Intelligent detection method and system for brain cognitive function

Abstract

The invention provides a brain cognitive function intelligent detection method and system, and belongs to the technical field of intelligent medical treatment. The method comprises the following steps: acquiring neuropsychology data of a test object, wherein the neuropsychology data comprises cognitive information; inputting the neuropsychology data into a trained prediction model to determine whether the test object has cognitive function impairment or not; wherein the cognitive function impairment is caused by electromagnetic radiation. According to the method, the model is adopted for cognitive function damage prediction, subjective factors of detection of a traditional method are effectively avoided, operation is easy and convenient, repeated measurement can be achieved, the prediction result can be obtained in real time, and interactivity with a user is higher. By combining multi-dimensional neuropsychology data, early warning of cognitive function impairment is achieved, the accuracy and sensitivity are high, and the misdiagnosis rate is greatly reduced. Meanwhile, support is provided for personalized medical decision making, and good adaptability and expandability are achieved. The method is particularly suitable for people with cognitive function impairment caused by electromagnetic radiation.

Description

Technical Field

[0001] The present application relates to the field of smart medical technology, and in particular to a method and system for intelligent detection of brain cognitive functions. Background Art

[0002] Electromagnetic radiation is widely used in various fields, and the brain damage effects caused by electromagnetic radiation have also received more and more attention. Studies have shown that electromagnetic radiation causes imbalances in important molecular levels such as neurotransmitters in the brain of animals, pathological changes in brain tissue structure, decreased memory ability, and abnormal behaviors such as anxiety and depression. Epidemiology also shows that electromagnetic radiation can cause damage to brain structure and function and changes at the molecular level, leading to symptoms such as memory impairment, anxiety and depression in people exposed to radiation.

[0003] Brain damage may cause neurological and psychiatric diseases, leading to cognitive impairments such as memory, attention, execution, and visual space, which seriously reduce the patient's quality of life. Clinically, neuropsychological testing methods are used to evaluate cognitive function, mainly scale examinations. Scale evaluation is time-saving, simple to operate, and has high sensitivity and specificity. It can quickly evaluate the patient's cognitive function. However, some scales cannot be reused.

[0004] In addition to scales, computerized neuropsychological tests developed based on a variety of experimental paradigms can be used to detect neurobehavioral abilities. Although computerized tests provide a neutral environment and a highly consistent presentation mode that improves portability and experimental reliability, existing computerized testing methods still have many shortcomings in terms of system integration, interactivity, data processing capabilities, personalized assessment and training, multi-dimensional information collection, and equipment requirements.

[0005] Therefore, existing methods for predicting cognitive impairment still need to be improved. Summary of the invention

[0006] The present application aims to solve at least one of the existing problems. To this end, the present application proposes an accurate, sensitive and repeatable method for predicting cognitive impairment.

[0007] Specifically, this application provides the following technical solutions:

[0008] In the first aspect, the present application proposes a method for predicting cognitive impairment. According to an embodiment of the present application, the method comprises: obtaining neuropsychological data of a test subject, wherein the neuropsychological data comprises: cognitive information; inputting the neuropsychological data into a trained prediction model to determine whether the test subject has cognitive impairment; wherein the cognitive impairment is caused by electromagnetic radiation.

[0009] In some examples of this application, the aforementioned method uses a model to predict cognitive impairment, effectively avoiding the subjective factors of traditional detection methods, easy to operate, repeatable measurement, real-time prediction results, and stronger interactivity with users. By combining multi-dimensional neuropsychological data, early warning of cognitive impairment can be achieved with high accuracy and sensitivity, greatly reducing the misdiagnosis rate. At the same time, it provides support for personalized medical decision-making and has good adaptability and scalability. The aforementioned method is particularly suitable for people with cognitive impairment caused by electromagnetic radiation.

[0010] In the second aspect, the present application proposes a cognitive function impairment prediction system. According to an embodiment of the present application, the device includes: a neuropsychological data acquisition module, which is used to acquire neuropsychological data of a test subject, wherein the neuropsychological data includes: cognitive information; an identification module, which is used to input the neuropsychological data into a trained prediction model to determine whether the test subject has cognitive function impairment; wherein the cognitive function impairment is caused by electromagnetic radiation.

[0011] In some examples of the present application, the aforementioned device realizes automated, high-precision, and personalized cognitive impairment detection, and has real-time, repeatability, and good adaptability, and is particularly suitable for predicting cognitive impairment caused by electromagnetic radiation. The device is easy to operate, user-friendly, and significantly reduces the misdiagnosis rate.

[0012] In a third aspect, the present application proposes a computer program product. According to an embodiment of the present application, the computer program product includes: computer instructions; when part or all of the computer instructions are run on a computer, the method for predicting cognitive impairment as described in the first aspect of the present application is executed.

[0013] In a fourth aspect, the present application proposes a computing device. According to an embodiment of the present application, the computing device includes: a processor and a memory; the memory is used to store a computer program; the processor is used to execute the computer program to implement the cognitive function impairment prediction method as described in the first aspect of the present application.

[0014] In a fifth aspect, the present application proposes a computer-readable storage medium. According to an embodiment of the present application, the computer-readable storage medium stores computer instructions or programs, and when the computer instructions or programs are executed on a computer, the method for predicting cognitive impairment as described in the first aspect of the present application is executed.

[0015] In some examples of the present application, the aforementioned computer program product, computing device, and computer-readable storage medium achieve efficient automation and improve efficiency and accuracy by automatically executing the cognitive impairment prediction method through computer instructions. Secondly, the characteristics of the instructions make the above method highly consistent and reliable in different environments.

[0016] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A schematic diagram of a cognitive impairment prediction system provided for a specific embodiment of the present application;

[0019] Figure 2 It is a schematic diagram of an electronic device provided in a specific embodiment of the present application. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0021] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In the present application, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. In the description of the present application, unless otherwise specified, "multiple" refers to two or more than two.

[0022] In this article, unless otherwise specified, the term "prediction model" is a computational model or algorithm that can automatically perform tasks such as prediction, classification, recognition or decision-making by learning and analyzing input data. The learning process of the model is based on statistical principles and data pattern recognition, and uses training data sets to adjust model parameters and optimize the model to improve its prediction or reasoning ability. Machine learning models can use various algorithms and techniques, such as logistic regression, ridge regression, linear regression, deep learning, neural networks, support vector machines, decision trees, random forests, etc. These models can be trained and optimized by supervised learning, unsupervised learning or reinforcement learning. In some examples of this application, a graph convolutional network is used to extract features from neuropsychological data, and random forests are used for integration to obtain a prediction model.

[0023] In this document, unless otherwise specified, the term "module" refers to a computer program or a part of a computer program that has a predetermined function and works together with other related parts to achieve a predetermined goal, and can be implemented in whole or in part by using software, hardware (such as processing circuits or memories) or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or modules. In addition, each module or module can be part of an overall device or module that includes the function of the device or module.

[0024] Existing technologies have obvious defects in assessing brain damage and cognitive impairment caused by electromagnetic radiation, including poor portability of traditional scale assessments, complex operation, partial subjectivity affecting assessment accuracy, and computerized neuropsychological testing in terms of system integration, interactivity, data processing capabilities, and personalized assessment. In addition, existing methods lack comprehensive integration of multi-dimensional neuropsychological data, making it difficult to fully reflect the complex effects of electromagnetic radiation on brain function. These limitations hinder accurate and comprehensive assessments of cognitive impairment and are in urgent need of improvement.

[0025] To this end, the present application proposes a cognitive impairment prediction method, a cognitive impairment prediction system, a computer program product, a computing device, and a computer-readable storage medium. The following describes each of them:

[0026] Methods for predicting cognitive impairment

[0027] In one aspect of the present application, the present application proposes a method for predicting cognitive function damage caused by electromagnetic radiation. The method comprises:

[0028] 1) obtaining neuropsychological data of a test subject, wherein the neuropsychological data includes: cognitive information;

[0029] In some examples of the present application, the aforementioned test subjects are selected from people with electromagnetic radiation, such as those working in an electromagnetic radiation environment or being exposed to electromagnetic radiation equipment for a long time, including power industry practitioners, communications industry practitioners, medical industry practitioners, military personnel, etc.

[0030] After extensive research, the inventors found that electromagnetic radiation has the greatest impact on memory among people with cognitive impairment. To this end, the neuropsychological data of the object obtained includes at least: cognitive information, and the cognitive information is selected from at least one of instantaneous memory, continuous memory, spatial location memory breadth and graphic memory. Further, the neuropsychological data may also include: at least one of physical examination information and scale information. By combining other types of data on the basis of cognitive information, people with cognitive impairment can be identified more accurately and sensitively.

[0031] Among them, the aforementioned physical examination information at least includes the duration of exposure to electromagnetic radiation, and may also include basic information (gender, age, etc.), education information, job information, daily working hours, weekly working hours, past medical history, family medical history and other information; the aforementioned scale information is selected from at least one of the test subject's MoCA (Montreal Cognitive Assessment), MMSE (Mini Mental State Examination), SAS (Self-Rating Anxiety Scale), SDS (Self-Rating Depression Scale), SCL-90 (90-item Symptom Checklist (Symptom Self-Rating Scale)) and PSQI (Pittsburgh Sleep Quality Index) scale information.

[0032] In some examples of the present application, the inventors further developed applications, websites, and program packages, etc., through which the neuropsychological data of the test subject is obtained. It is understandable that the aforementioned applications, websites, or program packages are pre-embedded with the neuropsychological data acquisition method.

[0033] 2) Inputting the neuropsychological data into a trained prediction model to determine whether the test subject has cognitive impairment; wherein the cognitive impairment is caused by electromagnetic radiation.

[0034] In some examples of the present application, the aforementioned prediction model is selected from at least one of Random Forest (RF), Decision Tree, Graph Convolution Networks (GCN), Graph Attention Network, Graph Autoencoders, Graph Generative Networks and Graph Spatial-temporal Networks. In some preferred examples, the aforementioned prediction model is selected from the integration of Random Forest and Graph Convolution Network models. The use of model prediction is significantly superior to existing testing methods, such as non-invasive repeated measurements, avoiding paper-based measurements, and the subjects can memorize the answers.

[0035] In the RF-GNN framework, the input graph data (i.e., neuropsychological data) is split into multiple subgraphs, each with its own adjacency matrix and node feature matrix. These subgraphs are subjected to feature extraction through GCN, which uses graph convolutional layers to aggregate information about neighboring nodes and update node features to generate a high-dimensional feature representation of the graph. These features are then fed into a fully connected network (FCN) for further feature processing, including dimensionality reduction and nonlinear transformation. The processed features are standardized and adjusted through an alignment mechanism to ensure that features from different subgraphs can be effectively fused at the same scale. The aligned features are fed into a model integration module, which may use ensemble learning methods such as random forests to fuse features from different branches and output the final classification prediction. In this way, the RF-GCN framework can more effectively utilize the local structural information of the graph and improve the accuracy and robustness of classification.

[0036] In some examples of the present application, the aforementioned prediction model is obtained by training in the following manner:

[0037] (1) obtaining training set data, wherein the training set data includes: a neuropsychological data set of normal subjects exposed to electromagnetic radiation and subjects with cognitive impairment;

[0038] In this step, the aforementioned training set can be selected from neuropsychological data collected by oneself or neuropsychological data from a public database. In a specific example of the present application, the inventor uses self-collected data for training and verification.

[0039] In this application, the inventor uses the MoCA scale standard threshold to divide the self-collected data into normal people and cognitive impairment people. By using clinical methods to divide the training data, the accuracy and reliability of the prediction results are further guaranteed. In a specific example of this application, the aforementioned standard threshold is selected from 26 points, and a score below 26 is considered cognitive impairment.

[0040] The inventors further considered factors such as education level in the training set. In order to avoid the impact of the scale test questions on the cognitive level of people with lower education levels (education years ≤ 12 years), the inventors determined that people with education years ≤ 12 years will be given an additional 1 point based on the standard threshold.

[0041] In this step, the neuropsychological data sets of normal subjects and cognitive impairment subjects exposed to electromagnetic radiation are determined through the MoCA scale information.

[0042] The neuropsychological data set used for training includes cognitive information, and the cognitive information is selected from at least one of instantaneous memory, continuous memory, spatial location memory span and graphic memory. Further, the neuropsychological data may also include: at least one of physical examination information and scale information.

[0043] Among them, the aforementioned physical examination information at least includes the duration of exposure to electromagnetic radiation, and may also include basic information (gender, age, etc.), job information, daily working hours, weekly working hours, past medical history, family medical history and other information; the aforementioned scale information is selected from at least one of the MoCA, MMSE, SAS, SDS and SCL-90 scale information of the test subject.

[0044] (2) preprocessing the neuropsychological data set to determine a valid neuropsychological data set;

[0045] After obtaining the neuropsychological data set, the abnormal data in the data set is further removed. In some examples of the present application, the abnormal data removal method includes: 1) removing data with scale information missing exceeding a first predetermined threshold; 2) removing data with cognitive information missing exceeding a second predetermined threshold.

[0046] The aforementioned first threshold may be selected from 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or 100%.

[0047] The aforementioned second threshold may be selected from 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or 100%.

[0048] (3) Inputting the valid neuropsychological data set into the prediction model, using the standard threshold as a label, performing supervised training on the prediction model, and determining a trained prediction model.

[0049] In this step, the standard threshold is also determined based on the MoCA score.

[0050] In some examples of the present application, the method further includes tuning parameters of the prediction model, including:

[0051] (1) Determine the optimal number of iterations of the model;

[0052] During the training process, the “early stopping method” is used to prevent the model from overfitting the training data. When the model does not continue to increase the training accuracy after 10 epochs, the model training is forcibly terminated.

[0053] (2) Determine the value of θ for constructing an adjacency matrix. The construction of the adjacency matrix is ​​based on the Euclidean distance between neuropsychological data. The distance matrix is ​​calculated and then the adjacency matrix is ​​generated according to a given threshold (θ). The positions with values ​​greater than θ in the matrix are set to 1, and the positions with values ​​less than or equal to θ are set to 0.

[0054] A 5-fold cross validation was set up, and a different θ value was selected for each fold, for example [1, 5, 10, 15, 50], to determine the final training effect of each fold, and the θ value was determined based on the accuracy evaluation indicator of the model.

[0055] (3) Determine the learning rate, number of trees, number of nodes, and number of edges.

[0056] In a specific example of the present application, the training of the aforementioned model includes:

[0057] First, read the data set, obtain the adjacency matrix, and obtain all the edges in the adjacency matrix;

[0058] According to the preset ratio (gamma = 0.75), the number of discarded edges is calculated (0.25), the samples are masked, and samples are taken from the training set. Nodes are selected according to a certain ratio, and sub-sampled from the training set. The edges to be retained are randomly selected because the sampling is selected with replacement. After checking, duplicate nodes and edges are deleted.

[0059] The sub-sampled data is used as features for training, and the trained model is saved.

[0060] In some examples of this application, a prediction model was obtained based on the above training method, and its accuracy was 61% and sensitivity was 62% in 100 samples. In addition, the inventor further collected an external validation set to verify the accuracy of the above prediction model, which is expected to be more than 75%.

[0061] Cognitive function impairment prediction system

[0062] In another aspect of the present application, the present application proposes a cognitive function impairment prediction system, referring to Figure 1 The device includes: a neuropsychological data acquisition module 100 and an identification module 200.

[0063] Module 100 is used to obtain neuropsychological data of the test subject, wherein the neuropsychological data includes: cognitive information;

[0064] In some examples of the present application, the cognitive information is selected from at least one of short-term memory, continuous memory, spatial location memory span, and graphic memory.

[0065] In some examples of the present application, the neuropsychological data may also include: at least one of physical examination information and scale information. The physical examination information includes: the duration of exposure to electromagnetic radiation; the scale information is selected from at least one of the MoCA, MMSE, SAS, SDS, SCL-90, and PSQI scale information of the test subject.

[0066] Module 200 is used to input the neuropsychological data into a trained prediction model to determine whether the test subject has cognitive impairment; wherein the cognitive impairment is caused by electromagnetic radiation.

[0067] In some examples of the present application, the prediction model is obtained by training in the following manner: obtaining training set data, the training set data including: a neuropsychological data set of normal subjects exposed to electromagnetic radiation and subjects with cognitive impairment; preprocessing the neuropsychological data set to determine a valid neuropsychological data set; inputting the valid neuropsychological data set into the prediction model, performing supervised training on the prediction model using a standard threshold as a label, and determining a trained prediction model.

[0068] The aforementioned preprocessing method includes: 1) eliminating data whose scale information is missing and exceeds a first predetermined threshold; 2) eliminating data whose cognitive information is missing and exceeds a second predetermined threshold.

[0069] In some examples of the present application, the prediction model is selected from at least one of a random forest, a decision tree, a graph convolutional network, a graph attention network, a graph autoencoder, a graph generation network, and a graph spatiotemporal network. In a preferred example of the present application, the prediction model is selected from an integration of a random forest and a graph convolutional network.

[0070] In some examples of the present application, the neuropsychological datasets of the normal subjects and the subjects with impaired cognitive functions exposed to electromagnetic radiation are determined by MoCA scale information.

[0071] Based on the aforementioned device, the prediction of cognitive function impairment of the subject can be achieved efficiently and automatically.

[0072] It should be understood that the device embodiment and the method embodiment may correspond to each other, and similar descriptions may refer to the method embodiment. To avoid repetition, they will not be described here. Specifically, Figure 1 The device shown can execute the above-mentioned embodiment of the cognitive function impairment prediction method, and the operations and / or functions performed by each module in the device correspond to the method embodiment, which will not be repeated here for the sake of brevity.

[0073] The above describes the device of the embodiment of the present application from the perspective of the functional module in conjunction with the accompanying drawings. It should be understood that the functional module can be implemented in hardware form, can be implemented by instructions in software form, and can also be implemented by a combination of hardware and software modules. Specifically, the steps of the method embodiment in the embodiment of the present application can be completed by the hardware integrated logic circuit and / or software form instructions in the processor, and the steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware decoding processor to perform, or a combination of hardware and software modules in the decoding processor to perform. Optionally, the software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory, and completes the steps in the above method embodiment in conjunction with its hardware.

[0074] Computer program product, computing device, and computer readable storage medium

[0075] In another aspect, the present application provides a computer program product, a computing device, and a computer-readable storage medium. Based on the aforementioned computer program product, computing device, or computer-readable storage medium, the aforementioned cognitive function impairment prediction method is executed.

[0076] The so-called electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. Computing devices may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are intended to be examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0077] like Figure 2As shown, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 502 or a computer program loaded from a storage unit 508 to a RAM (Random Access Memory) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An I / O (Input / Output) interface 505 is also connected to the bus 504.

[0078] A number of components in the device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0079] The computing unit 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Units), various dedicated AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, a DSP (Digital Signal Processor), and any appropriate processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as a cognitive function impairment prediction method. For example, in some embodiments, the cognitive function impairment prediction method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to execute the aforementioned cognitive function impairment prediction method in any other appropriate manner (eg, by means of firmware).

[0080] In the present application, the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, which can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the aforementioned program may be printed, because the aforementioned program may be obtained electronically, for example, by optically scanning the paper or other medium, and then editing, interpreting or processing in other suitable ways as necessary, and then storing it in a computer memory. The various computer-readable storage media described in the present invention may represent one or more devices and / or other machine-readable storage media for storing information. The term "machine-readable storage medium" may include, but is not limited to, wireless channels and various other media capable of storing, containing and / or carrying instructions and / or data.

[0081] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0082] A person of ordinary skill in the art may understand that all or part of the steps in the method of the above-mentioned embodiment may be completed by instructing related hardware through a program, and the aforementioned program may be stored in a computer-readable storage medium, which, when executed, includes one of the steps of the method embodiment or a combination thereof.

[0083] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0084] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0085] It should be noted that the features and technical effects described in this article for different aspects can be used as reference for each other and will not be repeated here.

[0086] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0087] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention without departing from the principles and intent of the present invention.

Claims

1. A method for predicting cognitive impairment, characterized in that: include: obtaining neuropsychological data of the test subject, the neuropsychological data comprising: cognitive information; inputting the neuropsychological data into a trained prediction model to determine whether the test subject has cognitive impairment; Wherein, the cognitive function damage is caused by electromagnetic radiation.

2. The method according to claim 1, characterized in that The cognitive information is selected from at least one of instantaneous memory, continuous memory, spatial location memory span and graphic memory; Optionally, the neuropsychological data may further include: at least one of physical examination information and scale information; Optionally, the physical examination information includes: duration of exposure to electromagnetic radiation; Optionally, the scale information is selected from at least one of the test subject's MoCA, MMSE, SAS, SDSSCL-90 and PSQI scale information.

3. The method according to claim 1, characterized in that The prediction model is obtained by training in the following way: Acquiring training set data, the training set data comprising: a neuropsychological data set of normal subjects exposed to electromagnetic radiation and subjects with cognitive impairment; Preprocessing the neuropsychological data set to determine a valid neuropsychological data set; The effective neuropsychological data set is input into the prediction model, and the prediction model is supervisedly trained with a standard threshold as a label to determine a trained prediction model.

4. The method according to claim 3, characterized in that: The pre-processing comprises: 1) Eliminate data whose scale information is missing and exceeds a first predetermined threshold; 2) Eliminate data with cognitive information missing exceeding a second predetermined threshold.

5. The method according to claim 3, characterized in that: The prediction model is selected from at least one of a random forest, a decision tree, a graph convolutional network, a graph attention network, a graph autoencoder, a graph generation network, and a graph spatiotemporal network; Preferably, the prediction model is selected from a random forest-graph convolutional neural network.

6. The method according to claim 3, characterized in that The neuropsychological data sets of normal subjects and cognitive impairment subjects exposed to electromagnetic radiation are determined by MoCA scale information.

7. A cognitive function impairment prediction system, characterized in that: include: A neuropsychological data acquisition module is used to acquire neuropsychological data of the test subject, wherein the neuropsychological data includes: cognitive information; an identification module for inputting the neuropsychological data into a trained prediction model to determine whether the test subject has cognitive impairment; Wherein, the cognitive function damage is caused by electromagnetic radiation.

8. A computer program product, characterized in that include: Computer instructions; When part or all of the computer instructions are executed on a computer, the method for predicting cognitive function impairment according to any one of claims 1 to 7 is executed.

9. A computing device, characterized in that include: Processor and memory; The memory is used to store computer programs; The processor is used to execute the computer program to implement the method for predicting cognitive function impairment according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions or programs, and when the computer instructions or programs are executed on a computer, the method for predicting cognitive function impairment according to any one of claims 1 to 7 is executed.

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