CN114668371A - Sudden Death Warning System - Google Patents

Abstract

The invention discloses a sudden death early warning system which comprises a user terminal and an early warning cloud platform, wherein the early warning cloud platform comprises an intelligent analysis module and a risk evaluation module, and the user terminal is configured to acquire low-frequency sudden death risk factor information, medium-frequency sudden death risk factor information and high-frequency sudden death risk factor information of a user; the intelligent analysis module is configured to calculate the sudden death risk index of the user according to the low-frequency sudden death risk factor information, the medium-frequency sudden death risk factor information and the high-frequency sudden death risk factor information; the risk evaluation module is configured to evaluate the sudden death risk trend of the user through the corresponding historical sudden death risk index, so that the user can conduct health management and reduce the sudden death risk.

Description

Sudden Death Warning System

technical field

The invention relates to the technical field of personal health risk assessment, in particular to a sudden death early warning system.

Background technique

Sudden cardiac death refers to unexpected, non-violent natural death caused by various cardiac causes, which is characterized by rapid and unpredictable death. Sudden loss of consciousness can occur. The main cause of sudden cardiac death is the change of hemodynamics. When the coronary blood vessels of the patient are blocked, the myocardial metabolism is abnormal, and the autonomic nervous tension changes, it is easy to cause sudden cardiac death in the patient. At present, there is no effective treatment for sudden cardiac death, and early prevention and risk assessment are particularly important in reducing the risk of sudden cardiac death.

SUMMARY OF THE INVENTION

In view of the deficiencies in the prior art, the present invention proposes a sudden death early warning system, which can evaluate the user's sudden death risk trend to reduce the risk of sudden death. The specific technical scheme is as follows:

A sudden death early warning system, in a first achievable manner, includes:

a user terminal, configured to collect low-frequency sudden death risk factor information, intermediate-frequency sudden death risk factor information, and high-frequency sudden death risk factor information of the user;

The early warning cloud platform is equipped with an intelligent analysis module and a risk assessment module;

The intelligent analysis module is configured to calculate the sudden death risk index of the user according to the low-frequency sudden death risk factor information, the medium-frequency sudden death risk factor information, the high-frequency sudden death risk factor information, and the corresponding weights of each sudden death risk factor;

The risk assessment module is configured to assess the sudden death risk trend of the user through a corresponding historical sudden death risk index.

In combination with the first implementable manner, in the second implementable manner, the user terminal includes a risk factor selection module configured to select the sudden death risk factor to be collected.

In combination with the first achievable manner, in the third achievable manner, the low-frequency sudden death risk factor information includes the user's personal basic information, long-term living habit information, long-term medication habit information, long-term physical symptom information, and family disease history information. personal medical history information;

The intermediate frequency sudden death risk factor information includes the user's short-term living habit information, short-term medication habit information, short-term physical symptom information, and physical examination report parameter information;

The high-frequency sudden death risk factor information includes the user's real-time physiological characteristic information, voice information, and daily living habit information.

In combination with the third achievable manner, in the fourth achievable manner, the weights corresponding to sudden death risk factors include:

Sudden Death Risk Factors Weights daily stress situation 0.37418 daily emotional state 0.33929 daily sleep quality 0.19337 daily diet quality 0.09317 daily smoking 0.17205 daily medication 0.07929 Daily electronic entertainment 0.12132 daily exercise 0.29734 daily alcohol intake 0.32999 real-time heart rate 0.20008 Real-time ECG 0.23288 daily physical symptoms 0.17776 blood pressure 0.20008 blood oxygen 0.07999 blood sugar 0.10031 voice 0.00889

In combination with the second achievable manner, in the fifth achievable manner, the early warning cloud platform further includes a risk factor sorting module, and the risk factor sorting module is configured to perform the sudden death risk factors selected by the risk factor selection module. sort.

With reference to the fifth implementable manner, in the sixth implementable manner, the risk factor ranking module includes:

a data collection unit, configured to collect sudden death risk factor data corresponding to sudden death risk factors selected by multiple users, and construct a sudden death risk factor data set;

a cluster analysis unit, configured to use a clustering algorithm to perform cluster analysis on the sudden death risk factor data set;

The risk factor ranking unit is configured to, based on the analysis result of the cluster analysis unit, use a random forest model to rank the importance of all sudden death risk factors.

In combination with the first achievable manner, in a seventh achievable manner, the intelligent analysis module includes:

The index calculation unit is configured to calculate the user's low-frequency sudden death risk index, intermediate-frequency sudden death risk index, and high-frequency sudden death risk index respectively according to the low-frequency sudden death risk factor information, the medium-frequency sudden death risk factor information, and the high-frequency sudden death risk factor information;

The comprehensive calculation unit is configured to calculate the sudden death risk index of the user by combining the low frequency sudden death risk index, the medium frequency sudden death risk index, the high frequency sudden death risk index, and the corresponding weight coefficients.

With reference to the seventh implementable manner, in the eighth implementable manner, the index calculation unit includes:

an information collection unit, configured to collect questionnaire data of a plurality of experts;

The matrix construction unit is configured to construct the fuzzy adjacency matrix and the fuzzy reachability matrix of sudden death risk factors through the collected questionnaire data;

a weight calculation unit, configured to perform weight analysis according to the threshold distribution of the fuzzy adjacency relationship matrix and the fuzzy reachability matrix to obtain the weight of each sudden death risk factor;

The risk index calculation unit is configured to calculate the user's low frequency sudden death risk index and intermediate frequency sudden death risk according to the low frequency sudden death risk factor information, the medium frequency sudden death risk factor information and the high frequency sudden death risk factor information based on the weight of the corresponding sudden death risk factor. Index and High Frequency Sudden Death Risk Index.

With reference to the eighth implementable manner, in the ninth implementable manner, the user terminal includes a weight adjustment module, and the weight adjustment module is configured to adjust the weights of various sudden death risk factors obtained by the weight calculation unit. .

In combination with the first achievable manner, in a tenth achievable manner, the risk assessment module includes:

The index sequence construction unit is configured to obtain the historical sudden death risk index of the user, and sort it according to the obtaining time of the sudden death risk index, so as to obtain the user's early warning index sequence;

an index sequence processing unit, configured to perform logarithmic processing on the historical sudden death risk index in the early warning index sequence to obtain a logarithmic risk change rate sequence;

an early warning model construction unit, configured to construct an autoregressive conditional heteroscedastic regression model according to the logarithmic risk change rate sequence;

an early warning index prediction unit, configured to predict the user's future sudden death early warning index through the autoregressive conditional heteroskedastic regression model;

The sudden death risk assessment unit is configured to compare the future sudden death early warning index with the historical sudden death risk index, and evaluate the sudden death risk trend of the user.

In combination with the first implementable manner, in an eleventh implementable manner, the user terminal further includes an intelligent push module, and the intelligent push module is configured to push corresponding health information to the user according to the assessment result of the risk assessment module. knowledge information.

With reference to the first implementable manner, in a twelfth implementable manner, the user terminal further includes an alarm module, and the alarm module is configured to send alarm information to the user according to the evaluation result of the risk assessment module.

In combination with the first implementable manner, in a thirteenth implementable manner, a wearable device is further included, and the wearable device is used to collect real-time physiological feature information of the user.

Beneficial effect: by adopting the sudden death early warning system of the present invention, various risk factor information of the user about the risk of sudden death can be collected through the set user terminal, and the collected risk factor information can be analyzed through the set intelligent analysis module, and the user's future risk factor information can be obtained. Sudden death risk index, the set risk assessment module can combine the predicted sudden death risk index and the user's past sudden death risk index to evaluate the user's sudden death risk trend, so that the user can conduct health management and reduce the risk of sudden death.

Description of drawings

In order to describe the specific embodiments of the present invention more clearly, the accompanying drawings required for the specific embodiments will be briefly introduced below. In all the drawings, elements or sections are not necessarily drawn to actual scale.

FIG. 1 is a system block diagram of a sudden death early warning system provided by an embodiment of the present invention;

2 is a system block diagram of a risk factor ranking module provided by an embodiment of the present invention;

3 is a system block diagram of an index calculation unit provided by an embodiment of the present invention;

4 is a system block diagram of a risk assessment module provided by an embodiment of the present invention;

5 is a system block diagram of a user terminal provided by an embodiment of the present invention;

6 is a schematic diagram of the distribution of the fuzzy adjacency matrix threshold and the fuzzy reachable matrix threshold;

7 is a system interface of an early warning system provided by an embodiment of the present invention;

8 is a system interface of a risk factor selection module provided by an embodiment of the present invention;

FIG. 9 is a quantification column of sudden death risk factors provided by an embodiment of the present invention.

Detailed ways

Embodiments of the technical solutions of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only used to more clearly illustrate the technical solutions of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

The system block diagram of the sudden death early warning system shown in Figure 1, the early warning system includes:

a user terminal, configured to collect low-frequency sudden death risk factor information, intermediate-frequency sudden death risk factor information, and high-frequency sudden death risk factor information of the user;

The early warning cloud platform is equipped with an intelligent analysis module and a risk assessment module;

The intelligent analysis module is configured to calculate the sudden death risk index of the user according to the low-frequency sudden death risk factor information, the medium-frequency sudden death risk factor information, the high-frequency sudden death risk factor information, and the corresponding weights of each sudden death risk factor;

The risk assessment module is configured to assess the sudden death risk trend of the user through a corresponding historical sudden death risk index.

Specifically, the early warning system consists of a user terminal and an early warning cloud platform. The user terminal includes an information collection module, and the information collection module can collect low-frequency sudden death risk factor information, intermediate-frequency sudden death risk factor information and high-frequency sudden death risk factors related to users. information, and upload the collected information to the early warning cloud platform. The early warning cloud platform includes an intelligent analysis module and a risk assessment module. The intelligent analysis module can analyze the user's sudden death risk according to all the risk factor information collected by the user terminal, and obtain the user's future sudden death risk index. The risk assessment module can evaluate the user's sudden death risk trend according to the user's future sudden death risk index and the past historical sudden death risk index, so as to facilitate the user's health management.

The specific composition of the early warning system will be described in detail below with reference to FIGS. 2 to 9 .

In this embodiment, optionally, the low-frequency sudden death risk factor information collected by the user terminal includes the user's living habits, medication habits, personal medical history, and the like. The user terminal may collect information such as the user's living habits and medication habits through a questionnaire survey. For personal medical history, the user terminal can establish a connection with the management system of the hospital, so as to directly retrieve information such as the personal medical history of the user from the management system of the hospital.

The risk factor information of intermediate frequency sudden death includes information such as the user's short-term living habits, short-term medication habits, short-term physical symptoms and physical examination reports. Users can enter information such as short-term living habits, short-term medication habits, and short-term physical symptoms through the user terminal. For the medical examination report, the user terminal can directly retrieve from the hospital's management system, such as electrocardiogram data, blood test parameters, and medical image data.

Users can fill in information such as living habits through the input and output devices of the user terminal. When filling in information such as short-term living habits and short-term medication habits, the user terminal can display these sudden death risk factors in the form of quantitative columns. Information such as living habits is entered in the quantification column, so that each sudden death risk factor information can be quickly and manually entered.

Specifically, as shown in FIG. 9 , the user terminal can divide the quantitative evaluation of sudden death risk factors into five levels, and display them in the form of quantitative columns. The first-level evaluation corresponds to the lowest quantitative column, and the fifth-level evaluation corresponds to the highest quantitative column. column.

High-frequency sudden death risk factor information includes the user's real-time physiological characteristic information, such as real-time ECG, real-time heart rate, blood pressure, blood sugar, blood oxygen, etc., as well as the user's daily living habits, such as exercise, diet, alcohol consumption, smoking situation etc. The user terminal may collect the real-time physiological feature information of the user through the wearable device for monitoring the physiological feature information. For information such as the user's living habits of the day, the user can fill in the information through a terminal such as a mobile phone and send it to the user terminal.

In this embodiment, optionally, the user terminal includes a risk factor selection module configured to select sudden death risk factors to be collected. As shown in Figure 8, the user terminal is provided with a risk factor selection module. The user can select the risk factor of sudden death that exists in himself through the risk factor selection module and send it to the early warning cloud platform, so that the early warning cloud platform can formulate a personalized sudden death risk assessment method. Personalized sudden death early warning assessment results.

In this embodiment, optionally, the early warning cloud platform further includes a risk factor sorting module, and the risk factor sorting module is configured to sort the sudden death risk factors selected by the risk factor selection module. Users can prioritize the risk factors that they focus on, which is more conducive to the use of the user's mobile terminal.

In this embodiment, optionally, the risk factor sorting module includes:

a data collection unit, configured to collect sudden death risk factor data of multiple users, and construct a sudden death risk factor data set;

a cluster analysis unit, configured to use a clustering algorithm to perform cluster analysis on the sudden death risk factor data set;

The risk factor ranking unit is configured to, based on the analysis result of the cluster analysis unit, use a random forest model to rank the importance of all sudden death risk factors.

Specifically, the risk factor ranking module is composed of a data collection unit, a cluster analysis unit and a risk factor ranking unit. Among them, the data collection unit can collect the sudden death risk factor data of a plurality of users to construct a sudden death risk factor data set. The data acquisition unit can directly retrieve the risk factor information of multiple users from the user terminal, so as to obtain the sudden death risk factor data of multiple users. The retrieved sudden death risk factor data includes the user's frequency sudden death risk factor data, intermediate frequency sudden death risk factor data data and high-frequency sudden death risk factor data.

The cluster analysis unit may use a clustering algorithm, such as K-means clustering algorithm, to perform cluster analysis on the data in the sudden death risk factor data set constructed by the data acquisition unit, and classify the sudden death risk factor data in the sudden death risk factor data set as low sudden death risk factors. Risk data and sudden death high risk data.

Specifically, first, 2 data points are randomly divided into 2 clusters from the sudden death risk factor data set, and the 2 points are regarded as the centroid of the current cluster. Determines into which cluster the next given input data point will be divided based on the centroid distance between each next point and these 2 initial points. After all points are classified, recalculate the centroids of all clusters, and then calculate the distance from each point to the centroid again. This process is repeated until the convergence state is satisfied.

The risk factor sorting unit can use the random forest importance sorting method to sort the clustering results of the clustering analysis unit, specifically:

First, a number of samples are selected from the sudden death risk factor data set as a training set by the method of sampling and replacement; then, a decision tree is generated by using the training set obtained by sampling. In each generated node, several sudden death risk factors are randomly selected to divide the training set, and the best divided sudden death risk factors are found. Repeat the steps for many times to build multiple decision trees; after that, determine the contribution value of each sudden death risk factor to each decision tree, take the average value, and record it as the weight of each sudden death risk factor. Finally, all sudden death risk factors are ranked according to the weight of each sudden death risk factor.

In this embodiment, optionally, the intelligent analysis module includes:

The index calculation unit is configured to calculate the user's low-frequency sudden death risk index, intermediate-frequency sudden death risk index, and high-frequency sudden death risk index respectively according to the low-frequency sudden death risk factor information, the medium-frequency sudden death risk factor information, and the high-frequency sudden death risk factor information;

The comprehensive calculation unit is configured to calculate the sudden death risk index of the user by combining the low frequency sudden death risk index, the medium frequency sudden death risk index, the high frequency sudden death risk index, and the corresponding weight coefficients.

Specifically, the intelligent analysis module is composed of an index calculation unit and a comprehensive calculation unit. Wherein, the index calculation unit can retrieve the low-frequency sudden death risk factor information, the intermediate-frequency sudden death risk factor information and the high-frequency sudden death risk factor information related to the user from the user terminal. The index calculation unit can calculate the user's low frequency sudden death risk index according to the low frequency sudden death risk factor information, calculate the user's intermediate frequency sudden death risk index according to the medium frequency sudden death risk factor information, and calculate the user's high frequency sudden death risk index by using the high frequency sudden death risk factor information.

The index calculation unit can send the low-frequency sudden death risk index, the intermediate-frequency sudden death risk index and the high-frequency sudden death risk index of the user to the comprehensive calculation unit. The comprehensive calculation unit can calculate the sudden death of the user according to the low-frequency sudden death risk index, the medium-frequency sudden death risk index and the high-frequency sudden death risk index, as well as the preset weight coefficients of the low-frequency sudden death risk index, the medium-frequency sudden death risk index and the high-frequency sudden death risk index risk index. The specific calculation formula is as follows:

CS=LC×LC _weight +MC×MC _weight +HC×HC _weight ;

Among them, CS is the sudden death risk index, LC is the low frequency sudden death risk index, LC _weight is the weight coefficient corresponding to the low frequency sudden death risk index, MC is the medium frequency sudden death risk index, MC _weight is the weight coefficient corresponding to the medium frequency sudden death risk index, and HC is the high frequency sudden death risk index. Sudden death risk index, HC _weight is the weight coefficient corresponding to the high sudden death risk index.

In this embodiment, optionally, the index calculation unit includes:

an information collection unit, configured to collect questionnaire data of a plurality of experts;

The matrix construction unit is configured to construct the fuzzy adjacency matrix and the fuzzy reachability matrix of sudden death risk factors through the collected questionnaire data;

A weight calculation unit, configured to perform weight analysis according to the threshold distribution of the fuzzy adjacency relationship matrix and the fuzzy reachability matrix to obtain the weight of each sudden death risk factor;

The risk index calculation unit is configured to calculate the user's low frequency sudden death risk index and intermediate frequency sudden death risk according to the low frequency sudden death risk factor information, the medium frequency sudden death risk factor information and the high frequency sudden death risk factor information based on the weight of the corresponding sudden death risk factor. Index and High Frequency Sudden Death Risk Index.

Specifically, the index calculation unit is composed of an information collection unit, a matrix construction unit, a weight calculation unit and a risk index calculation unit. Among them, the information collection unit can collect multiple pieces of investigation information about risk factors affecting sudden death. Risk factor survey information can be collected by conducting surveys with a number of senior clinicians and researchers in the medical and health industries.

The specific research method can be that the information collection unit sends a questionnaire to the computer or mobile phone used by each expert. After the expert completes the questionnaire through the computer or mobile phone, the completed questionnaire information is sent to the user terminal such as the computer or mobile phone. to the information collection unit.

The information collection unit may send the collected questionnaire information to the matrix construction unit, where the questionnaire information includes weight data of each sudden death risk factor assessed by experts. The matrix construction unit can count the data in all the questionnaire information, determine the weighted average value corresponding to each sudden death risk factor, and then construct the fuzzy adjacency relationship matrix of sudden death risk factors based on the weighted average value corresponding to all sudden death risk factors. Fuzzy reachability matrix.

When constructing the fuzzy adjacency matrix and fuzzy reachability matrix, the matrix construction unit conducts surveys on 15 senior experts in the form of questionnaires. Initial fuzzy relationship matrix A ₀ between sudden death risk factors. Then select the maximum and minimum fuzzy operators, and obtain the fuzzy reachability matrix by matrix power multiplication.

According to the constructed fuzzy adjacency matrix and fuzzy reachability matrix, the threshold set of fuzzy adjacency matrix can be obtained, as shown in Table 1 below. The distribution of the threshold set is shown in Figure 6:

Table 1 Fuzzy adjacency matrix threshold set

The weight calculation unit can select the non-0 and 1 data in the fuzzy reachability matrix, obtain the threshold set of the fuzzy reachability matrix as shown in the following table, and then obtain the intercept matrix of the threshold set, and use the ISM algorithm to calculate the initial value. Reachability and skeleton matrices between different sudden death risk factors in a fuzzy relationship matrix.

Table 2 Fuzzy reachability matrix threshold set

The weight calculation unit can construct an ANP network architecture according to the calculated reachability matrix and skeleton matrix, and construct an unweighted matrix based on the ANP network architecture, and determine the weight of each sudden death risk factor through the unweighted matrix. The weights of sudden death risk factors obtained through ANP network structure analysis are shown in the following table:

Sudden death risk factor names Weights daily stress situation 0.37418 daily emotional state 0.33929 daily sleep quality 0.19337 daily diet quality 0.09317 daily smoking 0.17205 daily medication 0.07929 Daily electronic entertainment 0.12132 daily exercise 0.29734 daily alcohol intake 0.32999 real-time heart rate 0.20008 real-time ECG 0.23288 daily physical symptoms 0.17776 blood pressure 0.20008 blood oxygen 0.07999 blood sugar 0.10031 voice 0.00889

Table 3 Sudden death risk factor weight table

The risk index calculation unit can calculate the weight of each sudden death risk factor obtained by the weight calculation unit and the preset gray evaluation matrix, and calculate the user's low-frequency sudden death risk factor information, medium-frequency sudden death risk factor information and high-frequency sudden death risk factor information. The user's low-frequency sudden death risk index, medium-frequency sudden death risk index and high-frequency sudden death risk index are obtained.

The risk index calculation unit may construct a risk level evaluation sample based on the questionnaire information collected by the information collection unit, and construct a sample matrix according to the risk level evaluation sample. The risk index calculation unit can determine the gray level and the possibility function of the risk level evaluation sample through the sample matrix, and calculate the gray evaluation coefficient based on this, thereby constructing the gray evaluation matrix. The risk index calculation unit can calculate the user's sudden death risk index by combining the weight of each sudden death risk factor and the gray evaluation matrix.

In this embodiment, optionally, the user terminal includes a weight adjustment module, and the weight adjustment module is configured to adjust the weights of various sudden death risk factors obtained by the weight calculation unit.

The weight calculation unit can send the calculated weight of each sudden death risk factor to the user terminal, and the user can adjust the weight of each sudden death risk factor through the weight adjustment module of the user terminal, and increase or decrease the weight of each sudden death risk factor. weight, so as to obtain a personalized sudden death warning index corresponding to the user.

In this embodiment, optionally, the risk assessment module includes:

The index sequence construction unit is configured to obtain the user's historical sudden death risk index, and sort it according to the obtaining time of the sudden death risk index, so as to obtain the user's early warning index sequence;

an index sequence processing unit, configured to perform logarithmic processing on the historical sudden death risk index in the early warning index sequence to obtain a logarithmic risk change rate sequence;

an early warning model construction unit, configured to construct an autoregressive conditional heteroscedastic regression model according to the logarithmic risk change rate sequence;

an early warning index prediction unit, configured to predict the user's future sudden death early warning index through the autoregressive conditional heteroskedastic regression model;

The sudden death risk assessment unit is configured to compare the future sudden death early warning index with the historical sudden death risk index, and evaluate the sudden death risk trend of the user.

Specifically, the risk assessment module is composed of an index sequence construction unit, an index sequence processing unit, an early warning model construction unit, an early warning index prediction unit and a sudden death risk assessment unit. Among them, the index sequence construction unit can collect the user's future sudden death risk index and past historical sudden death risk index calculated by the intelligent analysis module, and sort the collected sudden death risk index and historical sudden death risk index according to the calculation time sequence, and generate a user The early warning index sequence w ₁ ={CS ₁ , CS ₂ , CS ₂ ......, CS _n }.

The exponential sequence processing unit can perform logarithmic processing on all the sudden death risk indices in the early warning index sequence w ₁ generated by the exponential sequence building unit to obtain a user-related logarithmic risk change rate sequence w ₂ ={w ₁ , w ₂ , w ₃ ...... w _n }. The specific calculation formula is as follows:

The early warning model building unit can build an autoregressive conditional heteroscedastic regression model according to the logarithmic risk change rate sequence. The specific building steps of the model are as follows:

表示成P 期残差序列的线性函数

则有:First, let the random residual term ε _t ∈ N(0,σ ² ), the conditional heteroskedasticity of ARCH(p)

Expressed as a linear function of the P-period residual series

Then there are:

ε _t =e _t σ _t ;

A generalized autoregressive conditional heteroskedastic regression model GARCH is established, and the GARCH model is expressed as follows:

Among them, α ₀ , β _i are sudden death risk indices at different time points, p, q are the number of sudden death risk indices in different time periods.

In practical applications, in order to simplify the calculation, the GARCH(1,1) model is usually used to describe the conditional variance, that is, the GARCH(1,1) model can be;

According to the logarithmic risk change rate sequence w ₂ , each coefficient value in the GARCH(1, 1) model can be determined by a linear function, such as the intercept term C, the lag term coefficient D, and the like. Finally, the autoregressive conditional heteroskedastic regression model can be constructed according to the determined coefficient values. The model is as follows:

y _t =C+ε _t -Dy _t-1 ;

where E is the intercept coefficient and F is the lag term coefficient.

The early warning model construction unit can send the constructed autoregressive conditional heteroskedastic regression model to the early warning index prediction unit, and the early warning index prediction unit predicts the user's sudden death early warning index within a certain period of time in the future through the autoregressive conditional heteroskedastic regression model, and predicts The obtained sudden death early warning index is sent to the sudden death risk assessment unit. The sudden death risk assessment unit can compare the future sudden death early warning index with the historical sudden death risk index to evaluate the sudden death risk trend of the user. The obtained sudden death risk trend is shown in Figure 7. . If the sudden death early warning index is greater than the historical sudden death risk index, it indicates that the risk of sudden death of the user increases, and the user needs to perform health management, otherwise, no health management is required.

In this embodiment, optionally, the user terminal further includes an intelligent push module, and the intelligent push module is configured to push corresponding health knowledge information to the user according to the assessment result of the risk assessment module.

Specifically, the user terminal further includes an intelligent push module, which can push corresponding health knowledge information to the user according to the evaluation result of the early warning cloud platform. For example, when the risk of sudden death of a user increases, the intelligent push module can send relevant health information to the user through emails, text messages, etc.

In this embodiment, optionally, the user terminal further includes an alarm module, and the alarm module is configured to send alarm information to the user according to the evaluation result of the risk evaluation module. Specifically, the user terminal further includes an alarm module. When the early warning cloud platform assesses that the risk of sudden death of the user increases, the alarm module can send out an alarm signal in the form of sound and light.

The above embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that the foregoing embodiments can still be used for The recorded technical solutions are modified, or some or all of the technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and should be included in the The invention is within the scope of the claims and description.

Claims (13)

1. a sudden death early warning system, is characterized in that, comprises: a user terminal, configured to collect low-frequency sudden death risk factor information, intermediate-frequency sudden death risk factor information, and high-frequency sudden death risk factor information of the user; The early warning cloud platform is equipped with an intelligent analysis module and a risk assessment module; The intelligent analysis module is configured to calculate the user's sudden death risk index according to the low-frequency sudden death risk factor information, the medium-frequency sudden death risk factor information, the high-frequency sudden death risk factor information, and the corresponding weights of each sudden death risk factor; The risk assessment module is configured to assess the sudden death risk trend of the user through a corresponding historical sudden death risk index. 2 . The sudden death early warning system according to claim 1 , wherein the user terminal comprises a risk factor selection module configured to select the sudden death risk factors to be collected. 3 . 3. The sudden death early warning system according to claim 1, wherein the low-frequency sudden death risk factor information includes basic personal information of the user, long-term living habit information, long-term medication habit information, long-term physical symptom information, and family disease history information, personal medical history information; The intermediate frequency sudden death risk factor information includes the user's short-term living habit information, short-term medication habit information, short-term physical symptom information, and physical examination report parameter information; The high-frequency sudden death risk factor information includes the user's real-time physiological characteristic information, voice information, and daily living habit information. 4. The sudden death early warning system according to claim 3, wherein the weight corresponding to the sudden death risk factor comprises:

5 . The sudden death early warning system according to claim 2 , wherein the early warning cloud platform further comprises a risk factor sorting module, and the risk factor sorting module is configured to perform the sudden death risk factors selected by the risk factor selection module. 6 . sort. 6. The sudden death early warning system according to claim 5, wherein the risk factor sorting module comprises: a data collection unit, configured to collect sudden death risk factor data corresponding to sudden death risk factors selected by multiple users, and construct a sudden death risk factor data set; a cluster analysis unit, configured to use a clustering algorithm to perform cluster analysis on the sudden death risk factor data set; The risk factor ranking unit is configured to, based on the analysis result of the cluster analysis unit, use a random forest model to rank the importance of all sudden death risk factors. 7. The sudden death early warning system according to claim 1, wherein the intelligent analysis module comprises: The index calculation unit is configured to calculate the user's low-frequency sudden death risk index, intermediate-frequency sudden death risk index, and high-frequency sudden death risk index according to the low-frequency sudden death risk factor information, the medium-frequency sudden death risk factor information, and the high-frequency sudden death risk factor information; The comprehensive calculation unit is configured to calculate the sudden death risk index of the user by combining the low frequency sudden death risk index, the medium frequency sudden death risk index, the high frequency sudden death risk index, and the corresponding weight coefficients. 8. The sudden death early warning system according to claim 7, wherein the index calculation unit comprises: an information collection unit, configured to collect questionnaire data of a plurality of experts; The matrix construction unit is configured to construct the fuzzy adjacency matrix and the fuzzy reachability matrix of sudden death risk factors through the collected questionnaire data; a weight calculation unit, configured to perform weight analysis according to the threshold distribution of the fuzzy adjacency relationship matrix and the fuzzy reachability matrix to obtain the weight of each sudden death risk factor; The risk index calculation unit is configured to calculate the user's low frequency sudden death risk index and intermediate frequency sudden death risk according to the low frequency sudden death risk factor information, the medium frequency sudden death risk factor information and the high frequency sudden death risk factor information based on the weight of the corresponding sudden death risk factor. Index and High Frequency Sudden Death Risk Index. 9 . The sudden death early warning system according to claim 8 , wherein the user terminal comprises a weight adjustment module, and the weight adjustment module is configured to adjust the weights of various sudden death risk factors obtained by the weight calculation unit. 10 . . 10. The sudden death early warning system according to claim 1, wherein the risk assessment module comprises: The index sequence construction unit is configured to obtain the historical sudden death risk index of the user, and sort it according to the obtaining time of the sudden death risk index, so as to obtain the user's early warning index sequence; an index sequence processing unit, configured to perform logarithmic processing on the historical sudden death risk index in the early warning index sequence to obtain a logarithmic risk change rate sequence; an early warning model construction unit, configured to construct an autoregressive conditional heteroscedastic regression model according to the logarithmic risk change rate sequence; an early warning index prediction unit, configured to predict the user's future sudden death early warning index through the autoregressive conditional heteroskedastic regression model; The sudden death risk assessment unit is configured to compare the future sudden death early warning index with the historical sudden death risk index, and evaluate the sudden death risk trend of the user. 11. The sudden death early warning system according to claim 1, wherein the user terminal further comprises an intelligent push module, and the intelligent push module is configured to push corresponding health knowledge to the user according to the assessment result of the risk assessment module information. 12 . The sudden death early warning system according to claim 1 , wherein the user terminal further comprises an alarm module, and the alarm module is configured to send alarm information to the user according to the evaluation result of the risk evaluation module. 13 . 13 . The sudden death early warning system according to claim 1 , further comprising a wearable device, wherein the wearable device is used to collect real-time physiological characteristic information of the user. 14 .

Images