading database information, providing the individual with the treatment scheme corresponding to the hierarchy in each hierarchy, and storing the treatment scheme in the database.
By combining all the technical schemes, the invention has the advantages and positive effects that:
according to the method, the risk level of the patient suffering from the cardiovascular disease is accurately evaluated according to the risk factors of the transfusionis and the carotid artery ultrasonic examination data, and the acquired data is comprehensive and has high reliability; evaluating according to the data, and dividing the patients into different levels to realize the risk stratification of cardiovascular diseases; meanwhile, a training treatment scheme is provided for the patient, and the prevention of diseases and the improvement of the life style of the patient are realized. The whole system is buckled layer by layer, and the data is stored by the database independently, so that the system has a great effect on future patient review.
Wavelet packet decomposition transformation is carried out on collected information data to obtain wavelet coefficients under different scales, and the scale coefficient of a signal HH layer is set to be zero; calculating a certain image data characteristic wave index of cardiovascular diseases, calculating a certain image data characteristic peak index of cardiovascular diseases, calculating a certain image data characteristic pulse index of cardiovascular diseases, calculating a certain image data characteristic kurtosis index of cardiovascular diseases, calculating a certain image data characteristic margin index of cardiovascular diseases, calculating a certain image data characteristic Teager energy operator of cardiovascular diseases, calculating a standard deviation of an average value, calculating a sample circle average value of a sample, and splicing wavelet hierarchical decomposition characteristics and waveform characteristics of data at all detection points; performing feature synchronization processing on DCCA networks of any two measuring points by using a depth multi-view, performing consistency feature fusion, and processing by using an end-to-end multi-view classifier; predicting abnormal data of a certain image of unknown cardiovascular diseases, determining a risk level, and acquiring a risk level score for the acquired risk level by utilizing a preset scoring program.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained from the drawings without creative efforts.
Fig. 1 is a general structural diagram of a hierarchical management system for cardiovascular disease risk assessment according to an embodiment of the present invention.
FIG. 2 is a block diagram of a hierarchical management system for cardiovascular disease risk assessment according to an embodiment of the present invention.
Fig. 3 is a flowchart of the hierarchical management method for cardiovascular disease risk assessment according to the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In view of the problems in the prior art, the present invention provides a hierarchical management system and method for cardiovascular disease risk assessment, which will be described in detail with reference to the accompanying drawings.
As shown in fig. 1-2, a hierarchical management system for cardiovascular disease risk assessment is provided, which includes several modules, respectively: the system comprises an information acquisition module, a data analysis module, a data processing module, a comprehensive evaluation module and a database;
the information acquisition module is mainly used for collecting at least one item of data information about cardiovascular diseases of each individual in the evaluated crowd and storing the data in the database for storage.
And the data analysis module is connected with the database, reads the collected information, is used for analyzing and grading each individual in the crowd according to at least one item of data according to the data items which are divided in advance, obtains the danger level corresponding to each item of data after mapping the grading and the preset danger threshold value in a one-to-one correspondence manner, and stores the data in the database.
The method for obtaining the danger level corresponding to each item of data comprises the following steps:
1) wavelet packet decomposition transformation is carried out on the collected information data to obtain wavelet coefficients under different scales, and the scale coefficient of a signal HH layer is set to be zero;
2) calculating the characteristic volatility index S of certain image data of cardiovascular diseasesf(ii) a The formula is as follows:
wherein
Represents the RMS value of certain image waveform data of cardiovascular diseases,
representing the absolute average of certain image waveform data of cardiovascular diseases;
3) calculating the characteristic peak value index of certain item of image data of the cardiovascular disease, wherein the formula is as follows:
wherein xmaxWhich is indicative of the peak of the waveform,
the representation represents a root mean square value;
4) calculating the characteristic pulse index of certain image data of cardiovascular diseases, wherein the formula is as follows:
wherein xmaxWhich is indicative of the peak of the waveform,
representing the absolute average of certain image waveform data of cardiovascular diseases;
5) calculating the characteristic kurtosis index of certain image data of cardiovascular diseases, wherein the calculation formula is as follows:
wherein
6) Calculating the characteristic margin index of certain image data of cardiovascular diseases, wherein the calculation formula is as follows:
wherein
7) Calculating a Teager energy operator of certain image data characteristic of the cardiovascular disease, wherein the calculation formula is as follows:
wherein t represents the data acquisition time,
αtis the offset angle before and after the time t;
8) calculating the standard deviation according to the following calculation formula:
9) the standard deviation of the mean was calculated as follows:
10) calculating the sample circle average value of the sample according to the following calculation formula:
wherein X is a number of the samples,
S＝∑isin(angle)C＝∑icos(angle)，
res＝arctan2(S，C)；
11) splicing wavelet hierarchical decomposition characteristics and waveform characteristics of the data of each detection point;
performing feature synchronization processing on DCCA networks of any two measuring points by using a depth multi-view, performing consistency feature fusion, and processing by using an end-to-end multi-view classifier;
predicting abnormal data of a certain image of an unknown cardiovascular disease, determining a risk level, and acquiring a risk level score for the acquired risk level by utilizing a preset scoring program;
and the data processing module is connected with the database, reads in the data obtained by the data analysis module, and divides each individual according to a preset hierarchical setting according to the danger level of each item of data of each individual.
And the comprehensive evaluation module reads in the hierarchy information, synthesizes all the data information and provides the individual in each hierarchy with the treatment scheme corresponding to the hierarchy.
And the database is mainly used for storing the data stored in each module and reading the data to pass through the database.
The information acquisition module collects data including: risk factor data and carotid ultrasound examination data.
The risk factors include: blood pressure data, blood lipid data, blood glucose index, obesity index, smoking index, drinking index, family cardiovascular disease genetic data, age data, mental health index, physical activity data.
The carotid artery ultrasound examination data comprises: the carotid intima-media thickness (IMT for short, less than or equal to 0.9 is normal, intima thickening is 1.0-1.2, plate forming is 1.2-1.4, carotid stenosis is more than or equal to 1.5) can be evaluated as intimal thickening, plaque forming and carotid stenosis according to the IMT thickening degree; further, the degree of arterial stenosis can be further classified into mild, moderate and severe stenosis. Degree of carotid stenosis (< 50% mild, 50% -69% moderate, more than or equal to 70% severe).
Carotid plaque is complex in composition, is not completely identical in nature, is relatively stable in some cases, and is easily ruptured in some cases, and is called vulnerable plaque. The vulnerable plaque may break the fibrous cap under the action of emotional agitation, violent exercise, alcoholism, cold and other causes, so as to form acute thrombus or cause downstream vascular embolism. Therefore, after assessing the stenosis rate of the carotid artery, it is also necessary to identify the stability of the plaque. I.e. the plaque index.
The information acquisition module comprises:
an acquisition unit for acquiring at least one item of data of potential cardiovascular diseases of each individual in the population; including in particular risk factor data and carotid ultrasound examination data.
And the first data transmission unit is connected with the acquisition unit and is used for transmitting the collected at least one item of data to a first data storage module of a database for storage.
The data analysis module comprises:
the analysis unit is connected with the first data storage module and used for grading each individual in the crowd according to the at least one item of data and according to data items;
the mapping unit is connected with the analysis unit and used for mapping the scores with a preset danger threshold value to obtain a danger grade corresponding to each item of data;
and the second data transmission unit is connected with the mapping unit and is used for transmitting the danger level of each item of data of each individual to a database and storing the danger level in a data storage module.
The data analysis module scores the severity of the risk factors according to the risk factor data, and maps the scores with a preset risk threshold value to obtain a risk grade corresponding to the risk factor data: in this embodiment, the risk threshold of the risk factor data is 3 points, that is, when the score is lower than 3 points, the risk factor data corresponding to the score is classified into a low risk level, and when the score is not lower than 3 points, the risk factor data corresponding to the score is classified into a high risk level.
The data analysis module scores the severity of the carotid artery according to the carotid artery ultrasonic examination data, and maps the carotid artery ultrasonic examination data with a preset danger threshold value to obtain a danger level corresponding to the carotid artery ultrasonic examination data; in this embodiment, the risk threshold of the carotid artery ultrasound examination data includes a first risk threshold (1.0mm) and a second risk threshold (3 minutes), the first risk threshold corresponds to the carotid artery intima-media thickness, and the second risk threshold corresponds to the plaque index; when the thickness of the middle carotid intima layer is less than 1.0mm and the plaque index is less than 3 minutes, dividing carotid artery ultrasonic examination data into low-risk levels to indicate that the carotid artery ultrasonic examination data are normal; and when the thickness of the middle carotid intima-media layer is not less than 1.0mm and/or the plaque index is not less than 3, dividing the carotid artery ultrasonic examination data into high risk levels to indicate that the carotid artery ultrasonic examination data is abnormal.
The data processing module comprises:
and the layering unit is connected with the second data storage module and is used for dividing each individual according to preset hierarchical setting according to the danger level of each item of data of each individual. Specifically, the corresponding patients are classified according to preset hierarchical settings according to the risk levels corresponding to the risk factor data and the risk levels corresponding to the carotid artery ultrasound examination data. The hierarchy includes: the patients corresponding to the risk factor data in the low risk level and the patients corresponding to the carotid artery ultrasound examination data are classified into the low risk level and the patients corresponding to the risk factor data in the high risk level and the patients corresponding to the carotid artery ultrasound examination data are classified into the medium/high risk level.
And the third data transmission unit is used for transmitting the hierarchy corresponding to each individual to a third data storage module of the database for storage.
The comprehensive evaluation module comprises:
and the evaluation unit is connected with the third data storage module and is used for providing a treatment scheme for each individual according to the level of each individual and the pre-trained model. Providing follow-up instructions for patients in a low risk tier; guidance for pre-treatment is provided to patients in the intermediate/high risk tier.
And the fourth data transmission unit is used for transmitting the treatment scheme corresponding to each individual to a fourth data storage module of the database for storage.
The database includes: the four data modules respectively store data transmitted by the information acquisition module, the data analysis module, the data processing module and the comprehensive evaluation module, the connection of each module is also a database, and the data reading is also performed through the database.
As shown in fig. 3, the management method provided by the present invention includes:
s101: collecting at least one item of data of potential cardiovascular diseases of each individual in the crowd and storing the data in a database;
s102: reading database information, scoring each individual in the crowd according to data items according to the at least one item of data, mapping the score with a preset risk threshold value to obtain a risk grade corresponding to each item of data, and storing the risk grade in a database;
s103: reading database information, dividing each individual according to a preset hierarchical setting according to the danger level of each item of data of each individual, and storing the divided individuals in a database;
s104: and reading database information, providing the individual with the treatment scheme corresponding to the hierarchy in each hierarchy, and storing the treatment scheme in the database.
According to the method, the risk level of the patient suffering from the cardiovascular disease is accurately evaluated according to the risk factors of the transfusionis and the carotid artery ultrasonic examination data, and the acquired data is comprehensive and has high reliability; evaluating according to the data, and dividing the patients into different levels to realize the risk stratification of cardiovascular diseases; meanwhile, a training treatment scheme is provided for the patient, and the prevention of diseases and the improvement of the life style of the patient are realized. The whole system is buckled layer by layer, and the data is stored by the database independently, so that the system has a great effect on future patient review.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, and any modification, equivalent replacement, and improvement made by those skilled in the art within the technical scope of the present invention disclosed herein, which is within the spirit and principle of the present invention, should be covered by the present invention.
Claims (10)
1. A hierarchical management system for cardiovascular disease risk assessment, the hierarchical management system for cardiovascular disease risk assessment comprising: the information acquisition module is used for collecting at least one item of data of cardiovascular diseases and storing the data in a database;
the data analysis module is connected with the database, reads the collected information, is used for analyzing and grading the pre-classified data items according to at least one item of data, obtains the danger grade corresponding to each item of data after mapping the grading and the preset danger threshold value in a one-to-one correspondence manner, and stores the data in the database;
the method for obtaining the danger level corresponding to each item of data comprises the following steps:
1) wavelet packet decomposition transformation is carried out on the collected information data to obtain wavelet coefficients under different scales, and the scale coefficient of a signal HH layer is set to be zero;
2) calculating the characteristic volatility index S of certain image data of cardiovascular diseasesf(ii) a The formula is as follows:
wherein
Represents the RMS value of certain image waveform data of cardiovascular diseases,
representing the absolute average of certain image waveform data of cardiovascular diseases;
3) calculating the characteristic peak value index of certain item of image data of the cardiovascular disease, wherein the formula is as follows:
wherein xmaxWhich is indicative of the peak of the waveform,
the representation represents a root mean square value;
4) calculating the characteristic pulse index of certain image data of cardiovascular diseases, wherein the formula is as follows:
wherein xmaxWhich is indicative of the peak of the waveform,
representing the absolute average of certain image waveform data of cardiovascular diseases;
5) calculating the characteristic kurtosis index of certain image data of cardiovascular diseases, wherein the calculation formula is as follows:
wherein
6) Calculating the characteristic margin index of certain image data of cardiovascular diseases, wherein the calculation formula is as follows:
wherein
7) Calculating a Teager energy operator of certain image data characteristic of the cardiovascular disease, wherein the calculation formula is as follows:
wherein t represents the data acquisition time,
αtis the offset angle before and after the time t;
8) calculating the standard deviation according to the following calculation formula:
9) the standard deviation of the mean was calculated as follows:
10) calculating the sample circle average value of the sample according to the following calculation formula:
wherein X is a number of the samples,
S＝∑isin(angle)C＝∑icos(angle)，
res＝＝arctan2(S，C)；
11) splicing wavelet hierarchical decomposition characteristics and waveform characteristics of the data of each detection point;
performing feature synchronization processing on DCCA networks of any two measuring points by using a depth multi-view, performing consistency feature fusion, and processing by using an end-to-end multi-view classifier;
predicting abnormal data of a certain image of an unknown cardiovascular disease, determining a risk level, and acquiring a risk level score for the acquired risk level by utilizing a preset scoring program;
the data processing module is connected with the database, reads in the data obtained by the data analysis module, and divides each individual according to a preset hierarchical setting according to the danger level of each item of data of each individual;
the comprehensive evaluation module is used for reading in the hierarchy information, integrating all the data information and sending a treatment scheme instruction corresponding to the hierarchy to the individual in each hierarchy;
and the database is used for storing the data stored in each module.
2. The hierarchical management system and method for cardiovascular disease risk assessment as set forth in claim 1, wherein the information collecting module collects data including: risk factor data and carotid ultrasound examination data.
3. The hierarchical management system and method for cardiovascular disease risk assessment according to claim 2, wherein the risk factors include: blood pressure data, blood lipid data, blood glucose index, obesity index, smoking index, drinking index, family cardiovascular disease genetic data, age data, mental health index, physical activity data.
4. The hierarchical management system for cardiovascular disease risk assessment as set forth in claim 1, said carotid ultrasound examination data comprising: carotid intimal-medial thickness, based on the degree of IMT thickening, for assessment of intimal thickening, plaque formation, carotid stenosis; and further, light, medium and severe stenosis data are further analyzed according to the degree of the arterial stenosis.
5. The hierarchical management system for cardiovascular disease risk assessment as set forth in claim 1, said information collection module comprising:
an acquisition unit for acquiring at least one item of data of potential cardiovascular diseases of each individual in the population;
and the first data transmission unit is connected with the acquisition unit and is used for transmitting the collected at least one item of data to a first data storage module of a database for storage.
6. The hierarchical management system for cardiovascular disease risk assessment as set forth in claim 1, said data analysis module comprising:
the analysis unit is connected with the first data storage module and used for grading each individual in the crowd according to the at least one item of data and according to data items;
the mapping unit is connected with the analysis unit and used for mapping the scores with a preset danger threshold value to obtain a danger grade corresponding to each item of data;
and the second data transmission unit is connected with the mapping unit and is used for transmitting the danger level of each item of data of each individual to a database and storing the danger level in a data storage module.
7. The hierarchical management system for cardiovascular disease risk assessment as set forth in claim 1, said data processing module comprising:
and the layering unit is connected with the second data storage module and is used for dividing each individual according to preset hierarchical setting according to the danger level of each item of data of each individual.
And the third data transmission unit is used for transmitting the hierarchy corresponding to each individual to a third data storage module of the database for storage.
8. The hierarchical management system for cardiovascular disease risk assessment according to claim 1, wherein the comprehensive evaluation module comprises:
and the evaluation unit is connected with the third data storage module and is used for providing a treatment scheme for each individual according to the level of each individual and the pre-trained model.
And the fourth data transmission unit is used for transmitting the treatment scheme corresponding to each individual to a fourth data storage module of the database for storage.
9. The hierarchical management system for cardiovascular disease risk assessment according to claim 1, wherein the database comprises: the four data modules respectively store data transmitted by the information acquisition module, the data analysis module, the data processing module and the comprehensive evaluation module, the connection of each module is also a database, and the data reading is also performed through the database.
10. A hierarchical management method for cardiovascular disease risk assessment is characterized in that the hierarchical management method for cardiovascular disease risk assessment comprises the following steps:
collecting at least one item of data of potential cardiovascular diseases of each individual in the crowd and storing the data in a database;
reading database information, scoring each individual in the crowd according to data items according to the at least one item of data, mapping the score with a preset risk threshold value to obtain a risk grade corresponding to each item of data, and storing the risk grade in a database;
reading database information, dividing each individual according to a preset hierarchical setting according to the danger level of each item of data of each individual, and storing the divided individuals in a database;
and reading database information, providing the individual with the treatment scheme corresponding to the hierarchy in each hierarchy, and storing the treatment scheme in the database.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113707321A (en) * | 2021-08-31 | 2021-11-26 | 中山大学附属第一医院 | Hypertension system blood vessel risk assessment system and assessment method thereof |
| CN115458172A (en) * | 2022-11-11 | 2022-12-09 | 中山大学附属第一医院 | Heart risk assessment system, equipment and medium |
| CN118116596A (en) * | 2024-04-25 | 2024-05-31 | 青岛宝迈得生物科技有限公司 | Liver and gall cardiovascular disease concurrent risk assessment method based on big data analysis |
| CN118155710A (en) * | 2024-03-26 | 2024-06-07 | 中山大学孙逸仙纪念医院 | Intelligent evaluation system for risk of genetic disease |
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2021
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113707321A (en) * | 2021-08-31 | 2021-11-26 | 中山大学附属第一医院 | Hypertension system blood vessel risk assessment system and assessment method thereof |
| CN115458172A (en) * | 2022-11-11 | 2022-12-09 | 中山大学附属第一医院 | Heart risk assessment system, equipment and medium |
| CN118155710A (en) * | 2024-03-26 | 2024-06-07 | 中山大学孙逸仙纪念医院 | Intelligent evaluation system for risk of genetic disease |
| CN118116596A (en) * | 2024-04-25 | 2024-05-31 | 青岛宝迈得生物科技有限公司 | Liver and gall cardiovascular disease concurrent risk assessment method based on big data analysis |
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