CN117379009A - Early warning and monitoring system for critical cardiovascular and cerebrovascular diseases of old people in community - Google Patents
Early warning and monitoring system for critical cardiovascular and cerebrovascular diseases of old people in community Download PDFInfo
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
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Abstract
The invention discloses a warning and monitoring system for critical cardiovascular and cerebrovascular diseases of community old people, belonging to the technical field of monitoring, comprising: the data collection subsystem is used for acquiring the state information of the elderly; the data processing subsystem is used for carrying out data processing on the physical state information of the old people to obtain a physical state characteristic point data set and a state information data set of the old people; the data analysis subsystem is used for constructing a cardiovascular and cerebrovascular emergency critical illness prediction model based on the senile human body state characteristic point data set and the state information data set; and the monitoring and early warning subsystem is used for monitoring and early warning the cardiovascular and cerebrovascular diseases of the old through the cardiovascular and cerebrovascular emergency critical illness prediction model. According to the invention, the physical condition information of the old people can be timely obtained through intelligent monitoring devices such as the intelligent bracelet and the like, and the information is collected and then analyzed, so that the monitoring difficulty is effectively reduced.
Description
Technical Field
The invention belongs to the field of monitoring, and particularly relates to a warning and monitoring system for cardiovascular and cerebrovascular critical diseases of old people in communities.
Background
At present, cardiovascular and cerebrovascular acute critical diseases are common but serious health problems in the old people in communities. These include heart disease, stroke, and other vascular related emergency conditions. The cardiovascular and cerebrovascular emergency critical conditions in the elderly require timely medical intervention, as these conditions can lead to serious health consequences, including disability and even death. In communities, improving cardiovascular health consciousness of elderly people, regular physical examination, maintaining a healthy lifestyle is important, such as healthy diet, moderate exercise, and smoking cessation. In addition, timely medical visits and emergency rescue are critical to the management of cardiovascular and cerebrovascular critical conditions to reduce potential risks and damage.
However, current monitoring of the elderly population requires considerable economic resources, including equipment, training personnel, and medical professionals, which can burden some communities and medical institutions. The health of the elderly population varies widely, some may require more frequent monitoring, while others may require less, creating a more tricky situation in the monitoring process.
Disclosure of Invention
The invention aims to provide a warning and monitoring system for cardiovascular and cerebrovascular critical diseases of old people in communities, which aims to solve the problems in the prior art.
In order to achieve the purpose, the invention provides a community old people cardiovascular and cerebrovascular emergency critical illness early warning and monitoring system, which comprises:
the data collection subsystem is used for acquiring the state information of the elderly;
the data processing subsystem is connected with the data collecting subsystem and is used for carrying out data processing on the physical state information of the old people to obtain a physical state characteristic point data set and a state information data set of the old people;
the data analysis subsystem is connected with the data processing subsystem and is used for constructing a cardiovascular and cerebrovascular emergency critical illness prediction model based on the state characteristic point data set and the state information data set of the elderly;
and the monitoring and early warning subsystem is connected with the data analysis subsystem and is used for monitoring and early warning the cardiovascular and cerebrovascular diseases of the old through the cardiovascular and cerebrovascular emergency critical disease prediction model.
Preferably, the data collection subsystem comprises:
the physical examination data collection module is used for collecting senile human examination data through the community physical examination database;
the portable data collection module is used for collecting daily body data information of the old through portable data;
and the matching module is used for matching and integrating the old person detection data and the old person daily body data information to obtain the old person body state information.
Preferably, the matching module includes:
the attribute analysis unit is used for dividing the old person detection data and the old person daily body data information into physical examination attribute data and daily attribute data;
the weighting calculation unit is used for calculating attribute similarity of the physical examination attribute data and the daily attribute data to obtain a weighting value calculation result;
and the data integration unit is used for setting a threshold value, matching the physical examination attribute data with the daily attribute data based on the weighted value calculation result and the threshold value, and integrating to obtain the physical state information of the old people.
Preferably, the data processing subsystem comprises:
the characteristic point identification module is used for acquiring a characteristic point data set of the physical state of the old people according to the physical state information of the old people;
the preprocessing module is used for preprocessing the state information of the aged based on the characteristic structure of neural network learning and training to obtain the state information data set.
Preferably, the preprocessing module includes:
a grouping unit for grouping the physical state information of the aged into a plurality of groups;
the verification unit is used for carrying out cross verification on a plurality of groups of data to obtain a verified data set;
and the arrangement unit is used for carrying out logic sequence analysis on the continuity data in the verified data set, and carrying out logic address arrangement on discrete data to obtain the state information data set.
Preferably, the data analysis subsystem comprises:
the three-dimensional model construction module is used for constructing a human body three-dimensional model based on the senile human body state characteristic point data set;
the prediction model calculation module is used for training a lightweight neural network model, inputting the state information data set into the lightweight neural network model for calculation, and obtaining a cardiovascular and cerebrovascular change prediction result;
and the data simulation module is used for importing the cardiovascular and cerebrovascular change prediction result into the human body three-dimensional model for simulation to obtain the cardiovascular and cerebrovascular emergency critical illness prediction model.
Preferably, the three-dimensional model building module includes:
the vector integration unit is used for acquiring the critical three-dimensional positions of the cardiovascular and cerebrovascular vessels of the aged based on the body state characteristic points of the aged;
a three-dimensional coordinate confirming unit for confirming a position coordinate through the key three-dimensional position;
and the construction unit is used for constructing a human body three-dimensional model through the position coordinates and the human body state characteristic points of the elderly.
Preferably, the data simulation module includes:
the abnormality confirmation unit is used for acquiring the abnormal condition data of the elderly through the cardiovascular and cerebrovascular variation prediction result;
the simulation unit is used for inputting the abnormal condition data of the elderly human body into the three-dimensional model of the human body to simulate pathological changes and obtain the cardiovascular and cerebrovascular emergency critical illness prediction model.
Preferably, the monitoring and early warning subsystem comprises:
the daily information collection module is used for collecting daily physiological information of the old;
and the abnormality early warning module is used for calculating the risk degree of the daily physiological information of the old based on the cardiovascular and cerebrovascular emergency critical illness prediction model, and if the risk degree is greater than or equal to a risk threshold value, early warning is carried out and the risk degree is displayed in the human three-dimensional model.
The invention has the technical effects that:
according to the invention, the physical condition information of the old people can be timely obtained through intelligent monitoring devices such as the intelligent bracelet and the like, and then the information is collected and analyzed, so that the monitoring difficulty is effectively reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application, illustrate and explain the application and are not to be construed as limiting the application. In the drawings:
fig. 1 is a schematic diagram of an early warning and monitoring system in an embodiment of the invention.
Detailed Description
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Example 1
As shown in fig. 1, the embodiment provides a system for early warning and monitoring cardiovascular and cerebrovascular critical diseases of old people in communities, which comprises:
the data collection subsystem is used for acquiring the state information of the elderly;
the data collection subsystem is responsible for capturing physical state information of the elderly. The system consists of a physical examination data collection module and a portable data collection module, wherein the physical examination data collection module is used for collecting information of the aged through daily physical data information. The data are matched and integrated through the matching module to form information which comprehensively reflects the physical state of the old people.
The data processing subsystem is connected with the data collecting subsystem and is used for carrying out data processing on the physical state information of the old people to obtain a physical state characteristic point data set and a state information data set of the old people;
the data processing subsystem is connected with the data collecting subsystem and is responsible for processing the collected senile physical state information. The system extracts a characteristic point data set of the body state of the elderly through a characteristic point identification module, and then processes the characteristic point data set through a preprocessing module. The preprocessing module includes a grouping unit, a verifying unit, and an arranging unit that group, verify, and arrange data to obtain a status information data set.
The data analysis subsystem is connected with the data processing subsystem and is used for constructing a cardiovascular and cerebrovascular emergency critical illness prediction model based on the state characteristic point data set and the state information data set of the elderly;
the data analysis subsystem is connected with the data processing subsystem and is responsible for modeling and predicting the state characteristic point data set and the state information data set of the aged. The three-dimensional model building module is used for building a human body three-dimensional model, and the prediction model calculation module is used for training a lightweight neural network model. The module calculates the state information dataset as input to obtain a predicted outcome of the cardiovascular and cerebrovascular changes. And finally, the prediction result is imported into a human body three-dimensional model through a data simulation module to simulate, so as to form a cardiovascular and cerebrovascular emergency critical disease prediction model.
And the monitoring and early warning subsystem is connected with the data analysis subsystem and is used for monitoring and early warning the cardiovascular and cerebrovascular diseases of the old through the cardiovascular and cerebrovascular emergency critical disease prediction model.
The monitoring and early warning subsystem is connected with the data analysis subsystem and is responsible for carrying out real-time monitoring and early warning on the cardiovascular and cerebrovascular conditions of the old. The system collects daily physiological information of the elderly through a daily information collection module, and inputs the information into an abnormality early warning module for risk calculation. If the risk exceeds the risk threshold, the early warning module sends out an early warning signal and reminds the old and family members to seek medical advice in time through the alarm and the short message notifier. In addition, the monitoring and early warning subsystem also has a remote consultation function, can remotely transmit the physical state information of the old and the cardiovascular and cerebrovascular critical illness prediction model to a superior medical institution, and receives remote diagnosis and evaluation of a professional doctor.
Preferably, the data collection subsystem comprises:
the physical examination data collection module is used for collecting senile human examination data through the community physical examination database;
the portable data collection module is used for collecting daily body data information of the old through portable data;
and the matching module is used for matching and integrating the old person detection data and the old person daily body data information to obtain the old person body state information.
Preferably, the matching module includes:
the attribute analysis unit is used for dividing the old person detection data and the old person daily body data information into physical examination attribute data and daily attribute data;
the weighting calculation unit is used for calculating attribute similarity of the physical examination attribute data and the daily attribute data to obtain a weighting value calculation result;
and the data integration unit is used for setting a threshold value, matching the physical examination attribute data with the daily attribute data based on the weighted value calculation result and the threshold value, and integrating to obtain the physical state information of the old people.
The data integration unit constructs a data storage frame according to the time sequence relation of the data acquired in each simulation process;
the data storage architecture is an XML architecture, in the XML architecture, according to the progressive sequence of the data acquisition time, the acquisition time is taken as a constraint condition, the state parameter is taken as a detailed description under the time, and simultaneously, the prediction result and the detection result of the same acquisition time are correspondingly stored according to the constraint condition.
The data to be matched comprises attribute data to be matched, the attribute data to be matched are a plurality of attribute data, the local database comprises a plurality of database attribute data, similarity calculation and weighted value calculation are carried out on the data to be matched, and the relation between the weighted value and a threshold value is compared; the method comprises the following steps:
firstly, importing data to be matched, and determining a plurality of attributes of the data to be matched so as to acquire attribute data to be matched;
and determining a plurality of attributes of the local database according to the local database so as to acquire database attribute data, and extracting the database attribute data.
Judging whether the set of the attribute data of the database and the set of the attribute data to be matched are empty sets or not, and if not, calculating an attribute similarity weighted value; if the attribute data set is the empty set, the next step is carried out, and whether the attribute data set to be matched and the matching data set are the empty set is judged;
calculating a reference threshold;
judging whether the attribute similarity weighted value is larger than a threshold value, if so, matching, and importing the data to be matched into a matching database; otherwise, the data is not matched, and the data to be matched is stored in the doubtful database.
Preferably, the data processing subsystem comprises:
the characteristic point identification module is used for acquiring a characteristic point data set of the physical state of the old people according to the physical state information of the old people;
the preprocessing module is used for preprocessing the state information of the aged based on the characteristic structure of neural network learning and training to obtain the state information data set.
Preferably, the preprocessing module includes:
a grouping unit for grouping the physical state information of the aged into a plurality of groups;
the verification unit is used for carrying out cross verification on a plurality of groups of data to obtain a verified data set;
and the arrangement unit is used for carrying out logic sequence analysis on the continuity data in the verified data set, and carrying out logic address arrangement on discrete data to obtain the state information data set.
Preferably, the data analysis subsystem comprises:
the three-dimensional model construction module is used for constructing a human body three-dimensional model based on the senile human body state characteristic point data set;
the prediction model calculation module is used for training a lightweight neural network model, inputting the state information data set into the lightweight neural network model for calculation, and obtaining a cardiovascular and cerebrovascular change prediction result;
and the data simulation module is used for importing the cardiovascular and cerebrovascular change prediction result into the human body three-dimensional model for simulation to obtain the cardiovascular and cerebrovascular emergency critical illness prediction model.
Preferably, the three-dimensional model building module includes:
the vector integration unit is used for acquiring the critical three-dimensional positions of the cardiovascular and cerebrovascular vessels of the aged based on the body state characteristic points of the aged;
a three-dimensional coordinate confirming unit for confirming a position coordinate through the key three-dimensional position;
and the construction unit is used for constructing a human body three-dimensional model through the position coordinates and the human body state characteristic points of the elderly.
Preferably, the data simulation module includes:
the abnormality confirmation unit is used for acquiring the abnormal condition data of the elderly through the cardiovascular and cerebrovascular variation prediction result;
the simulation unit is used for inputting the abnormal condition data of the elderly human body into the three-dimensional model of the human body to simulate pathological changes and obtain the cardiovascular and cerebrovascular emergency critical illness prediction model.
The three-dimensional model construction module is a unit for constructing a three-dimensional model of a human body according to a state characteristic point data set of the human body of the old people, and comprises a vector integration unit, a three-dimensional coordinate confirmation unit and a construction unit. The vector integration unit can acquire the key three-dimensional position information of the cardiovascular and cerebrovascular according to the physical state characteristic points of the old. The three-dimensional coordinate confirmation unit may confirm the corresponding position coordinates through the key three-dimensional positions. Finally, the construction unit constructs a three-dimensional model of the human body through the position coordinates and the characteristic point data of the state of the human body of the old people.
The prediction model calculation module is used for training a lightweight neural network model and inputting a state information data set into the model for calculation. In this way, we can obtain the prediction result of cardiovascular and cerebrovascular changes. The function of this module is primarily dependent on the characteristics of the neural network model, which automatically learns from a large amount of data and finds patterns therein.
The data simulation module can guide the prediction result of the cardiovascular and cerebrovascular changes into the three-dimensional model of the human body for simulation, thereby obtaining the prediction model of the acute critical illness of the cardiovascular and cerebrovascular diseases. This module includes an anomaly confirmation unit and an analog unit. The abnormality confirmation unit can obtain the data of the abnormal condition of the aged through the cardiovascular and cerebrovascular variation prediction result. The simulation unit can input the abnormal condition data of the elderly human body into a human body three-dimensional model, simulate pathological changes and obtain a cardiovascular and cerebrovascular emergency critical disease prediction model.
Preferably, the monitoring and early warning subsystem comprises:
the daily information collection module is used for collecting daily physiological information of the old;
and the abnormality early warning module is used for calculating the risk degree of the daily physiological information of the old based on the cardiovascular and cerebrovascular emergency critical illness prediction model, and if the risk degree is greater than or equal to a risk threshold value, early warning is carried out and the risk degree is displayed in the human three-dimensional model.
The foregoing is merely a preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the technical scope of the present application should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. The utility model provides a cardiovascular and cerebrovascular emergency critical illness early warning monitoring system of old crowd of community, its characterized in that includes:
the data collection subsystem is used for acquiring the state information of the elderly;
the data processing subsystem is connected with the data collecting subsystem and is used for carrying out data processing on the physical state information of the old people to obtain a physical state characteristic point data set and a state information data set of the old people;
the data analysis subsystem is connected with the data processing subsystem and is used for constructing a cardiovascular and cerebrovascular emergency critical illness prediction model based on the state characteristic point data set and the state information data set of the elderly;
and the monitoring and early warning subsystem is connected with the data analysis subsystem and is used for monitoring and early warning the cardiovascular and cerebrovascular diseases of the old through the cardiovascular and cerebrovascular emergency critical disease prediction model.
2. The community-aged population cardiovascular and cerebrovascular emergency critical illness early-warning and monitoring system of claim 1, wherein the data collection subsystem comprises:
the physical examination data collection module is used for collecting senile human examination data through the community physical examination database;
the portable data collection module is used for collecting daily body data information of the old through portable data;
and the matching module is used for matching and integrating the old person detection data and the old person daily body data information to obtain the old person body state information.
3. The community-aged population cardiovascular and cerebrovascular emergency critical illness early-warning and monitoring system according to claim 2, wherein the matching module comprises:
the attribute analysis unit is used for dividing the old person detection data and the old person daily body data information into physical examination attribute data and daily attribute data;
the weighting calculation unit is used for calculating attribute similarity of the physical examination attribute data and the daily attribute data to obtain a weighting value calculation result;
and the data integration unit is used for setting a threshold value, matching the physical examination attribute data with the daily attribute data based on the weighted value calculation result and the threshold value, and integrating to obtain the physical state information of the old people.
4. The community-aged population cardiovascular and cerebrovascular emergency critical illness early-warning and monitoring system according to claim 1, wherein the data processing subsystem comprises:
the characteristic point identification module is used for acquiring a characteristic point data set of the physical state of the old people according to the physical state information of the old people;
the preprocessing module is used for preprocessing the state information of the aged based on the characteristic structure of neural network learning and training to obtain the state information data set.
5. The community-aged population cardiovascular and cerebrovascular emergency critical illness early-warning and monitoring system of claim 4, wherein the preprocessing module comprises:
a grouping unit for grouping the physical state information of the aged into a plurality of groups;
the verification unit is used for carrying out cross verification on a plurality of groups of data to obtain a verified data set;
and the arrangement unit is used for carrying out logic sequence analysis on the continuity data in the verified data set, and carrying out logic address arrangement on discrete data to obtain the state information data set.
6. The community-aged population cardiovascular and cerebrovascular emergency critical illness early-warning and monitoring system of claim 4, wherein the data analysis subsystem comprises:
the three-dimensional model construction module is used for constructing a human body three-dimensional model based on the senile human body state characteristic point data set;
the prediction model calculation module is used for training a lightweight neural network model, inputting the state information data set into the lightweight neural network model for calculation, and obtaining a cardiovascular and cerebrovascular change prediction result;
and the data simulation module is used for importing the cardiovascular and cerebrovascular change prediction result into the human body three-dimensional model for simulation to obtain the cardiovascular and cerebrovascular emergency critical illness prediction model.
7. The community-aged population cardiovascular and cerebrovascular emergency critical illness early-warning and monitoring system according to claim 6, wherein the three-dimensional model construction module comprises:
the vector integration unit is used for acquiring the critical three-dimensional positions of the cardiovascular and cerebrovascular vessels of the aged based on the body state characteristic points of the aged;
a three-dimensional coordinate confirming unit for confirming a position coordinate through the key three-dimensional position;
and the construction unit is used for constructing a human body three-dimensional model through the position coordinates and the human body state characteristic points of the elderly.
8. The community-aged population cardiovascular and cerebrovascular emergency critical illness early-warning and monitoring system of claim 6, wherein the data simulation module comprises:
the abnormality confirmation unit is used for acquiring the abnormal condition data of the elderly through the cardiovascular and cerebrovascular variation prediction result;
the simulation unit is used for inputting the abnormal condition data of the elderly human body into the three-dimensional model of the human body to simulate pathological changes and obtain the cardiovascular and cerebrovascular emergency critical illness prediction model.
9. The community-aged population cardiovascular and cerebrovascular emergency critical illness early-warning and monitoring system according to claim 1, wherein the monitoring and early-warning subsystem comprises:
the daily information collection module is used for collecting daily physiological information of the old;
and the abnormality early warning module is used for calculating the risk degree of the daily physiological information of the old based on the cardiovascular and cerebrovascular emergency critical illness prediction model, and if the risk degree is greater than or equal to a risk threshold value, early warning is carried out and the risk degree is displayed in the human three-dimensional model.
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