CN114190943B - Method and system for improving cognition of chronic nephropathy patient based on IKAP - Google Patents

Abstract

The invention discloses a method and system for improving cognition of patients with chronic kidney disease based on IKAP, wherein the method includes: performing psychological intervention on the first user based on the IKAP theory to obtain the first psychological intervention change curve; obtaining the first psychological intervention curve according to the physiological intervention plan The first physiological change curve; the first user is dynamically evaluated to obtain the first exercise capacity change curve; the data of the first psychological intervention change curve, the first physiological change curve and the first exercise capacity change curve are obtained through the multiple linear regression function Fitting, the rehabilitation effect coefficient set is used as the output data set to obtain the coefficient output results; according to the cost function and the coefficient output results, the cognitive state evaluation model is constructed; based on the cognitive state evaluation model, the user's predicted cognitive state evaluation results are obtained, and the evaluation The result is pushed to the first user. It solves the technical problem that patients in the prior art do not have a clear understanding of their self-conditions, which in turn affects the rehabilitation effect.

Description

A method and system for improving cognition of patients with chronic kidney disease based on IKAP

technical field

The invention relates to the field of artificial intelligence, in particular to a method and system for improving cognition of patients with chronic kidney disease based on IKAP.

Background technique

Chronic kidney disease is chronic renal structure and dysfunction caused by various reasons. The diseases that cause chronic kidney disease include various primary and secondary glomerulonephritis, renal tubular injury and renal vascular lesions. In order to improve patients' self-care ability and quality of life, patients' awareness of chronic kidney disease and self should be strengthened, which is of great significance to patients' rehabilitation.

However, in the process of realizing the technical solution of the invention of the present application, it is found that the above-mentioned technology has at least the following technical problems:

In the prior art, the patient's self-cognition is not clear enough, which leads to the problem of affecting the rehabilitation effect.

Contents of the invention

By providing a method and system for improving the cognition of patients with chronic kidney disease based on IKAP, this application solves the technical problem that patients in the prior art do not have a clear understanding of their own situation, which in turn leads to affecting the rehabilitation effect, and achieves the goal of improving the patient's mental state, physiology, and health. Regression analysis of state and exercise ability can improve the patient's self-cognition compliance rate, further improve the patient's cognition level of chronic kidney disease and the mastery of self-management knowledge, and then improve the technical effect of chronic kidney disease rehabilitation effect.

In view of the above problems, the present invention is proposed to provide a method for overcoming the above problems or at least partially solving the above problems.

In the first aspect, the present application provides a method for improving cognition of patients with chronic kidney disease based on IKAP, the method comprising: performing psychological intervention on the first user based on IKAP theory to obtain a first psychological intervention change curve; obtaining the first The user's physiological state information, the physiological state information includes glomerular filtration rate, uric acid, serum albumin, hemoglobin, and urine protein quantification; a physiological intervention plan is formulated based on the physiological state information, and obtained according to the physiological intervention plan The first physiological change curve of the first user; according to the change value of each physiological index in the first physiological change curve, perform a dynamic exercise assessment on the first user to obtain a first exercise ability change curve; obtain a preset division A set of rehabilitation effect coefficients of the first user corresponding to the period; performing data fitting on the first psychological intervention change curve, the first physiological change curve and the first exercise capacity change curve through a multiple linear regression function, Using the set of rehabilitation effect coefficients of the first user as an output data set to obtain the coefficient output results of the multiple linear regression function; according to the cost function and the coefficient output results, construct a cognitive state assessment model; based on the cognition The state evaluation model obtains the user's predicted cognitive state evaluation result, and pushes the user's predicted cognitive state evaluation result to the first user.

On the other hand, the present application also provides a system for improving the cognition of patients with chronic kidney disease based on IKAP, the system includes: a first obtaining unit, the first obtaining unit is used to perform psychological intervention on the first user based on the IKAP theory , to obtain the first psychological intervention change curve; a second obtaining unit, the second obtaining unit is used to obtain the physiological state information of the first user, the physiological state information includes glomerular filtration rate, uric acid, serum protein , hemoglobin, urine protein quantification; a third obtaining unit, the third obtaining unit is used to formulate a physiological intervention plan based on the physiological state information, and obtain the first physiological change curve of the first user according to the physiological intervention plan ; The fourth obtaining unit, the fourth obtaining unit is used to evaluate the dynamic movement of the first user according to the change value of each physiological index in the first physiological change curve, and obtain the first exercise capacity change curve; The obtaining unit, the fifth obtaining unit is used to obtain the set of rehabilitation effect coefficients of the first user corresponding to the preset division period; the sixth obtaining unit, the sixth obtaining unit is used to perform multiple linear regression functions on the Perform data fitting on the first psychological intervention change curve, the first physiological change curve, and the first exercise capacity change curve, and use the set of rehabilitation effect coefficients of the first user as an output data set to obtain the multiple linear regression The coefficient output result of the function; the first construction unit, the first construction unit is used to construct the cognitive state evaluation model according to the cost function and the coefficient output result; the first push unit, the first push unit is used to The cognitive state evaluation model obtains the user's predicted cognitive state evaluation result, and pushes the user's predicted cognitive state evaluation result to the first user.

In a third aspect, the present application provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and operable on the processor, the transceiver, the The memory and the processor are connected through the bus, and the computer program implements the steps in any one of the methods described above when executed by the processor.

In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps in any one of the methods described above are implemented.

One or more technical solutions provided in this application have at least the following technical effects or advantages:

Since the first user is psychologically intervened based on the IKAP theory, the first psychological intervention change curve is obtained, the physiological intervention plan is formulated based on the physiological state information, and the corresponding first physiological change curve is obtained according to the physiological intervention plan. According to the The change value of each physiological index in the first physiological change curve, the dynamic evaluation of the exercise of the first user is performed, and the corresponding first exercise ability change curve is obtained, and the first psychological intervention change curve, the second Perform data fitting between a physiological change curve and the first exercise capacity change curve, use the set of rehabilitation effect coefficients of the first user as an output data set, obtain the regression coefficient of the multiple linear regression function, and obtain the regression coefficient of the multiple linear regression function according to the cost function and The coefficient output results are used to construct a cognitive state evaluation model, and the user's predicted cognitive state evaluation result is evaluated based on the cognitive state evaluation model, and finally the user's predicted cognitive state evaluation result is pushed to the user's technical solution. Furthermore, through regression analysis of the patient's psychological state, physiological state, and exercise ability, the patient's self-cognition compliance rate can be improved, and the patient's awareness of chronic kidney disease and self-management knowledge can be further improved, thereby improving the chronic kidney disease. The technical effect of the rehabilitation effect.

The above description is only an overview of the technical solution of the present application. In order to better understand the technical means of the present application, it can be implemented according to the contents of the description, and in order to make the above and other purposes, features and advantages of the present application more obvious and understandable , the following specifically cites the specific implementation manner of the present application.

Description of drawings

Fig. 1 is a schematic flow chart of a method for improving cognition of patients with chronic kidney disease based on IKAP in the present application;

Fig. 2 is a schematic flow chart of obtaining the first psychological intervention change curve in a method for improving the cognition of patients with chronic kidney disease based on IKAP in the present application;

Fig. 3 is a schematic flow diagram of obtaining a modified standard psychological assessment matrix in a method of improving the cognition of patients with chronic kidney disease based on IKAP in the present application;

Fig. 4 is a schematic flow diagram of obtaining the sensitive factors of each index in the user's psychological evaluation index set in a method for improving the cognition of patients with chronic kidney disease based on IKAP in the present application;

Fig. 5 is a schematic flow chart of generating the first exercise capacity change curve in a method of improving the cognition of patients with chronic kidney disease based on IKAP in the present application;

FIG. 6 is a schematic structural diagram of a system for improving the cognition of patients with chronic kidney disease based on IKAP in the present application;

FIG. 7 is a schematic structural diagram of an exemplary electronic device of the present application.

Explanation of reference numerals: first obtaining unit 11, second obtaining unit 12, third obtaining unit 13, fourth obtaining unit 14, fifth obtaining unit 15, sixth obtaining unit 16, first construction unit 17, first push Unit 18 , bus 1110 , processor 1120 , transceiver 1130 , bus interface 1140 , memory 1150 , operating system 1151 , application program 1152 and user interface 1160 .

Detailed ways

In the description of the present application, those skilled in the art should know that the present application can be implemented as methods, devices, electronic devices, and computer-readable storage media. Therefore, the present application can be implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software. Furthermore, in some embodiments, the present application can also be implemented in the form of a computer program product embodied in one or more computer-readable storage media having computer program code embodied therein.

Any combination of one or more computer-readable storage media may be used for the above-mentioned computer-readable storage medium. The computer-readable storage medium includes: electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or devices, or any combination of the above. More specific examples of computer-readable storage media include: portable computer diskettes, hard disks, random access memory, read-only memory, erasable programmable read-only memory, flash memory, optical fiber, optical disk read-only memory, optical storage devices, magnetic memory or any combination of the above. In this application, a computer-readable storage medium may be any tangible medium containing or storing a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device.

The acquisition, storage, use, and processing of data in the technical solution of this application all comply with the relevant provisions of national laws.

The present application describes the provided methods, devices, and electronic devices through flowcharts and/or block diagrams.

It should be understood that each block of the flowchart and/or block diagram and combinations of blocks in the flowchart and/or block diagram can be implemented by computer readable program instructions. These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, and these computer readable program instructions are executed by the computer or other programmable data processing apparatus to produce means for realizing the functions/operations specified by the blocks in the flowcharts and/or block diagrams.

These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to operate in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product including implementing the functions/operations specified by the blocks in the flowcharts and/or block diagrams.

It is also possible to load computer-readable program instructions on a computer, other programmable data processing apparatus or other equipment, so that a series of operational steps are performed on the computer, other programmable data processing apparatus or other equipment to produce a computer-implemented process, Instructions executed on computers or other programmable data processing devices can thus provide processes for realizing the functions/operations specified in the flowcharts and/or blocks in the block diagrams.

The application is described below in conjunction with the accompanying drawings in the application.

Embodiment one

As shown in Figure 1, the application provides a method for improving the cognition of patients with chronic kidney disease based on IKAP, wherein the method includes:

Step S100: Perform psychological intervention on the first user based on the IKAP theory to obtain a first psychological intervention change curve;

Specifically, the first user is a patient with chronic kidney disease. Chronic kidney disease is chronic kidney structure and dysfunction caused by various reasons. Diseases causing chronic kidney disease include various primary and secondary glomerulonephritis , renal tubular injury and renal vascular disease. If chronic kidney disease is not treated effectively in a timely manner, leading to the deterioration and progression of the disease, as the course of the disease progresses, patients with chronic kidney disease will develop chronic renal insufficiency, renal failure, and eventually uremia.

In order to improve patients' self-care ability and quality of life, patients' awareness of chronic kidney disease and self should be strengthened, which is of great significance to patients' rehabilitation. Based on the IKAP theory, first use a large number of successful cases to intervene on the patient's beliefs and attitudes from the psychological level. The IKAP theory is a theory that integrates Information, Knowledge, Attitude, and Practice. Based on the knowledge-attitude-practice theory, it is a kind of contemporary nursing health education model, which has a better therapeutic effect on the nursing rehabilitation of patients. By performing psychological interventions on patients, including educational interventions, therapeutic interventions such as psychotropic drug treatment, general psychological behavioral interventions, special psychological behavioral interventions, etc., the first psychological intervention change curve corresponding to the patient is obtained, and the first psychological intervention change curve When positive and effective psychological intervention methods are established for patients, the corresponding psychological state of patients can be evaluated by means of psychological assessment and evaluation forms.

Step S200: obtaining the physiological state information of the first user, the physiological state information including glomerular filtration rate, uric acid, serum protein, hemoglobin, urine protein quantification;

Specifically, the physiological state information of the first user is initial physiological state information of a chronic kidney disease patient, mainly including glomerular filtration rate, uric acid, serum protein, hemoglobin, and urine protein quantification. The amount of filtrate produced by the two kidneys per unit time is called the glomerular filtration rate, which is about 125ml/min for normal adults. The glomerular filtration rate is used to understand the decline of renal function in the early stage, and can be used to estimate in the course of chronic kidney disease The degree and development of functional nephron loss are used to guide the diagnosis and treatment of kidney diseases; uric acid is a metabolic waste produced by the body, and when renal function is impaired, the excretion of uric acid will also be affected, resulting in uric acid retention , will induce kidney inflammation and further damage kidney function; serum protein is the carrier of fatty acids in the blood, when the body needs energy or building materials, fat cells release fatty acids into the blood, fatty acids are acquired by serum proteins, and are used Serum protein in patients with chronic kidney disease will be high; hemoglobin is a special protein that transports oxygen in red blood cells, and it is the protein that makes blood red. Blood routine examination of patients with chronic kidney disease will find that hemoglobin decreases, resulting in renal anemia ;Urinary protein quantification refers to the accurate determination of the total amount of protein in all urine within 24 hours. Urinary protein quantitative determination is helpful for the diagnosis and differential diagnosis of urinary system diseases, and to understand the degree of kidney disease. Through the changes of various common physiological indicators, the physiological state of patients with chronic kidney disease can be accurately measured in order to receive accurate treatment.

Step S300: Formulate a physiological intervention plan based on the physiological state information, and obtain a first physiological change curve of the first user according to the physiological intervention plan;

Specifically, a physiological intervention plan is formulated based on the physiological state information, the physiological intervention plan is a treatment plan formulated according to the initial physiological state of the patient, and different kidney disease severity conditions of the patient correspond to different treatment plans. Timely and effective treatment of existing kidney diseases or diseases that may cause kidney damage (such as diabetes, hypertension, etc.) to prevent the occurrence of chronic renal failure; timely treatment of patients with mild or moderate chronic renal failure , to delay, stop or reverse the progress of chronic renal failure, prevent the occurrence of uremia; take early treatment measures for uremia patients, prevent the occurrence of some serious complications of uremia, improve the survival rate and quality of life of patients, including controlling blood pressure , diet, hemodialysis, peritoneal dialysis and kidney transplantation and other treatment measures, according to the specific conditions of patients. According to the physiological intervention plan, the first physiological change curve corresponding to the patient after receiving treatment is obtained, and the first physiological change curve reflects the change of the physiological state information of the patient after different treatment plans. Track and monitor the changes of common physiological indicators in order to know the status of the patient's renal function in a timely manner.

Step S400: According to the change value of each physiological index in the first physiological change curve, perform exercise dynamic evaluation on the first user to obtain a first exercise ability change curve;

As shown in Figure 5, further speaking, the step S400 of obtaining the first exercise capacity change curve of the present application also includes:

Step S410: Obtain a historical physiological index change data set, the historical physiological index change data set includes the change values of various physiological index of historical users;

Step S420: Building an exercise intensity support vector machine based on the historical physiological index change data set;

Step S430: Input the variation values of each physiological index in the first physiological variation curve into the exercise intensity support vector machine to obtain the first exercise intensity evaluation result;

Step S440: Generate a first exercise capacity change curve based on the first exercise intensity evaluation result.

Specifically, with the decline of renal function, patients with chronic kidney disease will experience varying degrees of decline in cardiopulmonary function, muscle atrophy, and physical and psychological dysfunction, which will seriously affect the quality of life of patients, so moderate exercise is required. The historical physiological index change data set includes the change value of each physiological index of each historical user, such as the glomerular filtration rate, uric acid, serum protein, hemoglobin, urinary protein quantitative physiological Indicator change value.

Based on the historical physiological index change data set, the exercise intensity support vector machine is constructed. The support vector machine is a generalized linear classifier that performs binary classification of data in a supervised learning manner, and its decision boundary is the optimal solution for learning samples. hyperplane. SVM uses the hinge loss function to calculate the empirical risk, and adds a regularization term to the solution system to optimize the structural risk. It is a sparse and robust classifier. Input the change values of the physiological indexes in the first physiological change curve into the exercise intensity support vector machine, and obtain the output result of the support vector machine, which is the first exercise intensity evaluation result, and the first exercise intensity evaluation result indicates that the patient can exercise Intensity level, including light exercise such as walking, gymnastics, moderate exercise such as badminton, jogging, and intensity exercise such as skipping rope, swimming, playing basketball and so on.

Based on the first exercise intensity evaluation result, a corresponding first exercise capacity change curve is generated, and the first exercise capacity change curve indicates that as the patient's physiological state changes, the patient's exercise intensity changes accordingly, and the exercise capacity is the patient's Sport level. Individually select the appropriate exercise level based on the patient's physical state to ensure that the patient exercises in a moderate amount, thereby improving the patient's rehabilitation effect.

Step S500: Obtain a set of rehabilitation effect coefficients of the first user corresponding to a preset division period;

Step S600: Perform data fitting on the first psychological intervention change curve, the first physiological change curve, and the first exercise capacity change curve through a multiple linear regression function, and collect the rehabilitation effect coefficients of the first user As an output data set, obtain the coefficient output result of the multiple linear regression function;

Specifically, the preset division period is the clinical treatment period of the kidney disease patient, which is determined according to the patient's treatment status and treatment plan, and the set of rehabilitation effect coefficients of the first user corresponding to the preset division period is obtained. The rehabilitation effect The coefficient set is the rehabilitation effect under the doctor's treatment and the patient's self-cognition. The rehabilitation effect coefficient is the patient's rehabilitation degree. The higher the coefficient, the better the rehabilitation effect. The first psychological intervention change curve, the first physiological change curve, and the first exercise capacity change curve are used as independent variables, and the set of rehabilitation effect coefficients of the first user is used as an output data set. Data fitting is carried out through the multiple linear regression function. The multiple linear regression function predicts or estimates the dependent variable by the optimal combination of multiple independent variables, which is more effective and more realistic than predicting or estimating with only one independent variable. The coefficient output results of the multiple linear regression function are obtained through multiple sets of data fitting, and the coefficient output results include psychological intervention coefficients, physiological change coefficients and exercise capacity coefficients, which are more accurate for subsequent model construction.

Step S700: Construct a cognitive state evaluation model according to the cost function and the coefficient output results;

Specifically, according to the cost function and the output results of the coefficients, a cognitive state assessment model is constructed, and the cognitive state assessment model predicts and evaluates the rehabilitation effect according to the patient's psychological, physiological, and exercise states at this time, in order to achieve Predicting the rehabilitation effect allows patients to have an accurate understanding of self-psychological adjustment, reasonable diet, appropriate exercise, and compliance with medical medication. The cost function is a loss function, which is the loss value parameter of the regression model. Optimizing the cognitive state assessment model through the cost function can reduce model errors and help maintain model stability and accuracy during prediction.

Step S800: Obtain a user's predicted cognitive state evaluation result based on the cognitive state evaluation model, and push the user's predicted cognitive state evaluation result to the first user.

Specifically, based on the cognitive state assessment model, the output of the model is obtained, that is, the patient's predicted cognitive state assessment result, and the user's predicted cognitive state assessment result is the patient's need to recognize the self-state, including psychological, motor , Cognitive outcomes of physiotherapy. Push the evaluation results of the user's predicted cognitive status to the patients and their families, so as to improve the cognitive compliance rate of patients in terms of psychological adjustment, reasonable diet, appropriate exercise, and medication compliance, and further improve patients' awareness of chronic kidney disease and mastery of self-management knowledge, thereby improving the rehabilitation effect of chronic kidney disease.

As shown in Figure 2, further speaking, the step S100 of obtaining the first psychological intervention change curve of the present application also includes:

Step S110: Construct a set of user psychological assessment indicators based on the IKAP theory, the set of user psychological assessment indicators includes psychological strength, psychological tolerance, psychological self-control ability, and psychological rhythmicity;

Step S120: Perform an initial psychological assessment on the first user based on the set of user psychological assessment indicators to obtain an initial psychological assessment matrix;

Step S130: Perform dimensionality reduction processing on the initial psychological assessment matrix to obtain a dimensionality-reduced initial psychological assessment matrix;

Step S140: Obtain a standard psychological assessment matrix in the first preset period, and calculate and obtain a psychological intervention matrix based on the dimensionality reduction initial psychological assessment matrix and the initial psychological assessment matrix;

Step S150: Perform psychological intervention on the first user based on the psychological intervention matrix to obtain a first psychological intervention change curve.

Specifically, based on the IKAP theory, a user psychological evaluation index set is constructed, and the user psychological evaluation index set includes psychological strength, psychological tolerance, psychological self-control ability, and psychological rhythmicity. The psychological strength is an evaluation index of emotional changes, indicating the patient's ability to resist stress; the psychological tolerance is the ability of the patient to bear and adjust the psychological pressure and negative emotions caused by adversity, mainly the adaptability to adversity, The strength of tolerance, endurance, and fighting power; the psychological self-control ability is the self-restraint of the patient when facing the therapeutic exercise, and a strong self-control ability is conducive to self-management; the psychological rhythm is the patient's psychological activity cycle, such as the state of consciousness Rhythmicity, when rhythmicity is low or disrupted, can produce insomnia.

An initial psychological assessment is performed on the first user based on the user psychological assessment index set to obtain an initial psychological assessment matrix, where the initial psychological assessment matrix is an initial psychological assessment index value of the patient. Perform dimensionality reduction processing on the initial psychological evaluation matrix. Dimensionality reduction processing can reduce time complexity and space complexity, remove noise mixed in the matrix data set, and clearly display important features in the data. Common dimensionality reduction algorithms include Principal component analysis, factor analysis, and independent component analysis. The standard psychological assessment matrix in the first preset period is the patient’s psychological assessment value within the expected time set by the doctor according to the patient’s condition, based on the dimensionality reduction initial psychological assessment matrix and the initial psychological assessment matrix , calculate and obtain the matrix difference, that is, the psychological intervention matrix, the psychological intervention matrix is a plan that requires psychological intervention treatment for the patient, based on the psychological intervention matrix, psychological intervention is performed on the first user, and the corresponding psychological state change curve of the patient is obtained. Provide psychological treatment to patients through the psychological intervention matrix, improve the accuracy of psychological intervention for patients, reduce the incidence of psychological problems in patients, and ensure the treatment effect of patients.

As shown in Fig. 3, further speaking, step S150 of this application also includes:

Step S151: performing characteristic classification on the basic information of the first user to obtain each characteristic user information;

Step S152: Classify the characteristic user information according to a predetermined classification standard, and obtain the corresponding characteristic user grade information;

Step S153: Based on the level information of each feature user, perform sensitivity analysis on each index in the user psychological evaluation index set, and obtain the sensitivity factor of each index;

Step S154: Generate a sensitivity correction matrix according to the sensitivity factors of each index;

Step S155: Multiply the standard psychological assessment matrix according to the sensitivity correction matrix to obtain a revised standard psychological assessment matrix.

Specifically, feature classification is performed on the basic information of the first user, and each feature user information is obtained, and each feature user information includes patient gender, age, weight, clinical stage, and the like. Classify the characteristic user information according to the predetermined classification standard, the predetermined classification standard is the medical classification standard of chronic kidney disease, and the various characteristic user level information is the classification level of the corresponding characteristic information of the patient, such as the clinical classification of the patient is serious grade. Based on the level information of each feature user, sensitivity analysis is performed on each index in the user psychological evaluation index set, the sensitivity analysis is to analyze the importance of each psychological evaluation index, and obtain the sensitivity factor corresponding to each index, so Sensitive factors of each index reflect the importance of each psychological index. According to the sensitivity factors of each index, a sensitivity correction matrix is generated, and the sensitivity correction matrix is a correction factor considering the sensitivity factors of each psychological index, and the sensitivity correction matrix is multiplied by the standard psychological evaluation matrix to obtain the corrected standard psychological assessment matrix. Combining the importance of each psychological index to modify the patient's psychological evaluation index, the patient's psychological evaluation result is more accurate, which in turn enables the doctor to formulate a more reasonable and accurate psychological intervention plan.

As shown in Figure 4, further, wherein, the construction of the three-dimensional monitoring model library, step S153 of the present application also includes:

Step S1531: Input the characteristic user information and the indicators into the correlation analysis model to obtain a first correlation analysis result, and the first correlation analysis result includes the correlation analysis results of each index;

Step S1532: Obtain the sensitivity coefficient of each index based on the sum of the products of the characteristic user level information and the correlation analysis results of each index;

Step S1533: Calculate the average value of the analysis results of the correlation degree of each index respectively, and obtain the average correlation degree of each index;

Step S1534: Use the average correlation degree of each index as the slope of the logical line of the coordinate axis, and the sensitivity coefficient of each index as the length value of the logical line of the coordinate axis, calculate and obtain the area value of the logical line of the coordinate axis, and calculate the The above area value is used as the sensitivity factor of each index.

Specifically, input the characteristic user information and the indicators into the correlation degree analysis model, and the correlation degree analysis model is a neural network model, which is used to analyze the correlation degree between the patient information and each psychological index, and obtain the The output result of the model is the first correlation degree analysis result, and the first correlation degree analysis result includes the correlation degree analysis results of each index. Calculate the sum of the products of the level information of each characteristic user and the correlation analysis results of each index, such as the sum of the products of the psychological self-control ability index and the level information of each characteristic user, as the sensitivity coefficient of the index. Calculate the average value of the analysis results of the correlation degree of each index respectively, obtain the average correlation degree of each psychological index, use the average correlation degree of each index as the slope of the logical line of the coordinate axis, and use the sensitivity coefficient of each index as the logical line slope of the coordinate axis For the length value of the line, calculate the area value of the logical line of the coordinate axis surrounded by the coordinate axes, and use the area value as the sensitivity factor of each indicator. The higher the sensitivity factor, the higher the importance of the indicator. Combining the index sensitive factors for psychological assessment can make the patient's psychological assessment result more accurate, which in turn enables the doctor to formulate a more reasonable and accurate psychological intervention plan.

Further, step S420 of this application also includes:

Step S421: Divide the historical physiological index change data set according to a predetermined ratio to obtain a first physiological index training sample and a first physiological index test sample;

Step S422: According to the first physiological index training sample and the first physiological index test sample, obtain the exercise intensity evaluation label of the first training sample and the exercise intensity evaluation label of the first test sample;

Step S423: Using the first physiological index training sample and the exercise intensity evaluation label of the first training sample as training data, construct the exercise intensity support vector machine.

Specifically, the historical physiological index change data set is randomly divided according to a predetermined ratio, for example, the training sample and the test sample are randomly divided according to the ratio of 6:4, and the corresponding first physiological index training sample and first physiological index training sample after division are obtained. Physiological index test samples. According to the first physiological index training sample and the first physiological index test sample, obtain the exercise intensity evaluation label of the first training sample and the exercise intensity assessment label of the first test sample, such as a high exercise intensity label, a moderate exercise intensity label, Light exercise intensity label.

The first physiological indicator training sample and the exercise intensity evaluation label of the first training sample are used as training data to construct the exercise intensity support vector machine. Wherein, if the physiological index training samples are linearly separable data, and after classification, they respectively correspond to exercise intensity labels, the first training physiological index training samples are used for training to obtain a linear classification support vector machine. If the physiological index training samples are linear inseparable data, use the kernel function in the support vector machine to map each physiological index training sample to a high-dimensional space, and then train the physiological index training samples using the optimal separation hyperplane in this high-dimensional space. Samples are classified. The exercise intensity support vector machine is obtained through training, which makes up for the shortcomings of the neural network such as slow convergence speed and overfitting, and provides a fast and effective method for evaluating the exercise intensity of patients, and then quickly and efficiently screens patients for accurate exercise intensity. and classified technical effects.

Further, step S423 of this application also includes:

Step S4231: input the first physiological index test sample into the exercise intensity support vector machine, and obtain the evaluation results of each exercise intensity in the first physiological index test sample;

Step S4232: Comparing the exercise intensity assessment results with the exercise intensity assessment label of the first test sample to obtain the assessment accuracy of the exercise intensity support vector machine.

Specifically, in order to detect the accuracy of the exercise intensity support vector machine, the first physiological index test sample is input into the exercise intensity support vector machine, and the output result of the support vector machine is the first physiological index test sample The results of each exercise intensity assessment in the Comparing the exercise intensity assessment results with the first test sample exercise intensity assessment label, when the exercise intensity assessment result is consistent with the first test sample exercise intensity assessment label, it indicates that the exercise intensity support vector machine assessment is accurate.

The number of samples with accurate evaluation and incorrect evaluation in the first physiological index test samples is counted respectively, and then the ratio of the number of accurate evaluation samples to the total number of first physiological index test samples is calculated. When the evaluation accuracy of the exercise intensity support vector machine reaches the standard, it shows that the evaluation performance of the support vector machine is up to the standard, and when the accuracy does not reach the standard, the number of training samples can be increased, the evaluation accuracy of the support vector machine can be improved, and the support is objectively and effectively supported. The accuracy of the vector machine is determined, thereby ensuring the accuracy of the screening and classification of the patient's exercise intensity.

In summary, a method and system for improving cognition of patients with chronic kidney disease based on IKAP provided by this application has the following technical effects:

Since the first user is psychologically intervened based on the IKAP theory, the first psychological intervention change curve is obtained, the physiological intervention plan is formulated based on the physiological state information, and the corresponding first physiological change curve is obtained according to the physiological intervention plan. According to the The change value of each physiological index in the first physiological change curve, the dynamic evaluation of the exercise of the first user is performed, and the corresponding first exercise ability change curve is obtained, and the first psychological intervention change curve, the second Perform data fitting between a physiological change curve and the first exercise capacity change curve, use the set of rehabilitation effect coefficients of the first user as an output data set, obtain the regression coefficient of the multiple linear regression function, and obtain the regression coefficient of the multiple linear regression function according to the cost function and The coefficient output results are used to construct a cognitive state evaluation model, and the user's predicted cognitive state evaluation result is evaluated based on the cognitive state evaluation model, and finally the user's predicted cognitive state evaluation result is pushed to the user's technical solution. Furthermore, through regression analysis of the patient's psychological state, physiological state, and exercise ability, the patient's self-cognition compliance rate can be improved, and the patient's awareness of chronic kidney disease and self-management knowledge can be further improved, thereby improving the chronic kidney disease. The technical effect of the rehabilitation effect.

Embodiment two

Based on the same inventive idea as a method for improving the cognition of patients with chronic kidney disease based on IKAP in the previous embodiment, the present invention also provides a system for improving the cognition of patients with chronic kidney disease based on IKAP, as shown in FIG. 6 , the system includes :

A first obtaining unit 11, the first obtaining unit 11 is used to perform psychological intervention on the first user based on the IKAP theory, and obtain a first psychological intervention change curve;

The second obtaining unit 12, the second obtaining unit 12 is used to obtain the physiological state information of the first user, the physiological state information includes glomerular filtration rate, uric acid, serum protein, hemoglobin, urine protein quantification;

A third obtaining unit 13, configured to formulate a physiological intervention plan based on the physiological state information, and obtain a first physiological change curve of the first user according to the physiological intervention plan;

A fourth obtaining unit 14, the fourth obtaining unit 14 is used to evaluate the dynamic movement of the first user according to the change values of the physiological indicators in the first physiological change curve, and obtain the first exercise capacity change curve;

A fifth obtaining unit 15, the fifth obtaining unit 15 is configured to obtain a set of rehabilitation effect coefficients of the first user corresponding to a preset division period;

The sixth obtaining unit 16, the sixth obtaining unit 16 is used to perform data fitting on the first psychological intervention change curve, the first physiological change curve and the first exercise capacity change curve through a multiple linear regression function , using the rehabilitation effect coefficient set of the first user as an output data set to obtain the coefficient output result of the multiple linear regression function;

A first construction unit 17, configured to construct a cognitive state assessment model according to the cost function and the coefficient output results;

A first push unit 18, configured to obtain a user's predicted cognitive state evaluation result based on the cognitive state evaluation model, and push the user's predicted cognitive state evaluation result to the first user.

Further, the system also includes:

A second construction unit, the second construction unit is used to construct a set of user psychological assessment indicators based on the IKAP theory, and the set of user psychological assessment indicators includes psychological strength, psychological tolerance, psychological self-control ability, and psychological rhythmicity;

A seventh obtaining unit, the seventh obtaining unit is configured to perform an initial psychological evaluation on the first user based on the set of user psychological evaluation indicators, and obtain an initial psychological evaluation matrix;

An eighth obtaining unit, the eighth obtaining unit is configured to perform dimensionality reduction processing on the initial psychological assessment matrix to obtain a dimensionality-reduced initial psychological assessment matrix;

A ninth obtaining unit, the ninth obtaining unit is used to obtain a standard psychological assessment matrix in the first preset period, and calculate and obtain a psychological intervention matrix based on the dimensionality reduction initial psychological assessment matrix and the initial psychological assessment matrix;

A tenth obtaining unit, the tenth obtaining unit is configured to perform psychological intervention on the first user based on the psychological intervention matrix, and obtain a first psychological intervention change curve.

Further, the system also includes:

An eleventh obtaining unit, the eleventh obtaining unit is configured to perform characteristic classification on the basic information of the first user, and obtain each characteristic user information;

A twelfth obtaining unit, the twelfth obtaining unit is configured to classify the characteristic user information according to a predetermined classification standard, and obtain corresponding characteristic user level information;

A thirteenth obtaining unit, the thirteenth obtaining unit is configured to perform a sensitivity analysis on each index in the user psychological evaluation index set based on the characteristic user level information, and obtain a sensitivity factor of each index;

a first generation unit, the first generation unit is configured to generate a sensitivity correction matrix according to the sensitivity factors of the indicators;

A fourteenth obtaining unit, the fourteenth obtaining unit is configured to multiply the standard psychological evaluation matrix according to the sensitive correction matrix to obtain a modified standard psychological evaluation matrix.

Further, the system also includes:

A fifteenth obtaining unit, the fifteenth obtaining unit is configured to input the characteristic user information and the indicators into the correlation analysis model to obtain a first correlation analysis result, the first correlation analysis result includes The results of the correlation analysis of each indicator;

A sixteenth obtaining unit, the sixteenth obtaining unit is used to obtain the sensitivity coefficient of each index based on the sum of the product of each characteristic user level information and the correlation analysis result of each index;

A seventeenth obtaining unit, the seventeenth obtaining unit is used to calculate the average value of the analysis results of the correlation degree of each index respectively, and obtain the average correlation degree of each index;

The eighteenth obtaining unit, the eighteenth obtaining unit is used to calculate and obtain the average correlation degree of each index as the slope of the logical line of the coordinate axis, and the sensitivity coefficient of each index as the length value of the logical line of the coordinate axis The area value of the logical line of the coordinate axis is used as the sensitivity factor of each index.

Further, the system also includes:

A nineteenth obtaining unit, the nineteenth obtaining unit is used to obtain a historical physiological index change data set, the historical physiological index change data set includes the change values of various physiological index of historical users;

A third construction unit, the third construction unit is used to construct an exercise intensity support vector machine based on the historical physiological index change data set;

A twentieth obtaining unit, the twentieth obtaining unit is configured to input the change value of each physiological index in the first physiological change curve into the exercise intensity support vector machine to obtain the first exercise intensity evaluation result;

A second generation unit, configured to generate a first exercise capacity change curve based on the first exercise intensity evaluation result.

Further, the system also includes:

A twenty-first obtaining unit, the twenty-first obtaining unit is configured to divide the historical physiological index change data set according to a predetermined ratio, and obtain a first physiological index training sample and a first physiological index test sample;

A twenty-second obtaining unit, the twenty-second obtaining unit is used to obtain the exercise intensity evaluation label of the first training sample and the first test sample according to the first physiological index training sample and the first physiological index test sample exercise intensity assessment label;

A fourth construction unit, the fourth construction unit is configured to use the first physiological index training sample and the exercise intensity evaluation label of the first training sample as training data to construct the exercise intensity support vector machine.

Further, the system also includes:

The twenty-third obtaining unit is configured to input the first physiological index test sample into the exercise intensity support vector machine, and obtain each exercise intensity evaluation result in the first physiological index test sample ;

A twenty-fourth obtaining unit, the twenty-fourth obtaining unit is configured to compare the exercise intensity evaluation results with the first test sample exercise intensity evaluation label, and obtain the evaluation accuracy of the exercise intensity support vector machine.

Various variations and specific examples of a method for improving cognition of patients with chronic kidney disease based on IKAP in the first embodiment of Figure 1 are also applicable to a system for improving cognition of patients with chronic kidney disease based on IKAP in this embodiment. For a detailed description of a method for improving the cognition of patients with chronic kidney disease based on IKAP, those skilled in the art can clearly know the implementation method of a system for improving the cognition of patients with chronic kidney disease based on IKAP in this embodiment, so for the sake of brevity of the description , which will not be described in detail here.

In addition, the present application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and operable on the processor. The transceiver, the memory, and the processor are respectively connected via the bus Connected, when the computer program is executed by the processor, the various processes of the above-mentioned method embodiments for controlling output data can be realized, and the same technical effect can be achieved. To avoid repetition, details are not repeated here.

Exemplary electronic device

Specifically, referring to FIG. 7 , the present application also provides an electronic device, which includes a bus 1110 , a processor 1120 , a transceiver 1130 , a bus interface 1140 , a memory 1150 and a user interface 1160 .

In the present application, the electronic device further includes: a computer program stored in the memory 1150 and operable on the processor 1120 , and when the computer program is executed by the processor 1120 , each process of the above-mentioned method embodiment for controlling output data is realized.

The transceiver 1130 is used for receiving and sending data under the control of the processor 1120 .

In this application, the bus architecture (represented by the bus 1110), the bus 1110 may include any number of interconnected buses and bridges, the bus 1110 will include one or more processors represented by the processor 1120 and each of the memory represented by the memory 1150 The circuits are connected together.

Bus 1110 represents one or more of any of several types of bus structures, including a memory bus and memory controller, a peripheral bus, an accelerated graphics port, a processor, or any bus using any of a variety of bus architectures Structured local bus. Such architectures include, by way of example and not limitation: Industry Standard Architecture bus, Micro Channel Architecture bus, Expansion bus, Video Electronics Standards Association, Peripheral Component Interconnect bus.

The processor 1120 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above-mentioned method embodiment can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software. The above-mentioned processors include: general-purpose processors, central processing units, network processors, digital signal processors, application-specific integrated circuits, field programmable gate arrays, complex programmable logic devices, programmable logic arrays, micro-control units or other Program logic devices, discrete gates, transistor logic devices, discrete hardware components. Each method, step and logic block diagram disclosed in this application can be realized or executed. For example, the processor may be a single-core processor or a multi-core processor, and the processor may be integrated in a single chip or located in multiple different chips.

Processor 1120 may be a microprocessor or any conventional processor. The method steps disclosed in connection with this application can be directly executed by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in readable storage media known in the art such as random access memory, flash memory, read-only memory, programmable read-only memory, erasable programmable read-only memory, and registers. The readable storage medium is located in the memory, and the processor reads the information in the memory, and completes the steps of the above method in combination with its hardware.

Bus 1110 may also connect together various other circuits, such as peripherals, voltage regulators, or power management circuits, and bus interface 1140 provides an interface between bus 1110 and transceiver 1130, as is known in the art. Therefore, it will not be further described in this application.

Transceiver 1130 may be a single element or multiple elements, such as multiple receivers and transmitters, providing a means for communicating with various other devices over a transmission medium. For example: the transceiver 1130 receives external data from other devices, and the transceiver 1130 is used to send the data processed by the processor 1120 to other devices. Depending on the nature of the computer device, a user interface 1160 may also be provided such as: touch screen, physical keyboard, display, mouse, speaker, microphone, trackball, joystick, stylus.

It should be understood that in the present application, the memory 1150 may further include a memory that is remotely set relative to the processor 1120, and these remotely set memories may be connected to a server through a network. One or more portions of the aforementioned network may be an ad hoc network, an intranet, an extranet, a virtual private network, a local area network, a wireless local area network, a wide area network, a wireless wide area network, a metropolitan area network, the Internet, a public switched telephone network, a plain old telephone service network , a cellular telephone network, a wireless network, a Wi-Fi network, and combinations of two or more of the foregoing. For example, cellular telephone networks and wireless networks may be GSM devices, Code Division Multiple Access devices, Global Interoperability for Microwave Access devices, General Packet Radio Service devices, Wideband Code Division Multiple Access devices, Long Term Evolution devices, LTE Frequency Division Duplex devices, LTE time division duplex devices, advanced long-term evolution devices, universal mobile communication devices, enhanced mobile broadband devices, massive machine-type communication devices, ultra-reliable low-latency communication devices, etc.

It should be understood that the memory 1150 in the present application may be a volatile memory or a nonvolatile memory, or may include both volatile memory and nonvolatile memory. Wherein, the non-volatile memory includes: read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, or flash memory.

Volatile memory includes: Random Access Memory, which acts as external cache memory. By way of illustration and not limitation, many forms of RAM are available, such as: static random access memory, dynamic random access memory, synchronous dynamic random access memory, double data rate synchronous dynamic random access memory, enhanced synchronous Dynamic random access memory, synchronously attached dynamic random access memory, and direct memory bus random access memory. The memory 1150 of the electronic device described herein includes, but is not limited to, the above and any other suitable type of memory.

In this application, the memory 1150 stores the following elements of the operating system 1151 and the application program 1152: executable modules, data structures, or a subset thereof, or an extended set thereof.

Specifically, the operating system 1151 includes various device programs, such as: framework layer, core library layer, driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application program 1152 includes various application programs, such as a media player and a browser, for implementing various application services. The program for implementing the method of this application may be included in the application program 1152 . Application programs 1152 include applets, objects, components, logic, data structures, and other computer device-executable instructions that perform particular tasks or implement particular abstract data types.

In addition, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, each process of the above-mentioned method embodiment for controlling output data is realized, and the same technical Effect, in order to avoid repetition, will not repeat them here.

The above is only the specific implementation of the application, but the scope of protection of the application is not limited thereto. Anyone familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the application. Should be covered within the protection scope of this application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims (9)

1. A method for improving the cognition of patients with chronic kidney disease based on IKAP, characterized in that the method comprises: Based on the IKAP theory, psychological intervention is performed on the first user, and the first psychological intervention change curve is obtained; Obtaining the physiological state information of the first user, the physiological state information including glomerular filtration rate, uric acid, serum protein, hemoglobin, urine protein quantification; Formulate a physiological intervention plan based on the physiological state information, and obtain a first physiological change curve of the first user according to the physiological intervention plan; According to the change value of each physiological index in the first physiological change curve, the first user is dynamically evaluated to obtain the first exercise ability change curve, specifically, a historical physiological index change data set is obtained, and the historical physiological index The change data set includes the change values of various physiological indicators of historical users; Building an exercise intensity support vector machine based on the historical physiological index change data set; Inputting the change values of each physiological index in the first physiological change curve into the exercise intensity support vector machine to obtain the first exercise intensity evaluation result; generating a first exercise capacity change curve based on the first exercise intensity evaluation result; Obtain a set of rehabilitation effect coefficients of the first user corresponding to a preset division period; Perform data fitting on the first psychological intervention change curve, the first physiological change curve, and the first exercise capacity change curve through a multiple linear regression function, and use the set of rehabilitation effect coefficients of the first user as output data Set, obtain the coefficient output result of described multiple linear regression function; Constructing a cognitive state assessment model according to the cost function and the coefficient output results; Obtaining a user's predicted cognitive state evaluation result based on the cognitive state evaluation model, and pushing the user's predicted cognitive state evaluation result to the first user. 2. The method according to claim 1, wherein said obtaining the first psychological intervention change curve comprises: Based on the IKAP theory, a set of user psychological evaluation indicators is constructed, which includes psychological strength, psychological tolerance, psychological self-control ability, and psychological rhythmicity; performing an initial psychological assessment on the first user based on the set of user psychological assessment indicators to obtain an initial psychological assessment matrix; performing dimensionality reduction processing on the initial psychological assessment matrix to obtain a dimensionality-reduced initial psychological assessment matrix; Obtaining a standard psychological assessment matrix in the first preset period, calculating and obtaining a psychological intervention matrix based on the dimensionality reduction initial psychological assessment matrix and the initial psychological assessment matrix; Based on the psychological intervention matrix, psychological intervention is performed on the first user to obtain a first psychological intervention change curve. 3. The method of claim 2, wherein the method comprises: Perform feature classification on the basic information of the first user to obtain each feature user information; Classify the characteristic user information according to a predetermined classification standard, and obtain corresponding characteristic user grade information; Based on the level information of each characteristic user, perform a sensitivity analysis on each index in the user psychological evaluation index set, and obtain a sensitivity factor of each index; Generate a sensitivity correction matrix according to the sensitivity factors of each index; The standard psychological assessment matrix is multiplied according to the sensitivity correction matrix to obtain a revised standard psychological assessment matrix. 4. The method according to claim 3, wherein said obtaining the sensitivity factors of each index comprises: Inputting the characteristic user information and the indicators into the correlation analysis model to obtain a first correlation analysis result, the first correlation analysis result including the correlation analysis results of each index; Based on the sum of the products of the characteristic user level information and the correlation analysis results of each index, the sensitivity coefficient of each index is obtained; Calculate the average value of the analysis results of the correlation degree of each index respectively, and obtain the average correlation degree of each index; The average correlation degree of each index is used as the slope of the logical line of the coordinate axis, and the sensitivity coefficient of each index is used as the length value of the logical line of the coordinate axis to calculate the area value of the logical line of the coordinate axis, and the area value Sensitivity factors for the above indicators. 5. method as claimed in claim 4, is characterized in that, described construction motion strength support vector machine comprises: Dividing the historical physiological index change data set according to a predetermined ratio to obtain a first physiological index training sample and a first physiological index test sample; According to the first physiological index training sample and the first physiological index test sample, obtain the exercise intensity evaluation label of the first training sample and the exercise intensity evaluation label of the first test sample; The first physiological indicator training sample and the exercise intensity evaluation label of the first training sample are used as training data to construct the exercise intensity support vector machine. 6. The method of claim 5, wherein the method comprises: Input the first physiological index test sample into the exercise intensity support vector machine, and obtain the evaluation results of each exercise intensity in the first physiological index test sample; Comparing the exercise intensity evaluation results with the exercise intensity evaluation label of the first test sample to obtain the evaluation accuracy of the exercise intensity support vector machine. 7. A system for improving the cognition of patients with chronic kidney disease based on IKAP, characterized in that the system comprises: A first obtaining unit, the first obtaining unit is used to perform psychological intervention on the first user based on the IKAP theory, and obtain a first psychological intervention change curve; A second obtaining unit, the second obtaining unit is used to obtain the physiological state information of the first user, the physiological state information includes glomerular filtration rate, uric acid, serum protein, hemoglobin, urine protein quantification; A third obtaining unit, configured to formulate a physiological intervention plan based on the physiological state information, and obtain a first physiological change curve of the first user according to the physiological intervention plan; A fourth obtaining unit, the fourth obtaining unit is configured to perform exercise dynamic evaluation on the first user according to the change values of each physiological index in the first physiological change curve, and obtain the first exercise capacity change curve; A nineteenth obtaining unit, the nineteenth obtaining unit is used to obtain a historical physiological index change data set, the historical physiological index change data set includes the change values of various physiological index of historical users; A third construction unit, the third construction unit is used to construct an exercise intensity support vector machine based on the historical physiological index change data set; A twentieth obtaining unit, the twentieth obtaining unit is configured to input the change value of each physiological index in the first physiological change curve into the exercise intensity support vector machine to obtain the first exercise intensity evaluation result; A second generating unit, configured to generate a first exercise capacity change curve based on the first exercise intensity evaluation result; A fifth obtaining unit, the fifth obtaining unit is configured to obtain a set of rehabilitation effect coefficients of the first user corresponding to a preset division period; A sixth obtaining unit, the sixth obtaining unit is used to perform data fitting on the first psychological intervention change curve, the first physiological change curve and the first exercise capacity change curve through a multiple linear regression function, and The recovery effect coefficient set of the first user is used as an output data set, and the coefficient output result of the multiple linear regression function is obtained; A first construction unit, the first construction unit is used to construct a cognitive state assessment model according to the cost function and the coefficient output results; A first push unit, configured to obtain a user's predicted cognitive state evaluation result based on the cognitive state evaluation model, and push the user's predicted cognitive state evaluation result to the first user. 8. An electronic device based on IKAP to improve the cognition of patients with chronic kidney disease, comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and operable on the processor, the transceiver . The memory and the processor are connected through the bus, wherein the computer program implements the steps in the method according to any one of claims 1-6 when executed by the processor. 9. A computer-readable storage medium, on which a computer program is stored, wherein when the computer program is executed by a processor, the steps in the method according to any one of claims 1-6 are realized.

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