CN115662653A - Chronic disease risk early warning setting and processing method of big data visualization platform - Google Patents
Chronic disease risk early warning setting and processing method of big data visualization platform Download PDFInfo
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Abstract
The invention provides a chronic disease risk early warning setting and processing method of a big data visualization platform, which comprises the following steps: the medication information module is connected with the data terminals of all levels of medical institutions to acquire medication information in the region range of all levels of medical institutions; the medication information is transmitted to a medication price trend analysis module to be processed and establish a chronic disease medication data set; the chronic disease medication data set is transmitted to a data visualization management module for data arrangement and classification, and is expressed and explained by a visualization means; and the processed and classified data are sent to a regional disease prediction module for prediction analysis, the analysis result of each chronic disease is displayed, and risk early warning is given to potential large-scale chronic diseases. The invention can know the drug price of the supervision base course by collecting the drug information of the base course, can give out early warning to potential chronic diseases in scale, and solves the problem that the old people in the villages and towns are not optimistic in drug use because medical facilities in the villages and towns are generally lagged behind in the prior art.
Description
Technical Field
The invention belongs to the field of public health, and particularly relates to a chronic disease risk early warning setting and processing method of a big data visualization platform.
Background
Chronic diseases are not only important public health problems in China but also are major health threats faced by the world at present due to the characteristics of high morbidity, high mortality, low awareness rate, low control rate, heavy economic burden of diseases and the like.
Although research shows that chronic diseases are preventable and controllable, the intelligent management and monitoring system for the drug administration big data of the chronic diseases is supposed to provide an efficient intelligent management and monitoring system for government managers.
Disclosure of Invention
The invention provides a chronic disease risk early warning setting and processing method of a big data visualization platform, aiming at the problem that the old people in the villages and towns are not optimistic in medication due to low general cultural degree and generally backward medical facilities of the villages and towns in the prior art.
A chronic disease risk early warning setting and processing method of a big data visualization platform comprises the following steps:
s1, a medication information module is connected with data terminals of all levels of medical mechanisms to acquire medication information in the region range of all levels of medical mechanisms, and medication information conditions of chronic diseases are displayed after the medication information is collected and counted;
s2, transmitting the medication information of each level of medical institution to a medication rate trend analysis module, carrying out data standardization processing so as to facilitate data sharing and medication rate trend analysis, and establishing the processed data as a chronic disease medication data set;
s3, the chronic disease medication data set is transmitted to a data visualization management module to be classified after data arrangement processing, and is expressed and explained by a visualization means so as to visually present analysis results of various chronic disease data;
and S4, the data processed and classified in the step S3 are sent to a regional disease prediction module for prediction analysis, so that the analysis result of each chronic disease in a selected time period is displayed, and risk early warning is given to potential large-scale chronic diseases.
In a preferred embodiment, an output end of the data visualization management module feeds back a signal to the medication information module to implement user interaction on a management page of the medication information module, so as to check the number of the living population of each community, the number of patients with various chronic diseases in the corresponding living population data, a change map of the patients with time, a drug sales ranking list in a given time period, and sales volumes of all levels of medical institutions corresponding to each drug.
In a preferred embodiment, the medication information of each level of medical institution includes community medication information and regional general medication information.
In a preferred embodiment, in the medication price trend analysis module, medication price trend analysis is performed on the medication price information in the medication information of each level of medical institution, and an increase reminding is sent according to the result of the medication price trend analysis.
In a preferred embodiment, in the drug rate trend analysis module, if the fluctuation of the drug rate in a given time period exceeds a threshold value, a multi-level fluctuation prompt is issued.
In a preferred embodiment, the data warping process in the data visualization management module includes filling in data and clearing out inconsistent data to reduce the size of the chronic medication data set; inconsistent data can be cleaned up using data reduction techniques.
In a preferred embodiment, the medication information counted by the medication information module comprises a community list, a standing population of each community, a chronic disease sales leaderboard of each community, the number of chronic diseases and the time of sick persons of each community, the sales volume of each type of medicine, and the sales volume of each type of medicine in medical institutions at all levels.
In a preferred embodiment, in the regional disease prediction module, a community distribution population corresponding to the population of each chronic disease patient and a variation map of the population of the chronic disease in the community selection time period are constructed to show the chronic disease condition in the community.
In a preferred embodiment, a big data visualization platform can be established according to a chronic disease risk early warning setting and processing method of the big data visualization platform, and public health regional problems can be queried, analyzed and traced according to the medication information.
Compared with the prior art, the invention has the following beneficial effects: according to the invention, a medicine medication information module, a medicine price trend analysis module, a data visualization management module and an area disease prediction module are utilized to count the number of sick people in each community of a basic level through each level of medical institutions, and a medicine medication information module is utilized to count the names of medicines used by several chronic diseases before a sales ranking list; then, a medicine price trend analysis module is used for determining the floating of medicines and medicine prices, when the fluctuation of the medicine price of a certain type of medicines in a certain period of time is large, the system can change the marking color and other multi-level reminders to warn a supervisor to find out the reason of the medicines with large fluctuation of the medicine price; then, the data is standardized by using a data visualization management module, a chronic disease medication data set is established, the regional disease prediction module performs statistics and expansion on the top ten types of the number of patients suffering from various diseases in a selected time period by using a map, so that the problem of difficult medication of middle-aged and elderly people in villages and towns is solved, and the problem of continuous spread of chronic diseases is finally solved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that those skilled in the art will appreciate that other embodiments can be obtained from the drawings provided without the benefit of the inventive faculty.
FIG. 1 is a schematic structural diagram of a big data visualization platform according to the present invention;
FIG. 2 is a schematic view of a home interface of a big data visualization platform according to the present invention;
FIG. 3 is a schematic diagram of an analysis case interface of the data visualization management module according to the present invention;
fig. 4 is a schematic diagram of a coronary heart disease case analysis interface in the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the invention provides a chronic disease risk early warning setting and processing method for a big data visualization platform, which comprises the following steps:
the method comprises the following steps that S1, a medication information module is connected with data terminals of all levels of medical institutions to obtain medication information in the area range of all levels of medical institutions, and medication information conditions of the chronic diseases are displayed after the medication information is collected and counted, wherein the medication information conditions comprise a community list, a standing population of each community, a chronic disease sales ranking list of each community, the number of chronic patients and the time of sick persons in each community, the sales volume of each type of medicine and the sales volume of each type of medicine in all levels of medical institutions.
S2, transmitting the medication information of each level of medical institution to a medication rate trend analysis module, carrying out data standardization processing so as to facilitate data sharing and medication rate trend analysis, and establishing the processed data as a chronic disease medication data set;
s3, the chronic disease medication data set is transmitted to a data visualization management module to be classified after data normalization processing, and is expressed and explained by a visualization means so as to visually present analysis results of various chronic disease data;
and S4, the data processed and classified in the step S3 are sent to a regional disease prediction module for prediction analysis, so that the analysis result of each chronic disease in a selected time period is displayed, and risk early warning is given to potential large-scale chronic diseases.
As shown in fig. 2, in the above chronic disease big data visualization platform, the specific working contents of each module are as follows:
and the data input end of the medication information module is connected with the data terminals of all levels of medical institutions to acquire the medication information of all levels of medical institutions, and the medication information of all levels of medical institutions comprises the medication information of all communities and regional whole medication information. The medication information of each community is medication information data which is input into each community port in advance; in the regional overall medication information, the platform displays regional overall chronic disease medication information according to various administrative divisions of city, district and county according to automatic positioning. The basic medication information is collected and counted, and the information condition of the chronic disease medication is displayed, so that preparation is made for further data analysis.
And the medicine price trend analysis module is used for receiving the medicine information of each level of medical institution output by the medicine information module and carrying out standardized processing, so that the data sharing and calling among the modules are facilitated, and a chronic disease medicine data set is established and sent to the data visualization management module. The drug price trend analysis module can display a drug list and the expansion mark of each type of drug, and is also provided with a contact way of a supervisor.
In a specific embodiment, the drug price trend analysis module is used for carrying out drug price trend analysis and fluctuation reminding according to the drug price information in the medication information of the medical institutions at all levels; if the amplitude of the drug price in the set time period exceeds the threshold value, multi-level reminding is sent, the marking color of the drug with the excessively high amplitude is changed in the platform, a telephone informs a supervisor, if no message is sent, the corresponding staff is further contacted, and the like, the abnormal floating condition of the drug price is detected as much as possible, the floating of the drug price is supervised, and the drug purchasing cost of the old in the towns is controlled.
And the data visualization management module is used for carrying out data arrangement processing and classification on the medication data set.
The data normalization processing comprises filling data and clearing inconsistent data to reduce the size of a data set, and various visualization means such as a mulberry-based energy flow chart, a layered area chart, a bubble chart, a data chart and the like are used for expressing and explaining, so that various analysis results of the disease data of the chronic case example can be visually presented, as shown in fig. 3. Even the data of various chronic disease conditions can be integrated for individual visual display, the data of different aspects can be displayed in a unified visual manner, and the data management is enhanced, and fig. 4 is a schematic diagram of a coronary heart disease case analysis interface.
For a data visualization management module, an averaging method and a random difference value are generally required to be used for filling, and if a variable is a discrete type, a median or a dummy variable is generally used for filling. In the actual data production process, due to human factors or other reasons, the recorded data may have inconsistency, and the inconsistent data needs to be cleaned before analysis. For example, errors in data entry can be corrected by comparison with the original record, knowledge engineering tools can also be used to detect data violating rules, and as the amount of data increases, traditional data analysis becomes very time consuming and complex, making analysis impractical. Data reduction techniques are used to obtain a reduced representation of a data set, which greatly reduces the size of the data set while approaching or maintaining the integrity of the original data, and the reduced data set is more efficiently analyzed and can produce almost the same analysis results, and common methods include: dimension reduction, dimension transformation, numerical reduction, and the like. In the present invention, it is therefore preferable to use data reduction techniques to clean up inconsistent data to reduce the size of the data set.
In a specific embodiment, the output end of the data visualization management module feeds back a signal to the medication information module, so as to implement user interaction on the management page of the medication information module. The uniform data processing is carried out in the acquisition and transmission processes of the medication information so as to facilitate data sharing and calling, and the information frame in each module can be clicked and correspondingly asynchronously request to display the called data result. The frequent population of each community, the number of patients corresponding to various chronic diseases in the data of the frequent population, the change map of the patients along with time, the drug sales ranking list in a given time period and the sales volume of each grade of medical institution corresponding to each drug can be checked on the page of the medication information module.
And the regional disease prediction module analyzes the information data processed and classified by the data visualization management module to display the analysis result of each chronic disease in a selected time period and send out early warning on potential scale chronic diseases. The regional disease prediction module can construct community distribution number corresponding to the number of patients of each chronic disease and a change map of the number of the chronic diseases in the community selection time period so as to show the community situation. In addition, the supervision organization can inquire, analyze and trace the regional problems of public health by virtue of the medicine consumption record of the chronic disease information platform. A schematic diagram of a coronary heart disease case analysis interface is shown in fig. 4.
According to the method, a medicine medication information module, a medicine price trend analysis module, a data visualization management module and a regional disease prediction module are utilized to count the number of sick people in each community, and a medicine medication information module is utilized to count names of medicines for chronic diseases in the first ten times of sale; then, a medicine price trend analysis module is used for determining the floating of medicines, when the medicines are in normal floating, the represented signs can be selected to be green, and when the price fluctuation of certain medicines in a certain period of time is large, the system can change the colors of the signs, for example, the green is adjusted to be red, so that a supervisor is warned to find out the reason of the medicines with large price fluctuation of the medicines; and then, the data is standardized by using a data visualization management module, a chronic disease medication data set is established, and the regional disease prediction module can select and utilize a bar chart to carry out statistics and expansion on the top ten types of the number of patients suffering from various diseases in two months, so that the problem of difficult medication of middle-aged and old people in villages and towns is solved, and the problem of continuous spread of chronic diseases is finally solved.
When the name of a certain community is clicked in the medication information module, the frequently-living population of the community is displayed, and meanwhile, the name of the medicine used by the chronic disease selling the first ten times in the month of the community appears, in regional integral medication information, after the supervisor clicks the frequently-living population data, the system can display the number of people suffering from various diseases in the frequently-living population data in a data visualization mode, and can display the number change condition of various diseases in the month of the year in a bar chart mode, when the supervisor clicks the name of a certain medicine, the system can display the total sales volume of the medicine in the month of the year and specific health institutions selling the medicine in the region.
In the medicine price trend analysis module, when the medicine is in normal unsteady, the sign that its representative shows for green, and when the medicine price fluctuation of a certain type of medicine in a certain period of time is great, the system can be with green adjustment for red, utilizes the system to remind relevant supervisory personnel simultaneously, does not find out the condition to the great medicine price of fluctuation in corresponding time when relevant supervisory personnel, then the system will carry out direct warning through dialling corresponding personnel's cell-phone number.
In the regional disease prediction module, the number of people who distribute a certain disease in each community and the broken line graph of the number of people who are ill the disease in the community in nearly two months can be obtained by clicking the number of people who correspond to the disease by a supervisor, so that the supervisor can know the basic situation more directly. After regional public health problems appear, the information platform can be used for tracing the root cause, time and labor are saved, when the number of people suffering from a certain disease is in a growing trend all the time or reaches a preset proportion, the system can display early warning information by marking red (in the application, the specific first few of the time period and the ranking list are selectable and screenable, the first month or two months or one year, the ranking ten and the like are exemplary explanations and are not limited to the above).
The above embodiments are only exemplary embodiments of the present application, and are not intended to limit the present application, and the protection scope of the present application is defined by the claims. Various modifications and equivalents may be made to the disclosure by those skilled in the art within the spirit and scope of the disclosure, and such modifications and equivalents should also be considered as falling within the scope of the disclosure.
Claims (9)
1. A chronic disease risk early warning setting and processing method of a big data visualization platform is characterized by comprising the following steps:
s1, a medication information module is connected with data terminals of all levels of medical institutions to acquire medication information in the region range of all levels of medical institutions, and medication information conditions of chronic diseases are displayed after the medication information is collected and counted;
s2, transmitting the medication information of each level of medical institution to a medication rate trend analysis module, carrying out data standardization processing so as to facilitate data sharing and medication rate trend analysis, and establishing the processed data as a chronic disease medication data set;
s3, the chronic disease medication data set is transmitted to a data visualization management module to be classified after data normalization processing, and is expressed and explained by a visualization means so as to visually present analysis results of various chronic disease data;
and S4, the classified data processed in the step S3 are sent to a regional disease prediction module for prediction analysis so as to display the analysis result of each chronic disease in a selected time period and send risk early warning to the potential scale chronic diseases.
2. The chronic disease risk early warning setting and processing method of the big data visualization platform as claimed in claim 1,
the output end of the data visualization management module feeds back signals to the medication information module so as to realize user interaction on a management page of the medication information module, check the standing population of each community, the number of patients corresponding to various chronic diseases in the standing population data, the change maps of the patients along with time, the medicine sales ranking list in a given time period and the sales volume of each grade of medical institution corresponding to each medicine.
3. The chronic disease risk early warning setting and processing method of the big data visualization platform as claimed in claim 1,
the medication information of each level of medical institution comprises medication information of each community and regional integral medication information.
4. The chronic disease risk early warning setting and processing method of the big data visualization platform as claimed in claim 1,
and in the drug price trend analysis module, drug price trend analysis is carried out on the drug price information in the medication information of each level of medical institution, and a rising amplitude prompt is sent according to the result of the drug price trend analysis.
5. The chronic disease risk early warning setting and processing method of the big data visualization platform as claimed in claim 4,
in the drug price trend analysis module, if the fluctuation of the drug price in a given time period exceeds a threshold value, a multi-level fluctuation prompt is sent out.
6. The chronic disease risk early warning setting and processing method of the big data visualization platform as claimed in claim 1,
the data normalization processing in the data visualization management module comprises filling data and clearing inconsistent data to reduce the scale of the chronic disease medication data set;
inconsistent data can be cleaned up using data reduction techniques.
7. The chronic disease risk early warning setting and processing method of the big data visualization platform as claimed in claim 1,
the medication information counted by the medication information module comprises a community list, the standing population of each community, a chronic disease sales ranking list of each community, the number of chronic disease patients and the time of the sick personnel of each community, the sales volume of each type of medicine and the sales volume of each type of medicine in each level of medical institution.
8. The chronic disease risk early warning setting and processing method of the big data visualization platform according to claim 1,
in the regional disease prediction module, the community distribution number corresponding to the number of patients with each chronic disease and the change map of the number of the chronic diseases in the community selection time period are constructed so as to show the chronic disease condition in the community.
9. The chronic disease risk early warning setting and processing method of the big data visualization platform according to any one of claims 1 to 8,
the big data visualization platform can be established according to the chronic disease risk early warning setting and processing method of the big data visualization platform, and public health regional problems can be inquired, analyzed and traced according to the medication information.
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| CN116313132A (en) * | 2023-05-24 | 2023-06-23 | 四川省医学科学院·四川省人民医院 | Medical management system for chronic diseases |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116313132A (en) * | 2023-05-24 | 2023-06-23 | 四川省医学科学院·四川省人民医院 | Medical management system for chronic diseases |
| CN116313132B (en) * | 2023-05-24 | 2023-08-11 | 四川省医学科学院·四川省人民医院 | Medical management system for chronic diseases |
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