Cognitive disorder reversal evaluation method and device, electronic equipment and storage medium

Abstract

The invention provides a cognitive disorder reversal evaluation method and device, electronic equipment and a storage medium, and relates to the technical field of medicine. The method comprises the following steps: acquiring target information; the target information comprises a plurality of variables of different dimensions, and each variable is used for indicating at least one of a lifestyle, a dietary habit, mental health, a physical condition, an age and a gender of a target; screening a plurality of variables in the target information to select at least one target variable having significant influence on cognitive disorder reversal of the target from the target information; inputting the selected target variable into a reverse evaluation model to evaluate the cognitive impairment of the target to obtain an evaluation result; the evaluation result is used for indicating the possibility of reversal of cognitive impairment of the target. According to the method and the apparatus, the problem of low accuracy of individual cognitive impairment reversal evaluation in related technologies is solved.

Description

Technical Field

[0001] The present application relates to the field of medical technology, and in particular, to a method, device, electronic device and storage medium for evaluating the reversal of cognitive impairment. Background Art

[0002] Mild cognitive impairment (MCI) is a cognitive state between normal aging and dementia, and is often considered to be the early stage of dementia, especially Alzheimer's disease (AD). As a high-risk factor, a significant proportion of MCI patients will progress to dementia within a few years. However, studies have shown that some MCI patients can reverse to a normal cognitive state within a certain period of time, and the possibility of reversal is closely related to the individual characteristics of the patient and intervention measures.

[0003] Although some MCI patients can be reversed to a normal cognitive state, it is difficult to quantitatively predict which patients have a higher probability of reversal. Currently, a rough assessment can be made based on group data, but it cannot provide an accurate reference for individualized treatment plans, and the failure to accurately assess the probability of reversal for individual situations leads to uncertainty in the effectiveness of patient management and intervention.

[0004] From the above, we can see that how to improve the accuracy of the assessment of reversal of cognitive impairment in individuals remains to be solved. Summary of the invention

[0005] The present application provides a method, device, electronic device and storage medium for evaluating cognitive impairment reversal, which can solve the problem of low accuracy of cognitive impairment reversal evaluation in related technologies. The technical solution is as follows:

[0006] According to one aspect of the present application, a method for assessing reversal of cognitive impairment includes: obtaining target information; the target information includes variables of multiple dimensions, each of which is used to indicate at least one of the target's lifestyle, eating habits, mental health, physical condition, age, and gender; screening multiple variables in the target information to select at least one target variable from the target information that has a significant effect on the reversal of the target's cognitive impairment; inputting the selected target variable into a reversal assessment model to assess the target's cognitive impairment and obtain an assessment result; the assessment result is used to indicate the possibility of reversal of the target's cognitive impairment; the reversal assessment model is a trained machine learning model that has the ability to assess the target's cognitive impairment.

[0007] According to one aspect of the present application, a cognitive impairment reversal assessment device includes: an information acquisition module for acquiring target information; the target information includes variables of multiple different dimensions, each of which is used to indicate at least one of the target's lifestyle, eating habits, mental health, physical condition, age, and gender; a variable screening module for screening multiple variables in the target information to select at least one target variable from the target information that has a significant effect on the reversal of the target's cognitive impairment; a cognitive assessment module for inputting the selected target variable into a reversal assessment model to assess the target's cognitive impairment and obtain an assessment result; the assessment result is used to indicate the possibility of reversal of the target's cognitive impairment; the reversal assessment model is a trained machine learning model that has the ability to assess the target's cognitive impairment.

[0008] According to one aspect of the present application, an electronic device includes at least one processor and at least one memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the cognitive impairment reversal assessment method as described above is implemented.

[0009] According to one aspect of the present application, a storage medium stores a computer program thereon, and when the computer program is executed by one or more processors, the method for assessing the reversal of cognitive impairment as described above is implemented.

[0010] According to one aspect of the present application, a computer program product includes a computer program, and when the computer program is executed by one or more processors, the method for assessing the reversal of cognitive impairment as described above is implemented.

[0011] The beneficial effects of the technical solution provided by this application are:

[0012] In the above technical solution, by obtaining target information and integrating variables of different dimensions (such as demographics, lifestyle, psychological state, physiological condition, genetic information, etc.), a comprehensive analysis of the development and reversal of mild cognitive impairment (MCI) can be achieved, avoiding the limitations of single-dimensional evaluation. The screened target variables are input into the reversal evaluation model to provide personalized cognitive impairment reversal evaluation, thereby improving the accuracy of individual cognitive impairment reversal evaluation and providing a scientific basis for formulating tailored intervention measures for each target. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application, and those skilled in the art can obtain other drawings based on these drawings without creative work.

[0014] Figure 1 is a hardware structure diagram of an electronic device according to an exemplary embodiment;

[0015] Figure 2 is a flow chart of a cognitive impairment reversal assessment method according to an exemplary embodiment;

[0016] Figure 3 yes Figure 2 A flowchart of step 350 in one embodiment corresponding to the embodiment;

[0017] Figure 4 yes Figure 2 A flowchart of an embodiment of training a reversal evaluation model in a corresponding embodiment;

[0018] Figure 5 It is a schematic diagram of a specific implementation of a cognitive impairment reversal assessment method in an application scenario;

[0019] Figure 6 is a structural block diagram of a cognitive impairment reversal assessment device according to an exemplary embodiment;

[0020] Figure 7 The present invention is a structural block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0021] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be interpreted as limiting the present application.

[0022] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present disclosure refers to the presence of the features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein includes all or any unit and all combinations of one or more associated listed items.

[0023] As mentioned previously, the failure to accurately estimate the probability of reversal for individual circumstances has resulted in uncertainty about the effectiveness of patient management and interventions.

[0024] Existing studies are mostly based on group data analysis, which can provide an assessment of overall trends but lack individualized prediction tools. The heterogeneity between individuals (such as lifestyle, genetic background, health status, etc.) makes it difficult to accurately apply group prediction results to individual patients.

[0025] Moreover, existing studies often focus on only one influencing factor, such as lifestyle or dietary habits, and fail to integrate multi-dimensional data for cognitive impairment reversal assessment. In addition, due to the failure to fully utilize modern statistical and machine learning methods, existing cognitive impairment reversal assessment tools cannot achieve high-precision reversal predictions.

[0026] From the above, it can be seen that the relevant technology still has the defect of low accuracy in assessing the reversal of cognitive impairment of individuals.

[0027] To this end, the cognitive impairment reversal assessment method provided in the present application can effectively improve the accuracy of the cognitive impairment reversal assessment of an individual. Accordingly, the cognitive impairment reversal assessment method is suitable for a cognitive impairment reversal assessment device, which can be deployed in an electronic device, which can be a computer device configured with a von Neumann architecture, for example, the computer device includes a desktop computer, a laptop computer, a server, etc.

[0028] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below in conjunction with the accompanying drawings.

[0029] See also Figure 1 , Figure 1 The figure is a hardware structure diagram of an electronic device according to an exemplary embodiment.

[0030] It should be noted that the electronic device is only an example adapted to the present application and cannot be considered to provide any limitation on the scope of use of the present application. The electronic device cannot be interpreted as requiring dependence on or having Figure 2 One or more components of exemplary electronic device 200 are shown.

[0031] The hardware structure of the electronic device 200 may vary greatly due to different configurations or performances, such as Figure 2 As shown, the electronic device 200 includes: a power supply 210 , an interface 230 , at least one memory 250 , and at least one central processing unit (CPU) 270 .

[0032] Specifically, the power supply 210 is used to provide operating voltage for each hardware device on the electronic device 200 .

[0033] The interface 230 includes at least one wired or wireless network interface 231 for interacting with external devices.

[0034] Of course, in other examples adapted by the present application, the interface 230 may further include at least one serial-to-parallel conversion interface 233, at least one input-output interface 235, and at least one USB interface 237, etc. Figure 2 As shown, this is not intended to be a specific limitation.

[0035] The memory 250 is a carrier for storing resources, which may be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon include an operating system 251, an application 253 and data 255, etc. The storage method may be temporary storage or permanent storage.

[0036] Among them, the operating system 251 is used to manage and control various hardware devices and application programs 253 on the electronic device 200 to enable the central processor 270 to calculate and process the massive data 255 in the memory 250. It can be WindowsServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, etc.

[0037] The application program 253 is a computer program formed by computer-readable instructions based on the operating system 251 to complete at least one specific task, and may include at least one module ( Figure 2 Each module may include corresponding computer-readable instructions. For example, the cognitive impairment reversal assessment device may be considered as an application 253 deployed on the electronic device 200.

[0038] The data 255 may be photos, pictures, etc. stored in a disk, or may be target information, etc. stored in the memory 250 .

[0039] The central processor 270 may include one or more processors and is configured to communicate with the memory 250 through at least one communication bus to read the computer program stored in the memory 250, thereby realizing the operation and processing of the mass data 255 in the memory 250. For example, the cognitive impairment reversal assessment method is completed by the central processor 270 reading the application program 253 stored in the memory 250.

[0040] In addition, the present application can also be implemented through hardware circuits or hardware circuits combined with software. Therefore, the implementation of the present application is not limited to any specific hardware circuits, software, or a combination of the two.

[0041] See also Figure 2 The embodiment of the present application provides a method for assessing the reversal of cognitive impairment, which is applicable to an electronic device, and the hardware structure of the electronic device can be as follows Figure 1 shown.

[0042] In the following method embodiments, for ease of description, the execution subject of each step of the method is taken as an electronic device as an example for illustration, but this does not constitute a specific limitation.

[0043] like Figure 2 As shown, the method may include the following steps:

[0044] Step 310, obtaining target information.

[0045] Among them, the target information includes variables of multiple different dimensions, each variable is used to indicate at least one of the target's lifestyle, eating habits, mental health, physical condition, age, and gender. The target refers to people who need to undergo cognitive impairment reversal assessment, such as MCI patients.

[0046] First of all, it should be noted that variables of different dimensions refer to information that describes the multi-faceted characteristics of the target. Variables of different dimensions can affect the development and reversal of mild cognitive impairment (MCI) from different angles. They can include demographic dimensions, lifestyle dimensions, psychological state dimensions, physiological dimensions, genetic dimensions, etc., which are not limited here.

[0047] For example, taking a patient as an example, the variables of different dimensions may be as follows:

[0048] Demographic dimensions: age 70, female, education level college.

[0049] Lifestyle dimension: Mediterranean diet score 8 / 10, 4 hours of exercise per week.

[0050] Psychological state dimension: depression score is low and anxiety score is high.

[0051] Physiological dimensions: Hippocampal volume is low and Tau protein concentration is normal.

[0052] Genetic dimension: APOEε4 gene positive.

[0053] Regarding the assessment of reversal of cognitive impairment, it may include assessing the possibility of reversing mild cognitive impairment, and may also include assessing the possibility of mild cognitive impairment progressing to Alzheimer's disease, which is not limited here.

[0054] By integrating variables from multiple dimensions such as diet, lifestyle, and mental health, a comprehensive assessment is provided, avoiding bias caused by a single factor.

[0055] Step 330, screening multiple variables in the target information to select at least one target variable from the target information that has a significant effect on reversal of cognitive impairment of the target.

[0056] First of all, it should be noted that not all variables in the target information have an impact on the reversal of cognitive impairment. Some variables are irrelevant to the reversal of cognitive impairment or have little impact. Therefore, multiple variables in the target information can be screened to select target variables that have a significant impact on the reversal of the target's cognitive impairment from the target information.

[0057] In one possible implementation, before screening multiple variables in the target information, the following steps may be included: cleaning and standardizing the target information to remove missing values, outliers and / or duplicate values ​​in the target information.

[0058] It is understandable that there may be missing values, outliers and / or duplicate values ​​in the target information, which may affect the results of cognitive impairment reversal assessment. Specifically, missing values ​​may lead to information bias, while outliers may introduce noise and lead to wrong conclusions. Duplicate values ​​not only take up storage space, but may also lead to misleading results. Based on this, denoising, correcting errors and formatting the target information can significantly improve the quality of the target information, and high-quality target information can better reflect the real situation.

[0059] Of course, standardization involves converting target information of different formats, units or ranges into a unified form so that target information from different sources can be effectively compared and errors caused by inconsistent formats can be avoided.

[0060] After cleaning and standardization, the target information can be screened. In one possible implementation, step 330 may include the following steps: using a regression method to perform cognitive impact analysis on each variable in the target information to obtain an impact coefficient corresponding to each variable; if the impact coefficient corresponding to the variable meets the set threshold, the variable is used as the target variable.

[0061] Among them, the influence coefficient is used to indicate the degree of influence of the variable on the reversal of cognitive impairment of the target.

[0062] First of all, it should be noted that the regression method can identify the relationship between variables and evaluation results, so as to clarify the specific influence of each variable in the target information on the reversal of the target's cognitive impairment.

[0063] In one possible implementation, the regression method may be LASSO regression, which can automatically filter out the target variables that have the most significant impact on MCI reversal from the target information, such as daily fruit intake, physical activity, psychological state, etc., and remove irrelevant or less influential variables. Specifically, LASSO regression can reduce the influence coefficients of some variables to 0, thereby excluding variables with an influence coefficient of 0, and then analyzing the non-zero influence coefficients. The larger the non-zero influence coefficient, the more significant the effect of the variable on the reversal of cognitive impairment.

[0064] The threshold is set to indicate that the variable has a significant effect on the reversal of cognitive impairment. When the influence coefficient is greater than the set threshold, it indicates that the variable has a significant effect on the reversal of cognitive impairment.

[0065] Through the above process, the regression method is used to automatically screen out the target variables that have a significant impact on the reversal of the target's cognitive impairment from various variables. This can reduce the dimension of the variables and reduce the complexity, thereby improving the accuracy of subsequent cognitive impairment reversal assessments.

[0066] Step 350, input the selected target variable into the reversal assessment model to assess the target's cognitive impairment and obtain an assessment result.

[0067] Among them, the evaluation results are used to indicate the possibility of reversal of the target's cognitive impairment. Reversal of cognitive impairment refers to the target's transformation from mild cognitive impairment to a normal cognitive state. The reversal evaluation model is a trained machine learning model that has the ability to evaluate the reversal of the target's cognitive impairment.

[0068] In one possible implementation, the evaluation result includes a reversal probability; the reversal probability is used to indicate the probability of reversing the cognitive impairment of the target, such as Figure 3 As shown, step 350 may include the following steps:

[0069] Step 351, analyzing the influence of different target variables on the cognitive impairment of the reversal target through the reversal assessment model to obtain corresponding influence indicators.

[0070] In one possible implementation, the impact indicator includes a risk ratio and / or a confidence interval.

[0071] Among them, the risk ratio is used to quantify the relative risk of reversal of cognitive impairment when a target variable changes. For example, a risk ratio of 1.5 means that the probability of reversal increases by 50% for every unit increase in a target variable. The confidence interval indicates the credible range of the impact indicator and reflects the stability of the risk ratio estimate. If the confidence interval does not contain 1, it means that the target variable has a significant impact on the probability of reversal.

[0072] Step 353, calculating the probability of reversing the cognitive impairment of the target based on each influencing index to obtain the reversal probability.

[0073] It should be noted that based on the impact indicators corresponding to each variable and combined with the individual specific target variable value, the prediction formula is used to obtain the target reversal probability. For example, by weighting all target variables and their risk ratios, a personalized reversal probability of the target can be generated. The calculated probability value is between 0 and 1. The higher the value, the greater the possibility of reversing cognitive impairment. Based on this, a threshold (such as 0.5) can be set to determine whether cognitive impairment is considered to be reversible.

[0074] Through the above process, by combining the impact indicators and the target variables corresponding to the individual goals, the personalized calculation of the probability of reversal of cognitive impairment of the target can be achieved, which is more targeted than the traditional group assessment. The risk ratio and confidence interval can not only quantify the impact of each variable on the reversal of cognitive impairment, but also significantly improve the interpretability and credibility of the assessment results. In addition, through the flexible selection of impact indicators (such as introducing more dimensional variables), the model can be continuously optimized and expanded to meet the diverse needs of cognitive health assessment.

[0075] It should be noted that the target's living habits, physical condition and other information are not static, but will change dynamically with time and other factors. Therefore, the reversal assessment model needs to have the ability to regularly update and re-evaluate the dynamically changing target information to ensure the accuracy and real-time nature of the assessment results.

[0076] In one possible implementation, step 370 may include the following steps: regularly obtaining updated target information of the target; re-screening the updated target information to obtain updated target variables; inputting the updated target variables into the reversal assessment model to re-evaluate the target's cognitive impairment and obtain updated assessment results.

[0077] Through regular monitoring or user input, the target's latest living habits and health data (such as diet records, exercise status, mental state, etc.) are collected. Of course, according to the evaluation needs, a regular acquisition cycle can be set, such as weekly, monthly or quarterly.

[0078] Analyze the updated target information, combine the new information features, and screen out the updated target variables. For example, the new target information may indicate that the significance of the impact of certain variables has changed, and the selection of target variables needs to be adjusted. Then, input the screened updated variables into the reversal assessment model, and re-evaluate through the reversal assessment model to confirm the latest reversal of cognitive impairment of the target.

[0079] Through the above process, the reversal assessment model has the ability to dynamically adjust and can update the assessment results in real time according to changes in patient data (such as improvement in lifestyle or deterioration in health status) to ensure the accuracy of the assessment results.

[0080] In combination with the above embodiments, by acquiring target information and integrating variables of different dimensions (such as demographics, lifestyle, psychological state, physiological condition, genetic information, etc.), a comprehensive analysis of the development and reversal of mild cognitive impairment (MCI) can be achieved, avoiding the limitations of single-dimensional evaluation. The screened target variables are input into the reversal evaluation model to provide a personalized cognitive impairment reversal evaluation, thereby improving the accuracy of the individual cognitive impairment reversal evaluation, and providing a scientific basis for formulating tailored intervention measures for each target.

[0081] See also Figure 4 In an exemplary embodiment, the training of the reversal assessment model may include the following steps:

[0082] Step 410, obtaining sample data;

[0083] The sample data includes multiple samples and sample variables of different dimensions.

[0084] In one possible implementation, the sample data comes from the multidimensional data samples of the CLHLS database, including variables in dimensions such as eating habits, lifestyle, mental health, and cardiovascular metabolism, such as daily fruit intake, frequency of housework, body mass index (BMI), blood pressure, heart rate, etc.

[0085] Understandably, the multidimensional nature of the sample data ensures comprehensiveness when training the model, helping to identify the combined effects of different variables on the reversal of cognitive impairment.

[0086] Step 430, train the machine learning model with sample data, and adjust the weights of sample variables of different dimensions in the machine learning model according to the cognitive change standard rules, so that the weights corresponding to each sample variable can reflect the different impacts on the cognitive impairment of the sample.

[0087] First of all, it should be noted that by using sample data to train the machine learning model, a reversal assessment model can be constructed by analyzing the relationship between the sample variables and the target output (the possibility of reversal of cognitive impairment). The reversal assessment model type can be a Cox regression model or a time series model, such as LSTM, which is not limited here.

[0088] Regarding the weights corresponding to the sample variables, we can determine which variables are most important for the assessment of reversal of cognitive impairment by analyzing the absolute size of the weight values. In addition, if there are interactions between different variables, corresponding weights can also be assigned to reflect the synergistic or antagonistic effects between the variables.

[0089] Regarding the cognitive change standard rule, the weights of sample variables of different dimensions in the machine learning model are adjusted. Specifically, it can be flexibly adjusted according to different cognitive change standards (such as changes in MMSE scores, MMSE: Mini-Mental State Examination, Simple Mental State Scale), and the weight coefficients of sample variables can be adjusted to improve their evaluation accuracy in different situations.

[0090] Step 450, use a cross-validation method to test the cognitive impairment reversal assessment ability of the machine learning model until the cognitive impairment reversal assessment ability of the machine learning model meets the set conditions, thereby obtaining a reversal assessment model that has completed training.

[0091] First of all, it should be noted that the cross-validation method can be k-fold cross-validation, leave-one-out cross-validation, stratified k-fold cross-validation, time series cross-validation and other methods, again without limitation.

[0092] The setting conditions are used to determine whether the machine learning model has completed training. The cognitive impairment reversal assessment ability can be an evaluation indicator of the machine learning model, which can include the following indicators: C-index (consistency index) and time-dependent ROC curve, loss function or objective function, etc., which are not limited here.

[0093] For example, if the evaluation indicator is C-index (consistency index), the higher the value, the stronger the model's ability to distinguish samples. Generally, a C-index exceeding 0.7 can be considered a good model performance; if the evaluation indicator is a time-dependent ROC curve, the time-dependent ROC is more suitable for data that changes dynamically over time, and the higher the AUC (area under the curve) value, the better the model performance. When C-index and AUC reach the set threshold in multiple verifications and the range of variation is small, it means that the performance indicators are stable, the machine learning model has completed training, and a reversal evaluation model is obtained.

[0094] Under the effect of the above-mentioned embodiment, the sample data is derived from the reliable CLHLS database, and the multidimensional variables cover all aspects of individual health status, providing comprehensive and real data support for model training. In addition, the combination of multidimensional sample variables enables the model to capture complex correlations and provide multi-angle cognitive impairment assessment. The reversal assessment model based on the time series model can reflect the influence of sample variables at different time nodes by dynamically adjusting the weights of sample variables, thereby ensuring sensitivity to short-term changes and long-term trends. In addition, the cross-validation method avoids overfitting or underfitting problems, dynamically adapts to the influence of each variable, thereby improving the accuracy of the evaluation and ensuring that the model can maintain a high evaluation performance on new data.

[0095] Figure 5This is a schematic diagram of a specific implementation of a cognitive impairment reversal assessment method in an application scenario. In this application scenario, multidimensional health data (diet, lifestyle, mental health, cardiovascular metabolic indicators, etc.) of MCI patients are collected to evaluate the effect of intervention measures (such as cognitive training, diet adjustment or exercise plan) on cognitive impairment reversal.

[0096] Through step 801, health data of MCI patients are collected.

[0097] Through step 803, the LASSO regression method is used to select variables in the health data, remove irrelevant or less influential variables, and obtain the target variable.

[0098] Among them, intervention measures (such as cognitive training, diet adjustment or exercise plan) can be explicitly included in the variable set in step 803. After LASSO regression screening, significantly related intervention measure variables are retained to ensure that they can be analyzed in the Cox proportional hazard model.

[0099] Through step 805, the effect of each target variable on MCI reversal is estimated through the Cox proportional hazards regression model, and the hazard ratio and 95% confidence interval are generated.

[0100] Through step 807, the Cox proportional hazards regression model calculates the reversal probability of the MCI patient reversing to a normal cognitive state in real time according to different target variables and their corresponding hazard ratios.

[0101] Through step 809, a personalized assessment report is generated for the MCI patient based on the reversal probability.

[0102] Specifically, the difference in reversal probability with and without intervention can be compared to generate an effectiveness evaluation report of specific intervention measures. For example, after implementing cognitive training, the probability of reversal to a normal cognitive state increases from 20% to 50%.

[0103] In this application scenario, it is possible to quantitatively evaluate the specific effect of each intervention measure on the reversal of MCI, provide personalized health intervention recommendations for MCI patients, optimize the reversal plan for cognitive impairment, and guide medical institutions to develop more effective group intervention plans to improve the MCI reversal rate.

[0104] In addition, this method can not only evaluate the possibility of reversal of cognitive impairment, but can also be extended to evaluate other similar health status transitions, such as diabetes remission assessment, cardiovascular disease risk assessment, risk assessment of MCI developing into Alzheimer's disease, etc. It has a wide range of application scenarios and good expansion capabilities in variable dimensions.

[0105] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0106] The following is an embodiment of the device of the present application, which can be used to perform the cognitive impairment reversal assessment method involved in the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the method embodiment of the cognitive impairment reversal assessment method involved in the present application.

[0107] See also Figure 6 In an embodiment of the present application, a cognitive impairment reversal assessment device 900 is provided, including but not limited to: an information acquisition module 910, a variable screening module 930, and a cognitive assessment module 950.

[0108] The information acquisition module 910 is used to acquire target information; the target information includes multiple variables of different dimensions, each variable is used to indicate at least one of the target's lifestyle, eating habits, mental health, physical condition, age, and gender;

[0109] A variable screening module 930 is used to screen multiple variables in the target information to select at least one target variable that has a significant effect on the reversal of cognitive impairment of the target from the target information;

[0110] The cognitive assessment module 950 is used to input the selected target variable into the reversal assessment model to evaluate the target's cognitive impairment and obtain an assessment result; the assessment result is used to indicate the possibility of reversal of the target's cognitive impairment; the reversal assessment model is a machine learning model that has been trained and has the ability to evaluate the target's cognitive impairment.

[0111] It should be noted that the cognitive impairment reversal assessment device provided in the above embodiment only uses the division of the above-mentioned functional modules as an example when performing cognitive impairment reversal assessment. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the cognitive impairment reversal assessment device will be divided into different functional modules to complete all or part of the functions described above.

[0112] In addition, the cognitive impairment reversal assessment device and the cognitive impairment reversal assessment method provided in the above embodiments belong to the same concept, and the specific manner in which each module performs operations has been described in detail in the method embodiments and will not be repeated here.

[0113] See also Figure 7 In an embodiment of the present application, an electronic device 4000 is provided, and the electronic device 4000 may include: a desktop computer, a laptop computer, a server, etc.

[0114] exist Figure 7 In the embodiment, the electronic device 4000 includes at least one processor 4001 and at least one memory 4003.

[0115] The data interaction between the processor 4001 and the memory 4003 can be realized through at least one communication bus 4002. The communication bus 4002 may include a path for transmitting data between the processor 4001 and the memory 4003. The communication bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0116] Optionally, the electronic device 4000 may further include a transceiver 4004, which may be used for data interaction between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present application.

[0117] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. Processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0118] The memory 4003 can be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store a computer program in the form of instructions or data structures and can be accessed by the electronic device 400, but is not limited to this.

[0119] The memory 4003 stores a computer program, and the processor 4001 can read the computer program stored in the memory 4003 through the communication bus 4002 .

[0120] The computer program is executed by one or more processors 4001 to implement the cognitive impairment reversal assessment method in the above-mentioned embodiments.

[0121] In addition, an embodiment of the present application provides a storage medium on which a computer program is stored. The computer program is executed by one or more processors to implement the cognitive impairment reversal assessment method as described above.

[0122] A computer program product is provided in an embodiment of the present application, including a computer program, which is executed by one or more processors to implement the cognitive impairment reversal assessment method as described above.

[0123] Compared with related technologies, this solution can achieve a comprehensive analysis of the development and reversal of mild cognitive impairment (MCI) by obtaining target information and integrating variables of different dimensions (such as demographics, lifestyle, psychological state, physiological condition, genetic information, etc.), avoiding the limitations of single-dimensional evaluation. The screened target variables are input into the reversal evaluation model to provide personalized cognitive impairment reversal evaluation, thereby improving the accuracy of cognitive impairment reversal evaluation for individuals, and providing a scientific basis for formulating tailored intervention measures for each target.

[0124] The above description is only a partial implementation method of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for assessing reversal of cognitive impairment, characterized in that: include: Get target information; The target information includes multiple variables of different dimensions, each of which is used to indicate at least one of the target's lifestyle, eating habits, mental health, physical condition, age, and gender; Screening a plurality of variables in the target information to select at least one target variable from the target information that has a significant effect on reversal of cognitive impairment of the target; The selected target variable is input into the reversal assessment model to assess the cognitive impairment of the target and obtain an assessment result; the assessment result is used to indicate the possibility of reversal of the cognitive impairment of the target; the reversal assessment model is a machine learning model that has been trained and has the ability to assess the reversal of the cognitive impairment of the target.

2. The method according to claim 1, characterized in that The screening of the multiple variables in the target information to select at least one target variable having a significant effect on the reversal of cognitive impairment of the target from the target information includes: Using a regression method to perform cognitive impact analysis on each of the variables in the target information to obtain an impact coefficient corresponding to each of the variables; the impact coefficient is used to indicate the degree of influence of the variable on the reversal of cognitive impairment of the target; If the influence coefficient corresponding to the variable meets the set threshold, the variable is used as the target variable.

3. The method according to claim 1, characterized in that The evaluation result includes a reversal probability; the reversal probability is used to indicate the probability of reversing the cognitive impairment of the target; The step of inputting the selected target variable into the reversal assessment model to assess the target's cognitive impairment and obtain an assessment result includes: Analyzing the effects of different target variables on reversing the target's cognitive impairment through the reversal assessment model to obtain corresponding impact indicators; The probability of reversing the cognitive impairment of the target is calculated based on each of the influencing indicators to obtain the reversal probability.

4. The method according to claim 3, characterized in that The effect indicators include risk ratios and / or confidence intervals.

5. The method according to claim 1, characterized in that The training of the reversal assessment model includes: Acquire sample data; the sample data includes multiple samples and sample variables of different dimensions; Training the machine learning model with the sample data, and adjusting the weights of sample variables of different dimensions in the machine learning model according to the cognitive change standard rule, so that the weights corresponding to the sample variables can reflect the different effects of cognitive impairment on the sample; The cognitive impairment reversal assessment ability of the machine learning model is tested using a cross-validation method until the cognitive impairment reversal assessment ability of the machine learning model meets the set conditions, thereby obtaining a reversal assessment model that has completed training.

6. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: Regularly obtain updated target information of the target; Re-screening the updated target information to obtain an updated target variable; The updated target variable is input into the reversal assessment model to reassess the cognitive impairment of the target and obtain an updated assessment result.

7. The method according to any one of claims 1 to 5, characterized in that: Before screening the multiple variables in the target information, the method further includes: The target information is cleaned and standardized to remove missing values, outliers and / or duplicate values ​​in the target information.

8. A cognitive impairment reversal assessment device, characterized in that: include: An information acquisition module, used to acquire target information; The target information includes multiple variables of different dimensions, each of which is used to indicate at least one of the target's lifestyle, eating habits, mental health, physical condition, age, and gender; A variable screening module, used for screening a plurality of variables in the target information, so as to select at least one target variable having a significant effect on the reversal of cognitive impairment of the target from the target information; A cognitive assessment module is used to input the selected target variable into a reversal assessment model to assess the cognitive impairment of the target and obtain an assessment result; the assessment result is used to indicate the possibility of reversal of the cognitive impairment of the target; the reversal assessment model is a machine learning model that has been trained and has the ability to assess the reversal of the cognitive impairment of the target.

9. An electronic device comprising at least one processor and at least one memory, wherein: The memory stores a computer program, wherein when the computer program is executed by the processor, the method for assessing the reversal of cognitive impairment according to any one of claims 1 to 7 is implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by one or more processors, the method for assessing reversal of cognitive impairment according to any one of claims 1 to 7 is implemented.

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