Stroke prediction method, system and device

Abstract

The present application relates to the technical field of stroke prediction, and specifically to a stroke prediction method, system, and device. The stroke prediction method provided in the present application, in addition to identifying the patient's facial information, also obtains the patient's grip strength difference information and historical medical history information. After interactive processing of the grip strength difference information and the patient's historical medical history information, the information is combined with facial feature information as the input of the prediction model to predict stroke. In this way, the patient's facial information and grip strength difference information are combined, and the user's historical record data is also compared. The predicted risk results can reflect changes in the patient's condition, greatly improving the success rate of stroke prediction.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of stroke prediction, and specifically to a stroke prediction method, system, and device. Background Art

[0002] Stroke is a serious neurological disease with high morbidity and disability, placing a heavy burden on patients and society. Recent progress has been made in the research of stroke prediction models. Existing studies have primarily focused on the analysis of biomarkers, clinical features, and imaging data, while relatively little attention has been paid to visual features.

[0003] Visual features may have important indicative significance in the clinical presentation of stroke patients. Certain visual symptoms may be associated with specific types of stroke, especially in patients with migraine. For example, brief visual aura is considered to be a characteristic of young and middle-aged stroke patients.

[0004] The inventors of the present application found in their research that the existing system for predicting stroke through visual features relies on single facial recognition data without reference to other features of the human body, which results in poor prediction accuracy of the system. Summary of the Invention

[0005] In view of the above problems, the embodiments of the present application provide a stroke prediction method, system, device and computer-readable storage medium to solve the above technical problems existing in the prior art.

[0006] One aspect of the embodiments of the present application provides a method for predicting stroke, comprising:

[0007] Obtain facial recognition information, grip strength difference information, and historical medical records;

[0008] Generate facial feature information using a pre-trained visual feature prediction model based on the facial recognition information;

[0009] performing standardization processing on the facial feature information, grip strength difference information, and historical medical record information;

[0010] Interactively processing the standardized grip strength difference information and the standardized historical medical record information to generate interactive feature information;

[0011] constructing a multivariate regression model based on the facial feature information and the interactive feature information;

[0012] Estimating the multiple regression model according to the least squares method to generate estimated model parameters;

[0013] Adjusting the estimation model parameters according to a preset facial feature weight factor and a preset interaction feature weight factor, wherein the preset facial feature weight factor is greater than the preset interaction feature weight factor;

[0014] generating a stroke risk assessment value through the multivariate regression model according to the adjusted estimated model parameters;

[0015] Stroke prediction is performed based on the stroke risk assessment value.

[0016] Preferably, in some embodiments, generating facial feature information based on the facial recognition information using a pre-trained visual feature prediction model includes:

[0017] Extracting the facial recognition information to obtain left and right eye gaze images, a facial symmetry image, and a smile symmetry image;

[0018] performing normalization processing on the left and right eye gaze images, the facial symmetry image, and the smile symmetry image;

[0019] Performing convolution processing on the normalized image to generate a feature map, and performing a pooling operation on the feature map;

[0020] Processing the feature map after convolution and pooling operations through an activation function to generate a probability value corresponding to the feature map;

[0021] Facial feature information is generated according to the probability value.

[0022] Preferably, in some embodiments, the grip strength difference information includes a grip strength difference change rate.

[0023] Preferably, in some embodiments, the interactive processing of the standardized grip strength difference information and the standardized historical medical record information to generate interactive feature information includes:

[0024] generating a grip force difference matrix according to the standardized grip force difference information;

[0025] generating a historical medical record matrix according to the standardized historical medical record information;

[0026] multiplying the grip strength difference matrix by the historical medical record matrix to generate interactive feature information;

[0027] The constructing of a multivariate regression model according to the facial feature information and the interactive feature information includes:

[0028] The multiple regression model is:

[0029] ;

[0030] Among them, Y is the stroke risk assessment value, is the facial feature information, … is the interactive feature information, is the error term, is the intercept term, are the initial model parameters.

[0031] Preferably, in some embodiments, estimating the multivariate regression model according to the least squares method to generate estimated model parameters includes:

[0032] The estimated model parameters are determined by minimizing the sum of squared errors using the following formula:

[0033] ;

[0034] Where m is the number of samples.

[0035] Preferably, in some embodiments, adjusting the estimation model parameters according to a preset weight factor includes:

[0036] Constructing a weighted loss function according to the facial feature weight factor and the interactive feature weight factor;

[0037] Determining a weighted error corresponding to the facial feature information and the interactive feature information according to the weighted loss function;

[0038] The weighted loss function is optimized by a gradient descent algorithm according to the weighted error, and the estimation model parameters are adjusted.

[0039] Another aspect of the embodiments of the present application further provides a stroke management system, including a facial recognition unit, a grip strength acquisition unit, a questionnaire unit, and a data prediction unit;

[0040] The facial recognition unit is used to obtain facial recognition information and send the facial recognition information to the data prediction unit;

[0041] The grip strength acquisition unit is used to acquire grip strength difference information and send the grip strength difference information to the data prediction unit;

[0042] The questionnaire unit is used to obtain historical medical record information and send the historical medical record information to the data prediction unit;

[0043] The data prediction unit is configured to execute the stroke prediction method described in the above embodiment based on the facial recognition information, the grip strength difference information, and the historical medical record information.

[0044] Preferably, some embodiments further include an automatic registration unit;

[0045] The automatic registration unit is used to automatically generate registration information according to the stroke prediction result, wherein the registration information includes the stroke prediction result.

[0046] According to a third aspect of the embodiments of the present application, a stroke prediction device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;

[0047] The memory is used to store at least a program, and the program enables the processor to execute the operations of the stroke prediction method described in the above embodiment.

[0048] According to a fourth aspect of the embodiments of the present application, a computer-readable storage medium is provided, wherein at least one program is stored in the storage medium. When the program is executed on a stroke prediction device, the stroke prediction device performs the operations of the stroke prediction method described in the above embodiments.

[0049] In summary, in addition to identifying the patient's facial information, the embodiments of the present application also obtain the patient's grip strength difference information and historical medical history information. After interactive processing of the grip strength difference information and the patient's historical medical history information, they are combined with facial feature information and used as input to the prediction model to predict stroke. In this way, the patient's facial information and grip strength difference information are combined, and the user's historical record data is also compared. The predicted risk results can reflect changes in the patient's condition, greatly improving the success rate of stroke prediction.

[0050] The above description is only an overview of the technical solutions of the embodiments of the present application. In order to more clearly understand the technical means of the embodiments of the present application, they can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiments of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings are only used to illustrate the embodiments and are not to be considered as limiting the present application. In addition, the same reference symbols are used to represent the same components throughout the drawings. In the drawings:

[0052] Figure 1 A schematic diagram of the structure of a stroke management system provided in an embodiment of the present application;

[0053] Figure 2 A schematic flow chart of a stroke prediction method provided in an embodiment of the present application;

[0054] Figure 3A schematic diagram of a loss function optimization process provided in an embodiment of the present application;

[0055] Figure 4 A schematic diagram of the structure of a stroke prediction device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0056] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0057] With the development of deep learning technology, it has become possible to use visual data to improve the accuracy of stroke prediction. The application of facial features in stroke prediction is an emerging research field that uses artificial intelligence and machine learning technologies to analyze facial images to identify and predict patients' risk of stroke.

[0058] Clinically, before a stroke occurs, the body will experience many symptoms, such as numbness in the legs, weakness in the hands and feet, etc. At the same time, the patient's own diseases will also increase the risk of causing a stroke. The inventors of this application found in their research that the current stroke prediction system relies on single facial recognition data and does not refer to other medical history features of the patient, resulting in poor system prediction accuracy. The existing prediction system does not adequately process feature engineering and fails to identify and extract high-quality features that are helpful for prediction, which will cause misdiagnosis and miss the best time to treat stroke.

[0059] In view of this, the embodiment of the present application proposes a stroke prediction method and system. Through the stroke prediction method and system proposed in the embodiment of the present application, in addition to identifying the patient's facial information, the patient's grip strength difference information and historical medical history information are also obtained. The grip strength difference information and the patient's historical medical history information are interactively processed to generate interactive feature information. The interactive feature information is combined with the facial feature information and used as the input of the prediction model to predict stroke. In this way, the patient's facial information and grip strength difference information are combined, and the user's historical medical history data is also referenced. The predicted risk results obtained in this way can reflect changes in the patient's condition and greatly improve the success rate of stroke prediction. At the same time, the stroke prediction system provided by the embodiment of the present application is also connected to the hospital's medical treatment process, automatically provides registration services for users based on the prediction results, and includes the stroke prediction situation in the registration information, so that doctors can quickly and accurately diagnose the patient's condition.

[0060] like Figure 1 , which is a schematic diagram of the structure of the stroke prediction system proposed in the embodiment of the present application. The stroke prediction method proposed in the embodiment of the present application can be applied to the stroke prediction system.

[0061] The stroke prediction system 100 includes a face recognition unit 101 , a grip strength acquisition unit 102 , a questionnaire unit 103 and a data prediction unit 104 .

[0062] The facial recognition unit 101 is typically a camera used to obtain facial recognition information of the patient. When the patient visits the doctor, the stroke prediction system can remind the patient to stand in a specific position and use the camera to collect information about the patient's face, including taking static pictures and dynamic videos. The facial recognition unit 101 can also identify facial features that require key acquisition, such as eye features, mouth features, and smile features. If the above key information is not acquired, the facial recognition unit 102 can remind the user to take appropriate actions to acquire it again. After acquiring the above facial recognition information, the facial recognition unit 102 sends the facial recognition information to the data prediction unit.

[0063] The grip strength acquisition unit 102 is used to obtain grip strength difference information between the patient's left and right hands. According to clinical experience, when a patient suffers a stroke, their left and right hands will experience muscle spasms or atrophy to a certain extent, resulting in a significant difference in grip strength between the left and right hands. Obtaining the grip strength difference between the patient's left and right hands is of great significance for determining the risk of stroke. In an embodiment of the present application, to obtain the grip strength difference between the patient's left and right hands, the grip strength acquisition unit 102 can provide a handgrip dynamometer, which is connected to the data prediction unit 104 via an interface. When the patient clenches the handgrip dynamometer, the data prediction unit 104 obtains the grip strength information of the left and right hands and calculates the difference between them to obtain the difference between the two.

[0064] Furthermore, to more accurately assess the impact of left-hand and right-hand grip strength differences on stroke risk, multiple left-hand and right-hand grip strength difference values ​​can be periodically acquired, and the rate of change of the left-hand and right-hand grip strength difference values ​​determined based on these values. In practice, some patients do experience significant differences in left-hand and right-hand grip strength, and the grip strength difference alone may not accurately reflect a patient's stroke risk. Clinical observations have shown that the durability of hand grip strength is affected in early-stage stroke patients. Therefore, obtaining the rate of change of the grip strength difference can more accurately reflect stroke risk.

[0065] The questionnaire unit 103 is used to obtain the patient's historical medical information. The stroke management system can investigate the patient's historical medical information through voice inquiry or interface display. The historical medical information includes whether the patient suffers from hypertension, diabetes, hyperlipidemia, etc., whether there is a family history of cardiovascular and cerebrovascular diseases, living habits, etc.

[0066] The data prediction unit 104 is configured to predict the probability of stroke by executing the stroke prediction method proposed in the embodiment of the present application based on the facial recognition information acquired by the facial recognition unit 101, the grip strength difference information acquired by the grip strength acquisition unit 102, and the historical medical history information acquired by the questionnaire unit 103. The data prediction unit 104 may also present the stroke prediction result to the patient via a display screen.

[0067] The data prediction unit 104 generates facial feature information through a pre-trained visual feature prediction model based on the facial recognition information; standardizes the facial feature information, grip strength difference information and historical medical record information; interactively processes the standardized grip strength difference information and the standardized historical medical record information to generate interactive feature information; constructs a multivariate regression model based on the facial feature information and the interactive feature information; estimates the multivariate regression model based on the least squares method to generate estimated model parameters; adjusts the estimated model parameters based on preset facial feature weight factors and preset interactive feature weight factors, wherein the preset facial feature weight factor is greater than the preset interactive feature weight factor; generates a stroke risk assessment value through the multivariate regression model based on the adjusted estimated model parameters; and predicts a stroke based on the stroke risk assessment value.

[0068] Furthermore, the stroke management system also includes an automatic registration unit 105, which is used to automatically generate registration information based on the stroke prediction result, wherein the registration information includes the stroke prediction result. The automatic registration unit 105 automatically connects to the hospital treatment platform and can provide patients with a one-stop automatic registration service, so that stroke patients can receive rapid treatment. By including the stroke prediction result in the registration information, the doctor can directly obtain the patient's relevant information after seeing the registration information, which helps the doctor to provide accurate and timely treatment to the patient.

[0069] This embodiment of the application combines facial recognition information, the difference in grip strength between the left and right hands, and historical medical records to achieve a multi-dimensional assessment of stroke risk. This comprehensive approach can more comprehensively reflect an individual's physiological state, thereby improving the accuracy and reliability of predictions. Compared with traditional single-indicator prediction methods, this multi-feature fusion approach will provide more accurate clinical decision support. At the same time, the stroke management system enables one-click appointment registration, saving medical and social resource costs while building a solid barrier to potential stroke risks.

[0070] In the above embodiment, the data prediction unit 104 is used to execute the stroke prediction method proposed in the embodiment of the present application, specifically, Figure 2As shown, the stroke prediction method includes:

[0071] Step S10: Obtaining facial recognition information, grip strength difference information, and historical medical record information;

[0072] As described in the embodiment of the above-mentioned stroke management system, the facial recognition information can be obtained through the facial recognition unit of the stroke management system. The facial recognition information is obtained by taking photos or videos of the patient with a camera. The facial recognition information includes eye features, mouth features, and smile features, etc. The eye features can be used to determine whether the patient has symptoms of strabismus, the mouth features can be used to determine whether the patient has symptoms of crooked mouth, and the smile features can be used to determine whether the patient has symptoms of facial paralysis, etc. The above-mentioned features are typical symptoms of stroke patients. Obtaining facial recognition information by photographing the patient's face helps to determine whether the patient is at risk of stroke.

[0073] The grip strength difference information can be acquired by the grip strength acquisition unit in the above-mentioned embodiment. In the embodiment of the present application, in addition to acquiring the patient's facial information, grip strength difference information of the patient's left and right hands is further acquired. Combining this grip strength difference information with facial recognition information can more accurately determine whether the patient is at risk of stroke. In addition to including the difference in grip strength between the left and right hands, the grip strength difference information can also include the rate of change of the grip strength difference between the left and right hands. The rate of change of the grip strength difference represents the changes in the patient's muscles and can more accurately reflect whether the patient is at risk of stroke.

[0074] In an embodiment of the present application, in addition to the patient's facial information and grip strength difference information, the patient's historical medical history information is further obtained. The historical medical history information is mainly used to understand the patient's basic information and past medical history, including basic information such as age, gender, height, weight, and whether there is hypertension, diabetes, hyperlipidemia, cardiovascular and cerebrovascular family history, etc. By obtaining historical medical history information, a more comprehensive understanding of the patient's condition can be obtained, so as to more accurately judge whether the patient has the risk of stroke.

[0075] Step S20: Generate facial feature information using a pre-trained visual feature prediction model based on the facial recognition information;

[0076] In the stroke management system proposed in the above embodiment, a trained visual feature prediction model is pre-stored. The visual feature prediction model extracts and recognizes facial recognition information through a deep learning algorithm. For example, the acquired facial picture or video can be processed through the openCV function, including recognizing left and right eye gaze, facial symmetry, smile symmetry, mouth symmetry, etc. After the photo or video format is recognized into a picture format, the left and right eye gaze image, facial symmetry image, smile symmetry image, and mouth symmetry image are normalized, for example: the pixel value of the input data is rescaled from the range of [0, 255] to [0, 1].

[0077] The normalized image is convolved to generate a feature map, and the feature map is pooled. In this embodiment of the present application, the deep learning algorithm for processing facial recognition information includes a seven-layer CNN network consisting of two convolutional layers (with the activation function being rule), two pooling layers, a flatten layer, and two fully connected layers (with the activation functions being rule and sigmoid, respectively).

[0078] The convolutional layer is responsible for extracting features from the input data. By defining a set of convolutional filters, it performs convolution operations on the input data, generating a series of feature maps that capture local features and spatial structure.

[0079] The pooling layer is used for dimensionality reduction and sampling, reducing model parameters and computational complexity. Common pooling operations include max pooling and average pooling, which retain important information and reduce the amount of data by taking the maximum or average value within a specific area.

[0080] The Flatten layer is used to convert a multidimensional array, for example, a two-dimensional image matrix or a three-dimensional convolutional layer output, into a one-dimensional array so that subsequent layers can process it.

[0081] Fully connected layers are used to combine and classify features extracted by the convolutional and pooling layers. Each neuron in a fully connected layer is connected to all neurons in the previous layer, enabling high-level feature learning and classification of the input data.

[0082] The feature map after convolution and pooling operations is processed by an activation function to generate a probability value corresponding to the feature map, wherein the activation function is linear in the positive interval and is always zero in the negative interval, which helps to solve the gradient vanishing problem and accelerate the training process.

[0083] Through the above process, facial feature information is generated according to the probability value. Assuming that the AUC of the visual feature model is 92.538, the prediction results greater than 0.92 can be converted to 1, and the rest of the results can be converted to 0.

[0084] In this step, the pre-trained visual feature prediction model is used to first perform deep learning on the acquired facial recognition information and extract facial feature information, so as to combine it with the acquired grip strength difference information and historical medical record information to predict stroke outcomes.

[0085] Step S30: standardizing the facial feature information, grip strength difference information, and historical medical record information;

[0086] Combined with the questionnaire survey data, the historical medical record information obtained is standardized. For example, if the patient has a family history of hypertension, diabetes, hyperlipidemia, or cardiovascular and cerebrovascular diseases in the questionnaire survey, it is converted to 1, and if not, it is converted to 0. For the collected grip strength difference information, it can be judged according to the pre-set threshold. For example, if the pre-set threshold is 15%, if the grip strength difference is >15%, it is converted to 1, and if the grip strength difference is less than or equal to the rest, it is converted to 0.

[0087] Step S40: interactively processing the standardized grip strength difference information and the standardized historical medical record information to generate interactive feature information;

[0088] Creating interactive features is an effective way to improve the predictive ability of the model, especially in the multivariate linear regression model. Interactive features can help the model capture the complex relationship between different features. In the embodiment of the present application, although the grip strength difference information can reflect the risk of stroke in patients to a certain extent, in real life, there are indeed some people whose grip strength between their left and right hands is quite different. If these people are identified as high-risk stroke patients, it will obviously lead to erroneous results. In order to solve this problem, the embodiment of the present application interactively processes the grip strength difference information and historical medical record information, and interactively processes these two pieces of information so that they can more accurately reflect the relationship between the features.

[0089] A grip force difference matrix G is generated based on the standardized grip force difference information. The grip force difference matrix includes multiple grip force difference information. A historical medical history matrix is ​​generated based on the standardized historical medical history information. The historical medical history matrix may include age (A), gender (S), blood pressure (B), diabetes (D), etc. An interaction feature matrix is ​​generated by multiplying the grip force difference matrix with the historical medical history matrix. For example, the interaction feature matrices G*A*B, G*A*D, G*S*B, G*S*D, etc. can be generated by multiplying the aforementioned multiple features.

[0090] The interactive feature matrix combines grip strength difference information and historical medical record information, and can capture the complex relationships between different features, such as the relationship between grip strength difference and age, the relationship between age and blood pressure, the relationship between age and diabetes, etc.

[0091] Step S50: constructing a multivariate regression model based on the facial feature information and the interactive feature information;

[0092] A multiple regression model is an analytical technique in statistics used to study the relationship between a dependent variable (often called the response variable or outcome variable) and multiple independent variables (also called explanatory variables or predictor variables). In multiple regression analysis, a linear equation is constructed that predicts the value of the dependent variable based on the values ​​of multiple independent variables.

[0093] In the embodiment of the present application, we use the stroke risk assessment value as the dependent variable, the facial feature information and the interaction feature information as the dependent variable, and the basic form of the multiple regression model is:

[0094] ; (Formula 1)

[0095] Among them, Y is the stroke risk assessment value, is the facial feature information, … is the interactive feature information; is the error term, which represents the random variation that cannot be explained by the model; is the intercept term, is the initial model parameter, which represents the expected change in the dependent variable when the independent variable changes by one unit.

[0096] Step S60: Estimate the multivariate regression model according to the least squares method to generate estimated model parameters;

[0097] In the multivariate regression model, the least squares method is used for parameter estimation in the embodiments of the present application. This method finds the best coefficient estimate by minimizing the sum of squares of the residuals, where the residual refers to the difference between the actual observed value and the model predicted value.

[0098] Estimated model parameters are determined by minimizing the sum of squared errors:

[0099] ;

[0100] Among them, m is the number of samples. When the sum of squared errors is minimized, the estimated model parameters can be determined. The value of .

[0101] Step S70: adjusting the estimation model parameters according to a preset facial feature weight factor and a preset interaction feature weight factor, wherein the preset facial feature weight factor is greater than the preset interaction feature weight factor;

[0102] In practical applications, facial features often better reflect a patient's stroke risk. Interactive features like grip strength difference and historical medical history can be combined with facial features to improve and supplement them. Clinically, facial features are more important than grip strength difference and historical medical history.

[0103] Therefore, in order to more accurately reflect the relationship between facial feature information and interactive feature information, in the embodiment of the present application, the weights of each model parameter in the least squares method are adjusted to have different weight values. Feature weighting is a technique to increase the influence of specific features in the model. In the context of the least squares method, this can be achieved by modifying the loss function so that facial features are given higher weights during the optimization process. Figure 3 As shown, specifically including:

[0104] Step S71: constructing a weighted loss function according to the facial feature weight factor and the interactive feature weight factor;

[0105] When constructing the weighted loss function, we first define different weight factors for facial feature information and interactive feature information. Facial feature information corresponds to facial feature weight factors. , the interactive feature information corresponds to the interactive feature weight factor , since the weight of facial feature information is greater than the weight of interactive feature information, .

[0106] In the loss function, different weight factors are applied to the error terms of facial feature information and interaction feature information. The weighted loss function can be expressed as:

[0107] in, is the facial feature weight factor, is the interaction feature weight factor;

[0108] is the actual value of the i-th sample;

[0109]

[0110]

[0111]

[0112]

[0113] Step S72: determining a weighted error corresponding to the facial feature information and the interaction feature according to the weighted loss function;

[0114] For each sample, the weighted error of facial feature information and interaction feature information is calculated:

[0115] ;

[0116] ;

[0117] in, is the weighted error of facial feature information, is the weighted error of the interactive feature information.

[0118] Multiply each weighted error by the corresponding weight factor and calculate the squared weighted error, which are: and .

[0119] The squared weighted errors of all samples are summed to get the total weighted loss:

[0120] .

[0121] Step S73: Optimizing the weighted loss function by a gradient descent algorithm according to the weighted error, and adjusting the estimation model parameters.

[0122] The gradient descent algorithm is a commonly used numerical optimization method. In the embodiment of the present application, the weighted loss function is optimized by the gradient descent algorithm to adjust the estimated model parameters.

[0123] In each iteration, the model parameters are updated according to the gradient of the loss function. In each step of gradient descent, the facial features and interaction features are updated while taking into account the weight factors:

[0124] ;

[0125] in, is the learning rate, are the estimated model parameters to be updated.

[0126] Through the above steps, the embodiment of the present application ensures that facial feature information has a higher weight, thereby improving the accuracy of the multivariate regression model in predicting stroke risk.

[0127] Step S80: generating a stroke risk assessment value through the multivariate regression model according to the adjusted estimated model parameters;

[0128] The final stroke risk assessment value was generated by the multiple regression model of formula 1 using the adjusted estimated model parameters.

[0129] Step S90: performing stroke prediction based on the stroke risk assessment value.

[0130] In summary, the stroke prediction method proposed in the embodiment of the present application, in addition to identifying the patient's facial information, also obtains the patient's grip strength difference information and historical medical information, and after interactive processing of the grip strength difference information and the patient's historical medical information, combines them with facial feature information and uses them as input to the prediction model to predict stroke. In this way, the patient's facial information and grip strength difference information are combined, and the user's historical record data is also compared. The predicted risk results can reflect changes in the patient's condition, greatly improving the success rate of stroke prediction.

[0131] According to another aspect of the present application, a stroke prediction device is provided. Figure 4 , Figure 4 A schematic structural diagram of a stroke prediction device according to an embodiment of the present application is shown. The specific embodiment of the present application does not limit the specific implementation of the stroke prediction device.

[0132] like Figure 4 As shown, the stroke prediction device may include: a processor 402 , a memory 406 , a communication interface 404 and a communication bus 408 .

[0133] The processor 402, memory 406 and communication interface 404 communicate with each other via a communication bus 408. The memory 406 is used to store at least one program 410, which enables the processor 402 to execute the relevant steps in the above-mentioned stroke prediction method embodiment.

[0134] Specifically, the program 410 may include program code including computer-executable instructions.

[0135] Processor 402 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the stroke prediction device may be processors of the same type, such as one or more CPUs, or processors of different types, such as one or more CPUs and one or more ASICs.

[0136] The memory 406 is used to store the program 410. The memory 406 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage.

[0137] The program 410 can be specifically called by the processor 402 to enable the stroke prediction device to execute any operation method provided in the above-mentioned stroke prediction method embodiment, and the details will not be repeated here.

[0138] An embodiment of the present application further provides a computer-readable storage medium, in which executable instructions are stored. When the executable instructions are executed on a stroke prediction device, the stroke prediction device executes the stroke prediction method provided in any of the above embodiments.

[0139] The embodiment of the present application also provides a stroke prediction program, which is used to execute the stroke prediction method provided in the above embodiment.

[0140] The algorithm or demonstration provided here are not inherently relevant to any particular computer, virtual system or other equipment. Various general purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing this type of system. In addition, the present application embodiment is not directed to any specific programming language yet. It should be understood that various programming languages ​​can be utilized to realize the content of the present application described here, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the present application.

[0141] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0142] Similarly, it should be understood that in order to streamline the present application and assist in understanding one or more of the various aspects of the invention, in the above description of the exemplary embodiments of the present application, the various features of the embodiments of the present application are sometimes grouped together into a single embodiment, figure, or description thereof.

[0143] Those skilled in the art will appreciate that the modules in the devices of the embodiments can be adaptively modified and installed in one or more devices different from the embodiments. The modules, units, or components in the embodiments can be combined into a single module, unit, or component, or they can be divided into multiple sub-modules, sub-units, or sub-components. All features disclosed in this specification (including the accompanying abstract and drawings), and all processes or units of any method or device disclosed therein, can be combined in any combination, unless at least some of such features and / or processes or units are mutually exclusive. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying abstract and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0144] It should be noted that the above embodiments are illustrative rather than limiting of the present invention, and that those skilled in the art may design alternative embodiments without departing from the scope of the present invention. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order in which they are to be performed.

Claims

1. A method for predicting stroke, characterized in that: include: Acquiring facial recognition information, grip strength difference information, and historical medical record information, wherein the grip strength difference information includes a grip strength difference change rate; Extracting the facial recognition information to obtain left and right eye gaze images, a facial symmetry image, and a smile symmetry image; performing normalization processing on the left and right eye gaze images, the facial symmetry image, and the smile symmetry image; Performing convolution processing on the normalized image to generate a feature map, and performing a pooling operation on the feature map; Processing the feature map after convolution and pooling operations through an activation function to generate a probability value corresponding to the feature map; generating facial feature information according to the probability value; performing standardization processing on the facial feature information, grip strength difference information, and historical medical record information; Interactively processing the standardized grip strength difference information and the standardized historical medical record information to generate interactive feature information; constructing a multivariate regression model based on the facial feature information and the interactive feature information; Estimating the multiple regression model according to the least squares method to generate estimated model parameters; Adjusting the estimation model parameters according to a preset facial feature weight factor and a preset interaction feature weight factor, wherein the preset facial feature weight factor is greater than the preset interaction feature weight factor; generating a stroke risk assessment value through the multivariate regression model according to the adjusted estimated model parameters; Stroke prediction is performed based on the stroke risk assessment value.

2. The stroke prediction method according to claim 1, wherein The interactive processing of the standardized grip strength difference information and the standardized historical medical record information to generate interactive feature information includes: generating a grip force difference matrix according to the standardized grip force difference information; generating a historical medical record matrix according to the standardized historical medical record information; multiplying the grip strength difference matrix by the historical medical record matrix to generate interactive feature information; The constructing of a multivariate regression model according to the facial feature information and the interactive feature information includes: The multiple regression model is: ； Among them, Y is the stroke risk assessment value, is the facial feature information, … is the interactive feature information, is the error term, is the intercept term, are the initial model parameters.

3. The stroke prediction method according to claim 2, wherein: The step of estimating the multivariate regression model according to the least squares method to generate estimated model parameters includes: The estimated model parameters are determined by minimizing the sum of squared errors using the following formula: ； Where m is the number of samples.

4. The stroke prediction method according to claim 3, characterized in that: The adjusting the estimation model parameters according to the preset weight factors includes: Constructing a weighted loss function according to the facial feature weight factor and the interactive feature weight factor; Determining a weighted error corresponding to the facial feature information and the interactive feature information according to the weighted loss function; The weighted loss function is optimized by a gradient descent algorithm according to the weighted error, and the estimation model parameters are adjusted.

5. A stroke management system, characterized in that: It includes a facial recognition unit, a grip strength acquisition unit, a questionnaire survey unit, and a data prediction unit; The facial recognition unit is used to obtain facial recognition information and send the facial recognition information to the data prediction unit; The grip strength acquisition unit is used to acquire grip strength difference information and send the grip strength difference information to the data prediction unit; The questionnaire unit is used to obtain historical medical record information and send the historical medical record information to the data prediction unit; The data prediction unit is used to execute the stroke prediction method according to any one of claims 1 to 4 based on the facial recognition information, the grip strength difference information and the historical medical record information.

6. The stroke management system according to claim 5, characterized in that: Also included is an automatic registration unit; The automatic registration unit is used to automatically generate registration information according to the stroke prediction result, wherein the registration information includes the stroke prediction result.

7. A stroke prediction device, characterized in that: include: A processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus; The memory is used to store at least a program, and the program enables the processor to perform the operation of the stroke prediction method according to any one of claims 1 to 4.

8. A computer-readable storage medium, characterized in that The storage medium stores at least one program, and when the program is executed on the stroke prediction device, the stroke prediction device executes the operation of the stroke prediction method according to any one of claims 1 to 4.

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