A method and system for managing cardiovascular and cerebrovascular disease information

Abstract

The present invention discloses a method and system for managing cardiovascular and cerebrovascular disease information, which relates to the field of disease information management. The present invention obtains the disease severity level data of patients with cardiovascular and cerebrovascular diseases to be managed by constructing a final mapping equation for the severity level of cardiovascular and cerebrovascular diseases, and determines whether the patients with cardiovascular and cerebrovascular diseases to be managed need to take medicine; then, by comparing the disease severity data obtained from the aspect of drug taking of the patients with cardiovascular and cerebrovascular diseases to be managed with the disease severity data obtained from the aspect of the patients' disease information data, it is determined whether the current drug taking of the patients with cardiovascular and cerebrovascular diseases to be managed is reasonable, so as to make timely adjustments, which is beneficial to the recovery of the patients' conditions; among them, by setting the error threshold of the severity level mapping data, a quantitative judgment basis is provided for determining whether the mapping accuracy of the initial mapping equation for the severity level of cardiovascular and cerebrovascular diseases meets the requirements.

Description

Technical Field

[0001] The present invention belongs to the field of disease information management, and in particular, relates to a cardiovascular and cerebrovascular disease information management method and system. Background Art

[0002] Patients with cardiovascular and cerebrovascular diseases need to take medication during treatment; however, as the medication progresses and patients feel that their condition has improved, they may arbitrarily adjust the dosage, frequency and other information data of the medication. If the dosage and frequency are not adjusted to a reasonable level in time, the heart may face excessive pressure again, causing the disease to relapse or worsen; and it may affect the doctor's judgment of the disease and evaluation of the treatment effect. The doctor may not be able to accurately understand the patient's true condition, resulting in untimely or inaccurate adjustments to the treatment plan, delaying the best treatment time and increasing the difficulty of treatment. Summary of the invention

[0003] In view of the problems in the related art, the present invention proposes a cardiovascular and cerebrovascular disease information management method and system to overcome the above-mentioned technical problems existing in the existing related technology.

[0004] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0005] The present invention is a method for managing cardiovascular and cerebrovascular disease information, comprising the following steps:

[0006] S1. Collecting cardiovascular and cerebrovascular disease information data of patients with cardiovascular and cerebrovascular diseases to be managed, and obtaining a cardiovascular and cerebrovascular disease information data set to be managed;

[0007] S2. Collecting cardiovascular and cerebrovascular disease information data and corresponding cardiovascular and cerebrovascular disease severity level data of multiple existing cardiovascular and cerebrovascular disease patients to obtain an existing cardiovascular and cerebrovascular disease information data set matrix and an existing cardiovascular and cerebrovascular disease severity level data matrix;

[0008] S3, using the existing cardiovascular and cerebrovascular disease information data set matrix and the existing cardiovascular and cerebrovascular disease severity level data matrix to construct a final cardiovascular and cerebrovascular disease severity level mapping equation and map the cardiovascular and cerebrovascular disease information data set to be managed to obtain the first cardiovascular and cerebrovascular disease severity level data to be managed;

[0009] S4, collecting drug taking characteristic data of multiple existing cardiovascular and cerebrovascular disease patients, classifying them according to the existing cardiovascular and cerebrovascular disease severity level data matrix, and calculating the central data of each classification to obtain the existing drug taking characteristic classification central data matrix;

[0010] S5. Classify the characteristic data of the drugs taken by the patients with cardiovascular and cerebrovascular diseases to be managed according to the data matrix of the existing drug-taking characteristic classification center, and obtain the severity level data of the second cardiovascular and cerebrovascular diseases to be managed; adjust the characteristic data of the drugs taken by the patients with cardiovascular and cerebrovascular diseases to be managed according to the severity level data of the second cardiovascular and cerebrovascular diseases to be managed and the severity level data of the first cardiovascular and cerebrovascular diseases to be managed;

[0011] In this solution, the final mapping equation for the severity level of cardiovascular and cerebrovascular diseases is constructed by collecting the information data of cardiovascular and cerebrovascular diseases and the corresponding severity level data of multiple existing patients with cardiovascular and cerebrovascular diseases, and the severity level data of the patients with cardiovascular and cerebrovascular diseases to be managed is obtained to determine whether the patients with cardiovascular and cerebrovascular diseases to be managed need to take medicine; when it is determined that the patients with cardiovascular and cerebrovascular diseases to be managed need to take medicine, this solution calculates the drug-taking characteristic data of multiple existing patients with cardiovascular and cerebrovascular diseases and several classification center data thereof, and each classification center data corresponds to a severity level of cardiovascular and cerebrovascular diseases; therefore, by calculating the Euclidean distance between the drug-taking characteristic data of the cardiovascular and cerebrovascular diseases to be managed and each drug-taking classification center data, the drug-taking characteristic data of the patients with cardiovascular and cerebrovascular diseases to be managed is classified to obtain the corresponding severity level data of cardiovascular and cerebrovascular diseases; when the severity level data of cardiovascular and cerebrovascular diseases is different from the severity level data of the patients with cardiovascular and cerebrovascular diseases to be managed obtained by using the final mapping equation for the severity level of cardiovascular and cerebrovascular diseases, it indicates that the current drug-taking is unreasonable, so the drug-taking characteristic data of the patients with cardiovascular and cerebrovascular diseases to be managed is adjusted to make the characteristic data of drug-taking best match the severity of their diseases, thus improving the effect of drug treatment.

[0012] Preferably, S1 includes the following steps:

[0013] S11. Set multiple information types of cardiovascular and cerebrovascular diseases to obtain the information type set of cardiovascular and cerebrovascular diseases ,a 1i represents the i-th information type of the set cardiovascular and cerebrovascular diseases, represents the total number of information types of the set cardiovascular and cerebrovascular diseases; then set several severity levels of cardiovascular and cerebrovascular diseases to obtain the severity level set of cardiovascular and cerebrovascular diseases ,a 21 、a 22 、a 23 、a 24 、a 25respectively represent the first severity level of cardiovascular and cerebrovascular diseases, i.e., low risk, the second severity level, i.e., medium risk, the third severity level, i.e., high risk, the fourth severity level, i.e., very high risk, and the fifth severity level, i.e., critical, and the values are 1, 2, 3, 4, 5 respectively;

[0014] S12. Set the patients with cardiovascular and cerebrovascular diseases to be managed; collect the cardiovascular and cerebrovascular disease information data of the patients with cardiovascular and cerebrovascular diseases to be managed according to the set of cardiovascular and cerebrovascular disease information types, and obtain the dataset of cardiovascular and cerebrovascular disease information to be managed , represents the information data of the i-th type of cardiovascular and cerebrovascular diseases of the patients with cardiovascular and cerebrovascular diseases to be managed;

[0015] The set of cardiovascular and cerebrovascular disease information types includes blood pressure data type, blood lipid data type, blood glucose data type, incidence data type, etc.;

[0016] By collecting the cardiovascular and cerebrovascular disease information data of the patients with cardiovascular and cerebrovascular diseases to be managed, it provides data support for obtaining the disease severity level data of the patients with cardiovascular and cerebrovascular diseases to be managed subsequently.

[0017] Preferably, the S2 includes the following steps:

[0018] S21. Set multiple existing patients with cardiovascular and cerebrovascular diseases and historical data collection time points, and obtain the set of existing patients with cardiovascular and cerebrovascular diseases and the set of historical data collection time points , 、 respectively represent setting the i-th existing patient with cardiovascular and cerebrovascular diseases and the i-th historical data collection time point, and b1 and b2 respectively represent the total numbers of the set existing patients with cardiovascular and cerebrovascular diseases and historical data collection time points;

[0019] S22. According to the set of cardiovascular and cerebrovascular disease information types, the set of cardiovascular and cerebrovascular disease severity levels, the set of existing patients with cardiovascular and cerebrovascular diseases, and the set of historical data collection time points, collect the cardiovascular and cerebrovascular disease information data and the corresponding cardiovascular and cerebrovascular disease severity level data of each existing patient with cardiovascular and cerebrovascular diseases at each historical data collection time point, and obtain the matrix of existing cardiovascular and cerebrovascular disease information datasets and the matrix of existing cardiovascular and cerebrovascular disease severity level data ; they are as follows respectively:

[0020] ; ;

[0021] Among them, 、 respectively represent the cardiovascular and cerebrovascular disease information dataset and the cardiovascular and cerebrovascular disease severity level data of the i-th existing cardiovascular and cerebrovascular disease patient at the j-th historical data collection time point; , represents the k-th type of cardiovascular and cerebrovascular disease information data in

[0022] The methods for collecting cardiovascular and cerebrovascular disease severity level data include clinical evaluation, that is, evaluating the severity of a patient's cardiovascular and cerebrovascular diseases through a doctor's detailed inquiry of medical history, physical examination, and laboratory tests; imaging examinations, that is, through imaging examinations such as echocardiogram, carotid artery ultrasound, CT angiography (CTA), magnetic resonance angiography (MRA), etc., the structure and function of the heart and blood vessels can be evaluated, thereby determining the severity of the disease;

[0023] By collecting the cardiovascular and cerebrovascular disease information data and the corresponding cardiovascular and cerebrovascular disease severity level data of each existing cardiovascular and cerebrovascular disease patient, it provides data support for the subsequent construction of the final cardiovascular and cerebrovascular disease severity level mapping equation.

[0024] Preferably, the S3 includes the following steps:

[0025] S31. Construct the final cardiovascular and cerebrovascular disease severity level mapping equation by using the existing cardiovascular and cerebrovascular disease information dataset matrix and the existing cardiovascular and cerebrovascular disease severity level data matrix;

[0026] S32. Substitute each cardiovascular and cerebrovascular disease information data in the to-be-managed cardiovascular and cerebrovascular disease information dataset into the final cardiovascular and cerebrovascular disease severity level mapping equation for mapping to obtain the first to-be-managed cardiovascular and cerebrovascular disease severity level data; when the first to-be-managed cardiovascular and cerebrovascular disease severity level data is 1, the to-be-managed cardiovascular and cerebrovascular disease patient does not need to take medicine; otherwise, the to-be-managed cardiovascular and cerebrovascular disease patient needs to take medicine;

[0027] By constructing the final cardiovascular and cerebrovascular disease severity level mapping equation, it provides a mapping tool for subsequent mapping of the information data of the to-be-managed cardiovascular and cerebrovascular disease patients, and further determines whether the to-be-managed cardiovascular and cerebrovascular disease patients need to take medicine.

[0028] Preferably, the S31 includes the following steps:

[0029] S311. Construct the initial cardiovascular and cerebrovascular disease severity level mapping equation; as follows:

[0030] ;

[0031] In the formula, is the dependent variable of the initial cardiovascular and cerebrovascular disease severity level mapping equation, representing the cardiovascular and cerebrovascular disease severity level data; is the i-th independent variable of the initial cardiovascular and cerebrovascular disease severity level mapping equation, representing the i-th type of cardiovascular and cerebrovascular disease information data, is the independent variable coefficient of; is the bias of the initial cardiovascular and cerebrovascular disease severity level mapping equation; ceil represents the rounding function;

[0032] S312. Substitute each cardiovascular and cerebrovascular disease information data in each existing cardiovascular and cerebrovascular disease information data set in the existing cardiovascular and cerebrovascular disease information data set matrix into the initial cardiovascular and cerebrovascular disease severity level mapping equation for mapping to obtain the existing severity level initial mapping data matrix ; as follows:

[0033] ;

[0034] wherein, represents the cardiovascular and cerebrovascular disease severity level data obtained by substituting each cardiovascular and cerebrovascular disease information data in into the initial cardiovascular and cerebrovascular disease severity level mapping equation for mapping;

[0035] S313. Set the severity level mapping data error threshold; calculate the Euclidean distance between the existing severity level initial mapping data matrix and the existing cardiovascular and cerebrovascular disease severity level data matrix to obtain the existing severity level mapping error data c; the calculation formula is as follows:

[0036] ;

[0037] When the existing severity level mapping error data is greater than or equal to the severity level mapping data error threshold, adjust the initial cardiovascular and cerebrovascular disease severity level mapping equation until the existing severity level mapping error data is less than the severity level mapping data error threshold, and obtain the final cardiovascular and cerebrovascular disease severity level mapping equation; otherwise, there is no need to adjust the initial cardiovascular and cerebrovascular disease severity level mapping equation, and use the initial cardiovascular and cerebrovascular disease severity level mapping equation as the final cardiovascular and cerebrovascular disease severity level mapping equation;

[0038] By setting the severity level mapping data error threshold, a quantitative determination basis is provided for determining whether the mapping accuracy rate of the initial cardiovascular and cerebrovascular disease severity level mapping equation meets the requirements.

[0039] Preferably, the adjustment of the initial severity level mapping equation for cardiovascular and cerebrovascular diseases in S313 includes the following steps:

[0040] S3131. Construct a locust population for the cardiovascular and cerebrovascular disease level mapping equation , represents the i-th locust in the locust population of the cardiovascular and cerebrovascular disease level mapping equation, represents the size of the locust population of the cardiovascular and cerebrovascular disease level mapping equation; set the maximum number of iterations of the locust population of the cardiovascular and cerebrovascular disease level mapping equation to be and the current number of iterations to be ; denoted as the maximum number of iterations for level equation adjustment and the current number of iterations for level equation adjustment respectively; the search space dimension of the locust population of the cardiovascular and cerebrovascular disease level mapping equation is ;

[0041] S3132. Set the value range of each independent variable coefficient and bias of the initial severity level mapping equation for cardiovascular and cerebrovascular diseases to obtain the independent variable value range set d1 and the bias value range [d 21 , d 22 ; d 21 , d 22 represent the lower limit and upper limit of the bias value of the initial severity level mapping equation for cardiovascular and cerebrovascular diseases respectively; d1 is as follows:

[0042] ;

[0043] where , represent the lower limit and upper limit of the i-th independent variable coefficient value of the initial severity level mapping equation for cardiovascular and cerebrovascular diseases respectively;

[0044] According to the independent variable value range set and the bias value range, set the initial position matrix of each locust in the locust population of the cardiovascular and cerebrovascular disease level mapping equation to obtain the first initial position matrix set , represents the initial position matrix of the j-th locust in the locust population of the cardiovascular and cerebrovascular disease level mapping equation; as follows:

[0045] ;

[0046] where represents the position component on the i-th independent variable coefficient dimension of the initial severity level mapping equation for cardiovascular and cerebrovascular diseases; represents The position component on the bias dimension of the initial cardiovascular and cerebrovascular disease severity level mapping equation; and The calculation formulas are as follows:

[0047] ; ;

[0048] In the formula, rand 1j1i and rand 1j2 respectively represent random numbers between 0 and 1 generated for and ;

[0049] S3133. Construct the fitness function of the locust population of the cardiovascular and cerebrovascular disease level mapping equation according to the severity level mapping error data c ; as follows:

[0050] ;

[0051] S3134. Start the iteration. Before the iteration, set the current iteration number of the level equation to 1; in the first round of iteration, use the fitness function of the locust population of the cardiovascular and cerebrovascular disease level mapping equation Calculate the fitness value of the initial position matrix of each locust in the first initial position matrix set, and obtain the first fitness value set; take the maximum fitness value in the first fitness value set and the corresponding initial position matrix of the locust as the first global best fitness and the first global best position respectively; update the initial position matrix of each locust in the first initial position matrix set according to the first global best fitness and the first global best position; after the update is completed, add 1 to the current iteration number of the level equation and enter the next round of iteration;

[0052] In each subsequent round of iteration, use the fitness function of the locust population of the cardiovascular and cerebrovascular disease level mapping equation Calculate the fitness value of the position matrix of each locust in the locust population of the cardiovascular and cerebrovascular disease level mapping equation updated in the previous round of iteration, and obtain the second fitness value set; take the maximum fitness value in the second fitness value set and the corresponding position matrix of the locust as the second global best fitness and the second global best position respectively; update the position matrix of each locust in the locust population of the cardiovascular and cerebrovascular disease level mapping equation updated in the previous round of iteration according to the second global best fitness and the second global best position; after the update is completed, add 1 to the current iteration number of the level equation and enter the next round of iteration;

[0053] S3135. When When it reaches this point, stop the iteration and output the first final global optimal position; otherwise, continue the iteration until it reaches this point; substitute each position component in the first final global optimal position into the initial cardiovascular and cerebrovascular disease severity level mapping equation to obtain the optimized cardiovascular and cerebrovascular disease severity level mapping equation; substitute each cardiovascular and cerebrovascular disease information data in each existing cardiovascular and cerebrovascular disease information dataset in the existing cardiovascular and cerebrovascular disease information dataset matrix in S312 into the optimized cardiovascular and cerebrovascular disease severity level mapping equation for mapping to obtain the optimized mapping data matrix of the existing severity level; calculate the Euclidean distance between the optimized mapping data matrix of the existing severity level and the existing cardiovascular and cerebrovascular disease severity level data matrix to obtain the optimized mapping error data of the existing severity level;

[0054] When the optimized mapping error data of the existing severity level is less than the severity level mapping data error threshold, use the optimized cardiovascular and cerebrovascular disease severity level mapping equation as the final cardiovascular and cerebrovascular disease severity level mapping equation; otherwise, return to S3134 to continue the iteration until the optimized mapping error data of the existing severity level is less than the severity level mapping data error threshold;

[0055] The locust optimization algorithm can effectively explore different regions of the solution space. Locust individuals will be affected by other individuals during the search process, thus avoiding falling into local optimal solutions; it can usually find better solutions within fewer iterations; and it has fewer parameters and is easy to implement. Based on the above advantages, in this solution, the locust optimization algorithm is used to simultaneously perform multiple iterative adjustments on the coefficients of multiple independent variables and the bias of the initial cardiovascular and cerebrovascular disease severity level mapping equation, and use the error between the mapping data and the actual data of the initial cardiovascular and cerebrovascular disease severity level mapping equation as the fitness function; therefore, as the iteration progresses, the error between the mapping data and the actual data of the initial cardiovascular and cerebrovascular disease severity level mapping equation becomes smaller and smaller, and finally the mapping accuracy rate of the cardiovascular and cerebrovascular disease severity level mapping equation meets the requirements.

[0056] Preferably, S4 includes the following steps:

[0057] S41. Set multiple types of drug-taking characteristics to obtain a drug-taking characteristics type set; according to the drug-taking characteristics type set, the historical data collection time point set, and the existing cardiovascular and cerebrovascular disease patient set, collect the drug-taking characteristics data of each existing cardiovascular and cerebrovascular disease patient at each historical data collection time point to obtain the existing drug-taking characteristics dataset matrix as follows:

[0058] ;

[0059] Among them, represents the drug-taking feature dataset of the i-th existing cardiovascular and cerebrovascular disease patient at the j-th historical data collection time point; , represents the k-th type of drug-taking feature data in , where e represents the total number of set drug-taking feature types;

[0060] S42. Classify the existing drug-taking feature dataset matrix according to the existing cardiovascular and cerebrovascular disease severity level data matrix to obtain an existing drug-taking feature data classification matrix set ; represents the k-th drug-taking feature data classification matrix obtained by classifying the existing drug-taking feature dataset matrix; as follows:

[0061] ;

[0062] Among them, represents the j-th type of drug-taking feature data in the i-th group of existing drug-taking feature datasets assigned in ;

[0063] S43. Assign digital numbers to the non-numerical data in the existing drug-taking feature data classification matrix set to obtain a numbered existing drug-taking feature data classification matrix set; calculate the central data of each existing drug-taking feature data classification matrix in the numbered existing drug-taking feature data classification matrix set to obtain an existing drug-taking feature classification central data matrix ; as follows:

[0064] ;

[0065] Among them, represents the j-th type of drug-taking feature data in the classification central data set of the k-th drug-taking feature data classification matrix in the existing drug-taking feature data classification matrix set;

[0066] The drug-taking feature type set includes drug name, single-dose drug intake, and drug-taking frequency, etc.;

[0067] By calculating the classification central data of each existing drug-taking feature data classification matrix in the existing drug-taking feature data classification matrix set, on the one hand, it provides a classification basis for subsequent classification of the drug-taking feature data of the cardiovascular and cerebrovascular disease patients to be managed, and on the other hand, it corresponds one by one with the disease severity level data, so as to provide a basis for obtaining the disease severity level data corresponding to the drug-taking feature data of the cardiovascular and cerebrovascular disease patients to be managed subsequently.

[0068] Preferably, S5 includes the following steps:

[0069] S51. When the patient with cardiovascular and cerebrovascular diseases to be managed needs to take medicine, collect the characteristic data of the patient with cardiovascular and cerebrovascular diseases to be managed when taking medicine according to the set of medicine-taking characteristic types, and obtain the data set of medicine-taking characteristics to be managed; use the method in S43 to number the non-numerical data in the data set of medicine-taking characteristics to be managed, and obtain the numbered data set of medicine-taking characteristics to be managed;

[0070] Calculate the Euclidean distance between each row of data in the numbered data set of medicine-taking characteristics to be managed and the data matrix of the existing medicine-taking characteristic classification center, and obtain the set of Euclidean distances of medicine-taking characteristics;

[0071] S52. Obtain the data of the severity level of cardiovascular and cerebrovascular diseases corresponding to the smallest Euclidean distance data in the set of Euclidean distances of medicine-taking characteristics according to the data matrix of the existing severity levels of cardiovascular and cerebrovascular diseases, and obtain the second data of the severity level of cardiovascular and cerebrovascular diseases to be managed;

[0072] S53. When the second data of the severity level of cardiovascular and cerebrovascular diseases to be managed is different from the first data of the severity level of cardiovascular and cerebrovascular diseases to be managed, adjust the data set of medicine-taking characteristics to be managed until the second data of the severity level of cardiovascular and cerebrovascular diseases to be managed is the same as the first data of the severity level of cardiovascular and cerebrovascular diseases to be managed; otherwise, there is no need to adjust the data set of medicine-taking characteristics to be managed;

[0073] By comparing the severity data of the disease obtained from the medicine-taking of the patient with cardiovascular and cerebrovascular diseases to be managed with the severity data of the disease obtained from the patient's disease information data, it is possible to determine whether the current medicine-taking of the patient with cardiovascular and cerebrovascular diseases to be managed is reasonable, so as to make timely adjustments, which is beneficial to the recovery of the patient's condition.

[0074] A cardiovascular and cerebrovascular disease information management system includes a cardiovascular and cerebrovascular disease information type setting module, a data collection module for the cardiovascular and cerebrovascular diseases information of the patient to be managed, a data collection module for the existing cardiovascular and cerebrovascular disease patients, a construction module for the mapping equation of the severity level of cardiovascular and cerebrovascular diseases, a mapping module for the severity level of the patient to be managed, a data collection module for the medicine-taking characteristics of the existing patients, a classification center calculation module, a classification module for the medicine-taking characteristics data of the patient to be managed, and an adjustment module for the medicine-taking characteristics data of the patient to be managed;

[0075] The cardiovascular and cerebrovascular disease information type setting module is used to set various information types of cardiovascular and cerebrovascular diseases to obtain a set of cardiovascular and cerebrovascular disease information types;

[0076] The data acquisition module for the cardiovascular and cerebrovascular disease information of the patient to be managed is used to collect the cardiovascular and cerebrovascular disease information data of the patients with cardiovascular and cerebrovascular diseases to be managed according to the set of cardiovascular and cerebrovascular disease information types, so as to obtain the data set of cardiovascular and cerebrovascular disease information to be managed;

[0077] The data acquisition module for the existing patients with cardiovascular and cerebrovascular diseases is used to collect the cardiovascular and cerebrovascular disease information data of multiple existing patients with cardiovascular and cerebrovascular diseases and the corresponding data on the severity levels of cardiovascular and cerebrovascular diseases, so as to obtain the matrix of the existing cardiovascular and cerebrovascular disease information data set and the matrix of the existing cardiovascular and cerebrovascular disease severity level data;

[0078] The module for constructing the mapping equation of the severity level of cardiovascular and cerebrovascular diseases is used to construct the final mapping equation of the severity level of cardiovascular and cerebrovascular diseases by using the matrix of the existing cardiovascular and cerebrovascular disease information data set and the matrix of the existing cardiovascular and cerebrovascular disease severity level data;

[0079] The module for mapping the severity level of the patient to be managed is used to map the data set of the cardiovascular and cerebrovascular disease information to be managed by using the final mapping equation of the severity level of cardiovascular and cerebrovascular diseases, so as to obtain the first data on the severity level of the cardiovascular and cerebrovascular diseases to be managed;

[0080] The data acquisition module for the drug-taking characteristics of the existing patients is used to collect the drug-taking characteristic data of multiple existing patients with cardiovascular and cerebrovascular diseases, so as to obtain the matrix of the existing drug-taking characteristic data set;

[0081] The classification center calculation module is used to classify the matrix of the existing drug-taking characteristic data set in cooperation with the matrix of the existing cardiovascular and cerebrovascular disease severity level data and calculate the central data of each classification, so as to obtain the matrix of the existing drug-taking characteristic classification central data;

[0082] The module for classifying the drug-taking characteristic data of the patient to be managed is used to classify the characteristic data when the patient to be managed with cardiovascular and cerebrovascular diseases takes drugs according to the matrix of the existing drug-taking characteristic classification central data, so as to obtain the second data on the severity level of the cardiovascular and cerebrovascular diseases to be managed;

[0083] The module for adjusting the drug-taking characteristic data of the patient to be managed is used to adjust the characteristic data when the patient to be managed with cardiovascular and cerebrovascular diseases takes drugs according to the second data on the severity level of the cardiovascular and cerebrovascular diseases to be managed and the first data on the severity level of the cardiovascular and cerebrovascular diseases to be managed.

[0084] The present invention has the following beneficial effects:

[0085] 1. In the present invention, by constructing a mapping equation for the severity level of cardiovascular and cerebrovascular diseases, the severity level data of patients with cardiovascular and cerebrovascular diseases to be managed is obtained to determine whether the patients with cardiovascular and cerebrovascular diseases to be managed need to take medicine; then, by comparing the severity level data obtained from the aspect of drug taking of the patients with cardiovascular and cerebrovascular diseases to be managed with the severity level data obtained from the aspect of the patients' disease information data, it is determined whether the current drug taking of the patients with cardiovascular and cerebrovascular diseases to be managed is reasonable, so as to make timely adjustments, which is beneficial to the recovery of the patients' conditions.

[0086] 2. In the present invention, by setting a mapping data error threshold for the severity level, a quantitative determination basis is provided for determining whether the mapping accuracy of the initial mapping equation for the severity level of cardiovascular and cerebrovascular diseases meets the requirements.

[0087] 3. In the present invention, the locust optimization algorithm is used to simultaneously perform multiple iterative adjustments on the coefficients of multiple independent variables and the bias of the initial mapping equation for the severity level of cardiovascular and cerebrovascular diseases, and the error between the mapping data and the actual data of the initial mapping equation for the severity level of cardiovascular and cerebrovascular diseases is used as the fitness function; therefore, as the iteration progresses, the error between the mapping data and the actual data of the initial mapping equation for the severity level of cardiovascular and cerebrovascular diseases becomes smaller and smaller, and finally the mapping accuracy of the mapping equation for the severity level of cardiovascular and cerebrovascular diseases meets the requirements.

[0088] Of course, it is not necessary for any product implementing the present invention to simultaneously achieve all the above-mentioned advantages. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the invention, and for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0090] Figure 1 It is a schematic flowchart of the method for managing cardiovascular and cerebrovascular disease information in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0091] The technical solutions in the embodiments of the invention will be clearly and completely described below with reference to the drawings in the embodiments of the invention. Obviously, the described embodiments are only some embodiments of the invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the invention without creative efforts belong to the scope of protection of the invention.

[0092] Example 1, please refer to Figure 1, this embodiment is a method for managing cardiovascular and cerebrovascular disease information, including the following steps:

[0093] S1. Collect the cardiovascular and cerebrovascular disease information data of the patients with cardiovascular and cerebrovascular diseases to be managed, and obtain the dataset of cardiovascular and cerebrovascular disease information to be managed;

[0094] The S1 includes the following steps:

[0095] S11. Set multiple information types of cardiovascular and cerebrovascular diseases to obtain the set of cardiovascular and cerebrovascular disease information types , a 1i represents the i-th information type of the set cardiovascular and cerebrovascular diseases, represents the total number of information types of the set cardiovascular and cerebrovascular diseases; then set several severity levels of cardiovascular and cerebrovascular diseases to obtain the set of severity levels of cardiovascular and cerebrovascular diseases , a 21 , a 22 , a 23 , a 24 , a 25 respectively represent the first severity level of cardiovascular and cerebrovascular diseases, i.e., low risk, the second severity level, i.e., medium risk, the third severity level, i.e., high risk, the fourth severity level, i.e., very high risk, and the fifth severity level, i.e., critical, and the values are 1, 2, 3, 4, 5 respectively;

[0096] S12. Set the patients with cardiovascular and cerebrovascular diseases to be managed; collect the cardiovascular and cerebrovascular disease information data of the patients with cardiovascular and cerebrovascular diseases to be managed according to the set of cardiovascular and cerebrovascular disease information types, and obtain the dataset of cardiovascular and cerebrovascular disease information to be managed , represents the i-th type of information data of the cardiovascular and cerebrovascular diseases of the patients with cardiovascular and cerebrovascular diseases to be managed.

[0097] S2. Collect the cardiovascular and cerebrovascular disease information data and the corresponding cardiovascular and cerebrovascular disease severity level data of multiple existing patients with cardiovascular and cerebrovascular diseases, and obtain the matrix of the existing cardiovascular and cerebrovascular disease information dataset and the matrix of the existing cardiovascular and cerebrovascular disease severity level data;

[0098] The S2 includes the following steps:

[0099] S21. Set multiple existing patients with cardiovascular and cerebrovascular diseases and the historical data collection time points to obtain the set of existing patients with cardiovascular and cerebrovascular diseases and the set of historical data collection time points , . respectively represent setting the \(i\)-th existing cardiovascular and cerebrovascular disease patient and the \(i\)-th historical data collection time point, and \(b_1\), \(b_2\) respectively represent the total numbers of the set existing cardiovascular and cerebrovascular disease patients and historical data collection time points;

[0100] S22. According to the set of cardiovascular and cerebrovascular disease information types, the set of cardiovascular and cerebrovascular disease severity levels, the set of existing cardiovascular and cerebrovascular disease patients, and the set of historical data collection time points, collect the cardiovascular and cerebrovascular disease information data and the corresponding cardiovascular and cerebrovascular disease severity level data of each existing cardiovascular and cerebrovascular disease patient at each historical data collection time point, and obtain the existing cardiovascular and cerebrovascular disease information data set matrix and the existing cardiovascular and cerebrovascular disease severity level data matrix ; respectively as follows:

[0101] ; ;

[0102] Among them, 、 respectively represent the cardiovascular and cerebrovascular disease information data set and the cardiovascular and cerebrovascular disease severity level data of the \(i\)-th existing cardiovascular and cerebrovascular disease patient at the \(j\)-th historical data collection time point; , represents the \(k\)-th type of cardiovascular and cerebrovascular disease information data in

[0103] S3. Use the existing cardiovascular and cerebrovascular disease information data set matrix and the existing cardiovascular and cerebrovascular disease severity level data matrix to construct the final cardiovascular and cerebrovascular disease severity level mapping equation and map the cardiovascular and cerebrovascular disease information data set to be managed, and obtain the first cardiovascular and cerebrovascular disease severity level data to be managed;

[0104] The S3 includes the following steps:

[0105] S31. Use the existing cardiovascular and cerebrovascular disease information data set matrix and the existing cardiovascular and cerebrovascular disease severity level data matrix to construct the final cardiovascular and cerebrovascular disease severity level mapping equation;

[0106] The S31 includes the following steps:

[0107] S311. Construct the initial cardiovascular and cerebrovascular disease severity level mapping equation; as follows:

[0108] ;

[0109] In the formula, is the dependent variable of the initial cardiovascular and cerebrovascular disease severity level mapping equation, representing the cardiovascular and cerebrovascular disease severity level data; is the i-th independent variable of the initial cardiovascular and cerebrovascular disease severity level mapping equation, representing the cardiovascular and cerebrovascular disease information data of the i-th type. is the coefficient of the independent variable; is the bias of the initial cardiovascular and cerebrovascular disease severity level mapping equation; ceil represents the rounding function;

[0110] S312. Substitute each cardiovascular and cerebrovascular disease information data in each existing cardiovascular and cerebrovascular disease information dataset in the existing cardiovascular and cerebrovascular disease information dataset matrix into the initial cardiovascular and cerebrovascular disease severity level mapping equation for mapping to obtain the existing severity level initial mapping data matrix ; as follows:

[0111] ;

[0112] wherein, represents the cardiovascular and cerebrovascular disease severity level data obtained by substituting each cardiovascular and cerebrovascular disease information data in into the initial cardiovascular and cerebrovascular disease severity level mapping equation for mapping;

[0113] S313. Set the severity level mapping data error threshold; calculate the Euclidean distance between the existing severity level initial mapping data matrix and the existing cardiovascular and cerebrovascular disease severity level data matrix to obtain the existing severity level mapping error data c; the calculation formula is as follows:

[0114] ;

[0115] When the existing severity level mapping error data is greater than or equal to the severity level mapping data error threshold, adjust the initial cardiovascular and cerebrovascular disease severity level mapping equation until the existing severity level mapping error data is less than the severity level mapping data error threshold; otherwise, there is no need to adjust the initial cardiovascular and cerebrovascular disease severity level mapping equation, and use the initial cardiovascular and cerebrovascular disease severity level mapping equation as the final cardiovascular and cerebrovascular disease severity level mapping equation;

[0116] The adjustment of the initial cardiovascular and cerebrovascular disease severity level mapping equation in S313 includes the following steps:

[0117] S3131. Construct the locust population of the cardiovascular and cerebrovascular disease level mapping equation , represents the i-th locust in the locust population of the cardiovascular and cerebrovascular disease level mapping equation, represent the scale of the locust population of the cardiovascular and cerebrovascular disease level mapping equation; set the maximum number of iterations of the locust population of the cardiovascular and cerebrovascular disease level mapping equation to be and the current number of iterations to be ; denote them as the maximum number of iterations for adjusting the level equation and the current number of iterations for adjusting the level equation respectively; the search space dimension of the locust population of the cardiovascular and cerebrovascular disease level mapping equation is ;

[0118] S3132. Set the value range of each independent variable coefficient and bias of the initial cardiovascular and cerebrovascular disease severity level mapping equation to obtain the independent variable value range set d1 and the bias value range [d 21 , d 22 ; d 21 , d 22 represent the lower limit and upper limit of the bias value of the initial cardiovascular and cerebrovascular disease severity level mapping equation respectively; d1 is as follows:

[0119] ;

[0120] where , represent the lower limit and upper limit of the value of the i-th independent variable coefficient of the initial cardiovascular and cerebrovascular disease severity level mapping equation respectively;

[0121] Set the initial position matrix of each locust in the locust population of the cardiovascular and cerebrovascular disease level mapping equation according to the independent variable value range set and the bias value range to obtain the first initial position matrix set , represent the initial position matrix of the j-th locust in the locust population of the cardiovascular and cerebrovascular disease level mapping equation; as follows:

[0122] ;

[0123] where represents the position component on the i-th independent variable coefficient dimension of the initial cardiovascular and cerebrovascular disease severity level mapping equation; represents the position component on the bias dimension of the initial cardiovascular and cerebrovascular disease severity level mapping equation; , The calculation formulas of are as follows:

[0124] ; ;

[0125] In the formula, rand 1j1i , rand 1j2respectively represent for 、 random numbers between 0 and 1 generated;

[0126] S3133. Construct the fitness function of the locust population of the cardiovascular and cerebrovascular disease level mapping equation according to the severity level mapping error data c ; as follows:

[0127] ;

[0128] S3134. Start iteration. Before iteration, set the current iteration number of the level equation to 1; in the first round of iteration, use the fitness function of the locust population of the cardiovascular and cerebrovascular disease level mapping equation Calculate the fitness value of the initial position matrix of each locust in the first initial position matrix set, and obtain the first fitness value set; take the maximum fitness value in the first fitness value set and the corresponding initial position matrix of the locust as the first global best fitness and the first global best position respectively; update the initial position matrix of each locust in the first initial position matrix set according to the first global best fitness and the first global best position; after the update is completed, add 1 to the current iteration number of the level equation and enter the next round of iteration;

[0129] In each other round of iteration, use the fitness function of the locust population of the cardiovascular and cerebrovascular disease level mapping equation Calculate the fitness value of the position matrix of each locust in the locust population of the cardiovascular and cerebrovascular disease level mapping equation updated in the previous round of iteration, and obtain the second fitness value set; take the maximum fitness value in the second fitness value set and the corresponding position matrix of the locust as the second global best fitness and the second global best position respectively; update the position matrix of each locust in the locust population of the cardiovascular and cerebrovascular disease level mapping equation updated in the previous round of iteration according to the second global best fitness and the second global best position; after the update is completed, add 1 to the current iteration number of the level equation and enter the next round of iteration;

[0130] S3135. When , stop iteration and output the first final global best position; otherwise, continue iteration until until; substitute each position component in the first final global optimal position into the initial cardiovascular and cerebrovascular disease severity level mapping equation to obtain the optimized cardiovascular and cerebrovascular disease severity level mapping equation; substitute each cardiovascular and cerebrovascular disease information data in each existing cardiovascular and cerebrovascular disease information data set in the existing cardiovascular and cerebrovascular disease information data set matrix in S312 into the optimized cardiovascular and cerebrovascular disease severity level mapping equation for mapping to obtain the optimized mapping data matrix of the existing severity level; calculate the Euclidean distance between the optimized mapping data matrix of the existing severity level and the existing cardiovascular and cerebrovascular disease severity level data matrix to obtain the optimized mapping error data of the existing severity level;

[0131] When the optimized mapping error data of the existing severity level is less than the severity level mapping data error threshold, use the optimized cardiovascular and cerebrovascular disease severity level mapping equation as the final cardiovascular and cerebrovascular disease severity level mapping equation; otherwise, return to S3134 to continue the iteration until the optimized mapping error data of the existing severity level is less than the severity level mapping data error threshold;

[0132] S32: Substitute each cardiovascular and cerebrovascular disease information data in the to-be-managed cardiovascular and cerebrovascular disease information data set into the final cardiovascular and cerebrovascular disease severity level mapping equation for mapping to obtain the first to-be-managed cardiovascular and cerebrovascular disease severity level data; when the first to-be-managed cardiovascular and cerebrovascular disease severity level data is 1, the to-be-managed cardiovascular and cerebrovascular disease patient does not need to take medicine; otherwise, the to-be-managed cardiovascular and cerebrovascular disease patient needs to take medicine.

[0133] S4: Collect the drug-taking characteristic data of multiple existing cardiovascular and cerebrovascular disease patients, classify them in combination with the existing cardiovascular and cerebrovascular disease severity level data matrix, and calculate the central data of each classification to obtain the existing drug-taking characteristic classification central data matrix;

[0134] S4 includes the following steps:

[0135] S41: Set multiple drug-taking characteristic types to obtain the drug-taking characteristic type set; according to the drug-taking characteristic type set, the historical data collection time point set, and the existing cardiovascular and cerebrovascular disease patient set, collect the drug-taking characteristic data of each existing cardiovascular and cerebrovascular disease patient at each historical data collection time point to obtain the existing drug-taking characteristic data set matrix ; as follows:

[0136] ;

[0137] wherein, Denote the drug-taking characteristic data set of the \(i\)-th existing cardiovascular and cerebrovascular disease patient at the \(j\)-th historical data collection time point; , Denote the \(k\)-th type of drug-taking characteristic data in

[0138] S42. Classify the existing drug-taking characteristic data set matrix according to the existing cardiovascular and cerebrovascular disease severity level data matrix to obtain an existing drug-taking characteristic data classification matrix set ; Denote the \(k\)-th drug-taking characteristic data classification matrix obtained by classifying the existing drug-taking characteristic data set matrix; as follows:

[0139] ;

[0140] Among them, Denote the \(j\)-th type of drug-taking characteristic data in the \(i\)-th group of existing drug-taking characteristic data sets assigned in

[0141] S43. Assign digital numbers to the non-numerical data in the existing drug-taking characteristic data classification matrix set to obtain an existing drug-taking characteristic data classification matrix set after numbering; calculate the central data of each existing drug-taking characteristic data classification matrix in the existing drug-taking characteristic data classification matrix set after numbering to obtain an existing drug-taking characteristic classification central data matrix ; as follows:

[0142] ;

[0143] Among them, Denote the \(j\)-th type of drug-taking characteristic data in the classification central data set of the \(k\)-th drug-taking characteristic data classification matrix in the existing drug-taking characteristic data classification matrix set;

[0144] The said S43 includes the following steps:

[0145] S431. Construct a locust population for calculating the drug-taking characteristic classification center , Denote the \(i\)-th locust in the locust population for calculating the drug-taking characteristic classification center, Denote the scale of the locust population for calculating the drug-taking characteristic classification center; set the maximum number of iterations of the locust population for calculating the drug-taking characteristic classification center as and the current number of iterations as ; Denote the maximum number of iterations for calculating the classification center and the current iteration number for calculating the classification center respectively; the search space dimension for calculating the locust population by the drug-taking feature classification center is ;

[0146] S432. Randomly select one row of data from each classification matrix in the numbered existing drug-taking feature data classification matrix set multiple times to form the initial position matrix of each locust in the locust population for calculating the drug-taking feature classification center, and obtain the second initial position matrix set , represents the initial position matrix of the -th locust in the locust population for calculating the drug-taking feature classification center; as follows:

[0147] ;

[0148] Among them, represents the position component on the j-th type of drug-taking feature data dimension in the classification center dataset of the k-th drug-taking feature data classification matrix in the existing drug-taking feature data classification matrix set;

[0149] S433. Construct the fitness function of the -th locust in the locust population for calculating the drug-taking feature classification center according to the initial position matrix of the -th locust in the locust population for calculating the drug-taking feature classification center and the existing drug-taking feature data classification matrix set ; as follows:

[0150] ;

[0151] S434. Start iteration. Before iteration, set the current iteration number for calculating the classification center to 1; in the first round of iteration, use the fitness function of the -th locust in the locust population for calculating the drug-taking feature classification center to calculate the fitness values of the initial position matrices of each locust in the second initial position matrix set, and obtain the third fitness value set; take the maximum fitness value in the third fitness value set and the corresponding initial position matrix of the locust as the third global best fitness and the third global best position respectively; update the initial position matrices of each locust in the second initial position matrix set according to the third global best fitness and the third global best position; after the update is completed, add 1 to the current iteration number for calculating the classification center and enter the next round of iteration;

[0152] In each subsequent round of iteration, use the fitness function of the Fitness function of a single locust Calculate the fitness value of the position matrix of each locust in the locust population by using the drug-taking feature classification center updated in the previous iteration process to obtain the fourth fitness value set; take the maximum fitness value in the fourth fitness value set and the corresponding position matrix of the locust as the fourth global best fitness and the fourth global best position respectively; update the position matrix of each locust in the locust population by using the drug-taking feature classification center updated in the previous iteration process according to the fourth global best fitness and the fourth global best position; after the update is completed, increment the current iteration count of the classification center by 1 and enter the next iteration;

[0153] S435. When holds, stop the iteration and output the second final global best position; otherwise, continue the iteration until holds; take the second final global best position as the existing drug-taking feature classification center data matrix;

[0154] By using the locust optimization algorithm, perform multiple iteration operations on the classification center data of each existing drug-taking feature data classification matrix in the existing drug-taking feature data classification matrix set simultaneously, and use the overall dispersion degree of each existing drug-taking feature data classification matrix in the existing drug-taking feature data classification matrix set as the fitness function. As the iteration progresses, the overall dispersion degree of each existing drug-taking feature data classification matrix in the existing drug-taking feature data classification matrix set becomes smaller and smaller, which indicates that the classification center data of each existing drug-taking feature data classification matrix obtained by the iteration can better represent the classification center data of each existing drug-taking feature data classification matrix.

[0155] S5. Classify the feature data of the drugs taken by the patients with cardiovascular and cerebrovascular diseases to be managed according to the existing drug-taking feature classification center data matrix to obtain the second severity level data of the cardiovascular and cerebrovascular diseases to be managed; adjust the feature data of the drugs taken by the patients with cardiovascular and cerebrovascular diseases to be managed according to the second severity level data of the cardiovascular and cerebrovascular diseases to be managed and the first severity level data of the cardiovascular and cerebrovascular diseases to be managed;

[0156] The S5 includes the following steps:

[0157] S51. When the patients with cardiovascular and cerebrovascular diseases to be managed need to take drugs, collect the feature data of the drugs taken by the patients with cardiovascular and cerebrovascular diseases to be managed according to the drug-taking feature type set to obtain the to-be-managed drug-taking feature data set; use the method in S43 to number the non-numerical data in the to-be-managed drug-taking feature data set to obtain the numbered to-be-managed drug-taking feature data set;

[0158] Calculate the Euclidean distance between each row of the data matrix of the existing drug-taking characteristic classification center and the dataset of the drug-taking characteristics of the to-be-managed drugs after numbering to obtain a set of Euclidean distances of drug-taking characteristics;

[0159] S52. Obtain the severity level data of cardiovascular and cerebrovascular diseases corresponding to the smallest Euclidean distance data in the set of Euclidean distances of drug-taking characteristics according to the data matrix of the existing severity levels of cardiovascular and cerebrovascular diseases to obtain the second to-be-managed severity level data of cardiovascular and cerebrovascular diseases;

[0160] S53. When the second to-be-managed severity level data of cardiovascular and cerebrovascular diseases is different from the first to-be-managed severity level data of cardiovascular and cerebrovascular diseases, adjust the dataset of the drug-taking characteristics of the to-be-managed drugs until the second to-be-managed severity level data of cardiovascular and cerebrovascular diseases is the same as the first to-be-managed severity level data of cardiovascular and cerebrovascular diseases; otherwise, there is no need to adjust the dataset of the drug-taking characteristics of the to-be-managed drugs.

[0161] Embodiment 2. This embodiment discloses a cardiovascular and cerebrovascular disease information management system, which can implement the method of the above embodiment, including a cardiovascular and cerebrovascular disease information type setting module, a to-be-managed patient's cardiovascular and cerebrovascular disease information data acquisition module, an existing cardiovascular and cerebrovascular disease patient data acquisition module, a cardiovascular and cerebrovascular disease severity level mapping equation construction module, a to-be-managed patient's severity level mapping module, an existing patient's drug-taking characteristic data acquisition module, a classification center calculation module, a to-be-managed patient's drug-taking characteristic data classification module, and a to-be-managed patient's drug-taking characteristic data adjustment module;

[0162] The cardiovascular and cerebrovascular disease information type setting module is used to set various information types of cardiovascular and cerebrovascular diseases to obtain a set of cardiovascular and cerebrovascular disease information types;

[0163] The to-be-managed patient's cardiovascular and cerebrovascular disease information data acquisition module is used to collect the cardiovascular and cerebrovascular disease information data of the to-be-managed cardiovascular and cerebrovascular disease patients according to the set of cardiovascular and cerebrovascular disease information types to obtain a to-be-managed cardiovascular and cerebrovascular disease information dataset;

[0164] The existing cardiovascular and cerebrovascular disease patient data acquisition module is used to collect the cardiovascular and cerebrovascular disease information data and the corresponding severity level data of cardiovascular and cerebrovascular diseases of multiple existing cardiovascular and cerebrovascular disease patients to obtain an existing cardiovascular and cerebrovascular disease information dataset matrix and an existing cardiovascular and cerebrovascular disease severity level data matrix;

[0165] The cardiovascular and cerebrovascular disease severity level mapping equation construction module is used to construct a final cardiovascular and cerebrovascular disease severity level mapping equation by using the existing cardiovascular and cerebrovascular disease information dataset matrix and the existing cardiovascular and cerebrovascular disease severity level data matrix;

[0166] The severity level mapping module for the patients to be managed is used to map the information dataset of the cardiovascular and cerebrovascular diseases to be managed by using the final severity level mapping equation of cardiovascular and cerebrovascular diseases, so as to obtain the first severity level data of the cardiovascular and cerebrovascular diseases to be managed;

[0167] The drug-taking feature data acquisition module for existing patients is used to collect the drug-taking feature data of multiple existing cardiovascular and cerebrovascular disease patients, so as to obtain the existing drug-taking feature dataset matrix;

[0168] The classification center calculation module is used to classify the existing drug-taking feature dataset matrix in cooperation with the existing severity level data matrix of cardiovascular and cerebrovascular diseases and calculate the center data of each classification, so as to obtain the existing drug-taking feature classification center data matrix;

[0169] The drug-taking feature data classification module for the patients to be managed is used to classify the feature data when the patients to be managed with cardiovascular and cerebrovascular diseases take drugs according to the existing drug-taking feature classification center data matrix, so as to obtain the second severity level data of the cardiovascular and cerebrovascular diseases to be managed;

[0170] The drug-taking feature data adjustment module for the patients to be managed is used to adjust the feature data when the patients to be managed with cardiovascular and cerebrovascular diseases take drugs according to the second severity level data of the cardiovascular and cerebrovascular diseases to be managed and the first severity level data of the cardiovascular and cerebrovascular diseases to be managed.

[0171] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0172] The preferred embodiments of the invention disclosed above are only used to help illustrate the invention. The preferred embodiments do not elaborate all the details, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. The embodiments selected and specifically described in this specification are for better explaining the principles and practical applications of the invention, so that those skilled in the art in the relevant technical field can well understand and utilize the invention.

Claims

1. A method for managing cardiovascular and cerebrovascular disease information, characterized in that, It includes the following steps: S1. Collect the cardiovascular and cerebrovascular disease information data of the cardiovascular and cerebrovascular disease patients to be managed, and obtain the dataset of cardiovascular and cerebrovascular disease information to be managed; S2. Collect the cardiovascular and cerebrovascular disease information data of multiple existing cardiovascular and cerebrovascular disease patients and the corresponding data of the severity levels of cardiovascular and cerebrovascular diseases, and obtain the matrix of the existing cardiovascular and cerebrovascular disease information dataset and the matrix of the existing cardiovascular and cerebrovascular disease severity level data; S3. Use the matrix of the existing cardiovascular and cerebrovascular disease information dataset and the matrix of the existing cardiovascular and cerebrovascular disease severity level data to construct the final mapping equation of the severity level of cardiovascular and cerebrovascular diseases and map the dataset of cardiovascular and cerebrovascular disease information to be managed, and obtain the first severity level data of the cardiovascular and cerebrovascular disease to be managed; S4. Set multiple types of drug-taking characteristics, combine the historical data collection time point set and the existing cardiovascular and cerebrovascular disease patient set. At each historical data collection time point, collect the drug-taking characteristic data of each existing cardiovascular and cerebrovascular disease patient to obtain the existing drug-taking characteristic data set matrix and classify it to obtain the existing drug-taking characteristic data classification matrix set; number the non-numerical data in the existing drug-taking characteristic data classification matrix set to obtain the numbered existing drug-taking characteristic data classification matrix set; construct the drug-taking characteristic classification center to calculate the locust population and set its maximum number of iterations to and the current number of iterations to ; respectively denoted as the maximum number of iterations for classification center calculation and the current number of iterations for classification center calculation; randomly select one row of data from each classification matrix in the numbered existing drug-taking characteristic data classification matrix set multiple times to form the initial position matrix of each locust in the drug-taking characteristic classification center calculation locust population, and obtain the second initial position matrix set; start the iteration; in each round of iteration, use the fitness function of each locust in the drug-taking characteristic classification center calculation locust population to calculate the fitness value of the position matrix of each locust in the drug-taking characteristic classification center calculation locust population updated in the previous round of iteration and update the position matrix of each locust in the drug-taking characteristic classification center calculation locust population updated in the previous round of iteration; when is reached, stop the iteration and output the second final global best position; use the second final global best position as the existing drug-taking characteristic classification center data matrix; S5. Classify the characteristic data of the drugs taken by the cardiovascular and cerebrovascular disease patients to be managed according to the central data matrix of the existing drug-taking characteristic classification, and obtain the second severity level data of the cardiovascular and cerebrovascular disease to be managed; adjust the characteristic data of the drugs taken by the cardiovascular and cerebrovascular disease patients to be managed according to the second severity level data of the cardiovascular and cerebrovascular disease to be managed and the first severity level data of the cardiovascular and cerebrovascular disease to be managed.

2. The method for managing cardiovascular and cerebrovascular disease information according to claim 1, wherein The S1 includes the following steps: S11. Set multiple information types of cardiovascular and cerebrovascular diseases to obtain the set of cardiovascular and cerebrovascular disease information types; then set several severity levels of cardiovascular and cerebrovascular diseases to obtain the set of cardiovascular and cerebrovascular disease severity levels; S12. Set the cardiovascular and cerebrovascular disease patients to be managed; collect the cardiovascular and cerebrovascular disease information data of the cardiovascular and cerebrovascular disease patients to be managed according to the set of cardiovascular and cerebrovascular disease information types, and obtain the dataset of cardiovascular and cerebrovascular disease information to be managed.

3. The method for managing cardiovascular and cerebrovascular disease information according to claim 2, wherein The S2 includes the following steps: S21. Set multiple existing cardiovascular and cerebrovascular disease patients and the historical data collection time points to obtain the set of existing cardiovascular and cerebrovascular disease patients and the set of historical data collection time points; S22. According to the set of cardiovascular and cerebrovascular disease information types, the set of cardiovascular and cerebrovascular disease severity levels, the set of existing cardiovascular and cerebrovascular disease patients, and the set of historical data collection time points, collect the cardiovascular and cerebrovascular disease information data of each existing cardiovascular and cerebrovascular disease patient and the corresponding data of the severity levels of cardiovascular and cerebrovascular diseases at each historical data collection time point, and obtain the matrix of the existing cardiovascular and cerebrovascular disease information dataset and the matrix of the existing cardiovascular and cerebrovascular disease severity level data.

4. The method for managing cardiovascular and cerebrovascular disease information according to claim 3, characterized in that, The S3 includes the following steps: S31. Use the matrix of the existing cardiovascular and cerebrovascular disease information dataset and the matrix of the existing cardiovascular and cerebrovascular disease severity level data to construct the final mapping equation of the severity level of cardiovascular and cerebrovascular diseases; S32. Substitute each cardiovascular and cerebrovascular disease information data in the dataset of cardiovascular and cerebrovascular disease information to be managed into the final mapping equation of the severity level of cardiovascular and cerebrovascular diseases for mapping, and obtain the first severity level data of the cardiovascular and cerebrovascular disease to be managed; when the first severity level data of the cardiovascular and cerebrovascular disease to be managed is 1, the cardiovascular and cerebrovascular disease patient to be managed does not need to take medicine; otherwise, the cardiovascular and cerebrovascular disease patient to be managed needs to take medicine.

5. The method for managing cardiovascular and cerebrovascular disease information according to claim 4, wherein The S31 includes the following steps: S311. Construct an initial mapping equation for the severity level of cardiovascular and cerebrovascular diseases; S312. Substitute each cardiovascular and cerebrovascular disease information data in each existing cardiovascular and cerebrovascular disease information dataset in the existing cardiovascular and cerebrovascular disease information dataset matrix into the initial mapping equation for the severity level of cardiovascular and cerebrovascular diseases for mapping, to obtain an initial mapping data matrix of the existing severity levels; S313. Set a severity level mapping data error threshold; calculate the Euclidean distance between the initial mapping data matrix of the existing severity levels and the existing severity level data matrix of cardiovascular and cerebrovascular diseases to obtain existing severity level mapping error data; When the existing severity level mapping error data is greater than or equal to the severity level mapping data error threshold, adjust the initial mapping equation for the severity level of cardiovascular and cerebrovascular diseases until the existing severity level mapping error data is less than the severity level mapping data error threshold, to obtain a final mapping equation for the severity level of cardiovascular and cerebrovascular diseases; otherwise, there is no need to adjust the initial mapping equation for the severity level of cardiovascular and cerebrovascular diseases, and use the initial mapping equation for the severity level of cardiovascular and cerebrovascular diseases as the final mapping equation for the severity level of cardiovascular and cerebrovascular diseases.

6. The method for managing cardiovascular and cerebrovascular disease information according to claim 5, wherein The adjustment of the initial mapping equation for the severity level of cardiovascular and cerebrovascular diseases in S313 includes the following steps: S3131. Construct a locust population of the cardiovascular and cerebrovascular disease level mapping equation; set the maximum number of iterations of the locust population of the cardiovascular and cerebrovascular disease level mapping equation to be and the current number of iterations to be ; which are respectively denoted as the maximum number of iterations for adjusting the level equation and the current number of iterations for adjusting the level equation; S3132. Set the value range of each independent variable coefficient and the bias of the initial mapping equation for the severity level of cardiovascular and cerebrovascular diseases to obtain a set of independent variable value ranges and a bias value range; Set an initial position matrix for each locust in the locust population of the mapping equation for the cardiovascular and cerebrovascular disease level according to the set of independent variable value ranges and the bias value range, to obtain a first set of initial position matrices; S3133. Construct a fitness function for the locust population of the mapping equation for the cardiovascular and cerebrovascular disease level according to the severity level mapping error data; S3134. Start iteration. Before iteration, set the current iteration count of the level equation adjustment to 1; in each round of iteration, calculate the fitness value of the position matrix of each locust in the locust population of the mapping equation for the cardiovascular and cerebrovascular disease level updated in the previous round of iteration using the fitness function of the locust population of the mapping equation for the cardiovascular and cerebrovascular disease level and update the position matrix of each locust in the locust population of the mapping equation for the cardiovascular and cerebrovascular disease level updated in the previous round of iteration; S3135. When occurs, stop the iteration and output the first final global optimal position; otherwise, continue the iteration until is reached; substitute each position component in the first final global optimal position into the initial cardiovascular and cerebrovascular disease severity level mapping equation to obtain the optimized cardiovascular and cerebrovascular disease severity level mapping equation; substitute each cardiovascular and cerebrovascular disease information data in each existing cardiovascular and cerebrovascular disease information dataset in the existing cardiovascular and cerebrovascular disease information dataset matrix in S312 into the optimized cardiovascular and cerebrovascular disease severity level mapping equation for mapping to obtain the optimized mapping data matrix of the existing severity level; calculate the Euclidean distance between the optimized mapping data matrix of the existing severity level and the existing cardiovascular and cerebrovascular disease severity level data matrix to obtain the optimized mapping error data of the existing severity level; When the optimized existing severity level mapping error data is less than the severity level mapping data error threshold, use the optimized mapping equation for the severity level of cardiovascular and cerebrovascular diseases as the final mapping equation for the severity level of cardiovascular and cerebrovascular diseases; otherwise, return to S3134 to continue iteration until the optimized existing severity level mapping error data is less than the severity level mapping data error threshold.

7. The method for managing cardiovascular and cerebrovascular disease information according to claim 6, characterized in that, The said S5 includes the following steps: S51. When the cardiovascular and cerebrovascular disease patient to be managed needs to take medicine, collect the characteristic data of the cardiovascular and cerebrovascular disease patient to be managed when taking medicine according to the drug-taking characteristic type set, and obtain the to-be-managed drug-taking characteristic data set; use the method in S43 to number the non-numerical data in the to-be-managed drug-taking characteristic data set, and obtain the numbered to-be-managed drug-taking characteristic data set; Calculate the Euclidean distance between each row of data in the numbered to-be-managed drug-taking characteristic data set and the existing drug-taking characteristic classification center data matrix, and obtain the drug-taking characteristic Euclidean distance set; S52. According to the existing cardiovascular and cerebrovascular disease severity level data matrix, obtain the cardiovascular and cerebrovascular disease severity level data corresponding to the smallest Euclidean distance data in the drug-taking characteristic Euclidean distance set, and obtain the second to-be-managed cardiovascular and cerebrovascular disease severity level data; S53. When the second to-be-managed cardiovascular and cerebrovascular disease severity level data is different from the first to-be-managed cardiovascular and cerebrovascular disease severity level data, adjust the to-be-managed drug-taking characteristic data set until the second to-be-managed cardiovascular and cerebrovascular disease severity level data is the same as the first to-be-managed cardiovascular and cerebrovascular disease severity level data; otherwise, there is no need to adjust the to-be-managed drug-taking characteristic data set.

8. A cardiovascular and cerebrovascular disease information management system, based on the cardiovascular and cerebrovascular disease information management method according to any one of claims 1 to 7, characterized in that: It includes a cardiovascular and cerebrovascular disease information type setting module, a to-be-managed patient's cardiovascular and cerebrovascular disease information data collection module, an existing cardiovascular and cerebrovascular disease patient data collection module, a cardiovascular and cerebrovascular disease severity level mapping equation construction module, a to-be-managed patient severity level mapping module, an existing patient drug-taking characteristic data collection module, a classification center calculation module, a to-be-managed patient drug-taking characteristic data classification module, and a to-be-managed patient drug-taking characteristic data adjustment module; The cardiovascular and cerebrovascular disease information type setting module is used to set various information types of cardiovascular and cerebrovascular diseases to obtain a cardiovascular and cerebrovascular disease information type set; The to-be-managed patient's cardiovascular and cerebrovascular disease information data collection module is used to collect the cardiovascular and cerebrovascular disease information data of the to-be-managed cardiovascular and cerebrovascular disease patient according to the cardiovascular and cerebrovascular disease information type set, and obtain the to-be-managed cardiovascular and cerebrovascular disease information data set; The existing cardiovascular and cerebrovascular disease patient data collection module is used to collect the cardiovascular and cerebrovascular disease information data and the corresponding cardiovascular and cerebrovascular disease severity level data of multiple existing cardiovascular and cerebrovascular disease patients, and obtain the existing cardiovascular and cerebrovascular disease information data set matrix and the existing cardiovascular and cerebrovascular disease severity level data matrix; The cardiovascular and cerebrovascular disease severity level mapping equation construction module is used to construct the final cardiovascular and cerebrovascular disease severity level mapping equation by using the existing cardiovascular and cerebrovascular disease information data set matrix and the existing cardiovascular and cerebrovascular disease severity level data matrix; The to-be-managed patient severity level mapping module is used to map the to-be-managed cardiovascular and cerebrovascular disease information data set by using the final cardiovascular and cerebrovascular disease severity level mapping equation to obtain the first to-be-managed cardiovascular and cerebrovascular disease severity level data; The existing patient medication feature data acquisition module is used to collect the medication feature data of multiple existing cardiovascular and cerebrovascular disease patients, and obtain an existing medication feature dataset matrix; The classification center calculation module is used to classify the existing medication feature dataset matrix in cooperation with the existing cardiovascular and cerebrovascular disease severity level data matrix and calculate the center data of each classification, so as to obtain an existing medication feature classification center data matrix; The medication feature data classification module of the patient to be managed is used to classify the feature data when the patient to be managed with cardiovascular and cerebrovascular diseases takes medicine according to the existing medication feature classification center data matrix, and obtain the second severity level data of the cardiovascular and cerebrovascular diseases to be managed; The medication feature data adjustment module of the patient to be managed is used to adjust the feature data when the patient to be managed with cardiovascular and cerebrovascular diseases takes medicine according to the second severity level data of the cardiovascular and cerebrovascular diseases to be managed and the first severity level data of the cardiovascular and cerebrovascular diseases to be managed.

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