WO2019223508A1

WO2019223508A1 - Method for establishing alzheimer's disease stage assessment model, and computer device

Info

Publication number: WO2019223508A1
Application number: PCT/CN2019/085447
Authority: WO
Inventors: 郑亢; 王建春; 游华; 阙中有; 陈琦; 陈文�; 张小光; 林琅; 卢立; 苗成林; 曹启晨; 高伟明
Original assignee: 深圳市前海安测信息技术有限公司
Priority date: 2018-05-25
Filing date: 2019-05-05
Publication date: 2019-11-28
Also published as: CN110534189A

Abstract

A method for establishing an Alzheimer's disease stage assessment model and a computer device. The method comprises steps of: acquiring electronic medical history data regarding various symptoms of Alzheimer's disease (AD) patients (S21); performing big data analysis on each data item of the electronic medical history data regarding various symptoms, and obtaining a pathogenesis mechanism corresponding to a given symptom (S22); sequentially displaying, by means of a virtual reality device, brain neural network images related to the pathogenesis mechanism of each symptom on a display (S23); automatically acquiring, from the brain neural network images of each symptom, a brain neural region affecting the pathogenesis mechanism (S24); performing lesion tissue identification on the brain neural region of each pathogenesis mechanism, and incorporating an AD clinical staging scale to obtain AD stages (S25); and performing, according to all of the electronic medical history data of the AD patients and the stages, abstraction modeling to establish an AD stage assessment model (S26). The AD stage assessment model is established to enable comprehensive assessment of symptom-related behaviors of an AD patient, and to provide an evidentiary basis for treating the AD patient.

Description

Method and computer device for creating Alzheimer's disease grade evaluation model

Technical field

The present invention relates to the technical field of Alzheimer's disease assessment, and in particular, to a method and computer device for creating a hierarchical assessment model for Alzheimer's disease.

Background technique

Alzheimer disease (AD, also known as "Alzheimer's disease") is a type of cognitive impairment that can cause memory, execution, visual space, language communication, abstract thinking, learning and computing. Neurodegenerative diseases. Alzheimer's often occurs in old age or presenile age, and the risk of AD increases with age. Clinical manifestations include memory impairment, misidentification, impaired computing power, personality and behavior changes. Cognitive decline in AD patients is irreversible, so early detection and early intervention are especially critical to delay the progression of the disease. Cognitive assessment scales are particularly important in AD screening, such as the simple Alzheimer's disease assessment scale (ADAS), the simple mental state test scale (MMSE), the intelligent screening test (CASI), and the Montreal cognitive assessment Scale (MOCA), Memory and Execution Screening Scale (MES), Multidimensional Psychological Cognitive Ability Assessment. However, the AD cognitive assessment scale in the prior art cannot perform a graded assessment of AD disorders to comprehensively evaluate the disease behavior of AD patients, and therefore cannot find an effective method for treating AD patients. Therefore, it is necessary to provide a system and method for creating an Alzheimer's disease graded evaluation model to grade the degree of AD disease, so as to comprehensively evaluate the disease behavior of AD patients and provide a basis for the treatment of AD patients.

technical problem

The main object of the present invention is to provide an Alzheimer's disease rating assessment model creation method and a computer device, which are aimed at solving the AD cognitive assessment scale in the prior art that cannot grade the assessment of AD disorders to comprehensively evaluate AD patients. Technical issues with illness behavior.

Technical solutions

To achieve the above object, the present invention provides a computer device for creating an Alzheimer's disease grade evaluation model. The computer device includes a virtual reality device, a display, a processor suitable for implementing various computer program instructions, and a memory suitable for storing. A memory of a plurality of computer program instructions, which are loaded by a processor and execute the following steps: obtaining electronic medical record data of various diseases of AD patients with Alzheimer's disease; Big data analysis to obtain the pathogenesis of each disease; use the virtual reality device to display the neural network images related to the pathogenesis of each disease in turn on the display; automatically from the brain neural network images of each disease Obtain the brain nerve regions that affect the pathogenesis; identify the diseased tissue of each brain pathology region and combine the AD clinical rating scale to obtain the AD disease severity rating; according to the data of the electronic medical records of each AD patient and the disease severity Hierarchical abstraction modeling is used to construct a hierarchical assessment model for AD disease.

Preferably, the computer device is connected to a medical institution server through a communication network, and the step of obtaining electronic medical record data of various diseases of AD patients with Alzheimer's disease includes the following steps: collecting each AD patient for a health check at a regional medical institution Or electronic medical record information at the time of treatment, and the electronic medical record information of each AD patient is stored in the medical shared database of the medical institution server; the communication unit reads the various diseases of the AD patient from the medical shared database of the medical institution server through the communication unit. Electronic medical record data.

Preferably, the step of performing big data analysis on the electronic medical record data of each disease to obtain the pathogenesis corresponding to each disease includes the following steps: using the results of brain science and medical research on various types of brain neuron cells Analysis of spatial location and combined with the data of the electronic medical records of each disease to perform big data mining analysis to obtain pathological analysis results; according to the results of pathological analysis, emotions, anxiety, memory, execution, visual space, language communication with AD patients, Pathogenesis related to abstract thinking, learning ability and computing ability.

Preferably, the computer program instructions are loaded by the processor and further perform the following steps: using the AD disease hierarchical evaluation model to formulate corresponding health management services for AD patients with different diseases.

Preferably, the AD disease grade evaluation model includes a forgetfulness model, a disordered model, and a dementia model, and the health management service includes: a health management service customized for personalized memory enhancement training for AD patients with amnesia; Customized health management services for AD patients with personalized rehabilitation training; customized health management services for personalized family doctor services for AD patients with dementia.

In another aspect, the present invention also provides a method for creating an Alzheimer's disease rating evaluation model, which is applied to a computer device, the computer device includes a virtual reality device and a display. The method for creating an Alzheimer's disease rating evaluation model includes Steps: Obtain electronic medical record data of various diseases of AD patients with Alzheimer's disease; perform big data analysis on the electronic medical record data of each disease to obtain the corresponding pathogenesis of each disease; The brain neural network images related to the pathogenesis of each disease are presented on the display in turn; the brain neural areas that affect the pathogenesis are automatically obtained from the brain neural network images of each disease; The diseased tissue is identified and combined with the AD clinical classification scale to obtain the AD disease grade; based on the electronic medical record data of each AD patient and the disease degree grade, abstract modeling is performed to build an AD disease grade assessment model.

Preferably, the method for creating an Alzheimer's disease graded assessment model further includes the following steps: using the AD disease graded assessment model to formulate corresponding health management services for AD patients with different disorders, wherein the AD disease graded assessment model includes Amnesia model, confusion model and dementia model, the health management services include: health management service customized for AD patients with amnesia and personalized rehabilitation training; health management customized for personalized rehabilitation training for AD patients with disorder Services; Health management services tailored to personalized family doctor services for AD patients with dementia.

In another aspect, the present invention also provides a computer storage medium. The computer storage medium stores a plurality of computer program instructions, wherein the computer program instructions are loaded by a processor of a computer device and execute the Alzheimer's. Method for creating disease grade assessment model.

Beneficial effect

Compared with the prior art, the method and computer device for creating a graded assessment model of Alzheimer's disease according to the present invention adopts the above technical solution, and achieves the following technical effects: By performing big data mining and electronic medical record data of each AD disease, Analyze the pathogenesis of each disease, present the pathogenesis of each disease through VR technology, and combine with the AD clinical rating scale to perform abstract modeling to build an AD disease rating evaluation model, so as to provide theoretical basis and effective for the treatment of AD patients Treatment services.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a preferred embodiment of a computer device for creating an Alzheimer's disease grade assessment model according to the present invention;

FIG. 2 is a flowchart of a preferred embodiment of a method for creating an Alzheimer's disease grade assessment model according to the present invention.

The realization of the purpose, functional characteristics and advantages of the present invention will be further described with reference to the embodiments and the drawings. .

Best Mode of the Invention

In order to further explain the technical means and effects adopted by the present invention to achieve the intended purpose of the present invention, the specific implementation, structure, features, and effects of the present invention are described in detail below with reference to the drawings and preferred embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

Referring to FIG. 1, FIG. 1 is a block diagram of a preferred embodiment of a computer apparatus for creating an Alzheimer's disease grade assessment model according to the present invention. In this embodiment, the computer device 1 includes, but is not limited to, an Alzheimer's disease rating evaluation model creation system 10, a communication unit 11, a virtual reality device 12, a display 13, a memory 14, and a processor 15. The communication unit 11, the virtual reality device 12, the display 13, and the memory 14 are all electrically connected to the processor 15 through a data bus, and can be performed by the processor 15 and the Alzheimer's disease rating assessment model creation system 10. Information exchange.

In this embodiment, the computer device 1 may be a computing device including a computer, a server, a cloud platform server, and the like having a data processing and communication function, including a virtual reality device 12. The computer device 1 is connected to one or more medical institution servers 2 through a communication network 3. The medical institution server 2 is installed in a regional medical institution, such as a hospital at all levels, a medical examination center, or a health management center, and can manage the regional medical center. Medical information generated by all medical institutions under its jurisdiction. The medical institution server 2 includes a medical shared database 20 that stores data information such as electronic medical records, medical image information, and medical test information of AD patients for the computer device 1 to download from the medical institution server 2 through the communication network 3 The data of the electronic medical records of AD patients were read to establish a graded assessment model of Alzheimer's disease. The communication network 3 may be a wireless network (such as a communication network such as GPRS, CDMA) or an Internet network (such as a communication network such as the Internet).

In this embodiment, the communication unit 11 is a wired communication interface or a wireless communication interface, for example, communication supporting communication technologies such as GSM, GPRS, WCDMA, CDMA, TD-SCDMA, TD-LTE, and FDD-LTE. An interface. The communication unit 11 is used for communication between the computer device 1 and the medical institution server 2 through a communication network, for example, the computer device 1 collects various medical records of AD patients from the medical institution server 2. The virtual reality device 12 is used to sequentially display images of the cerebral neural network related to the pathogenesis of each disease on the display 13. The virtual reality device 12 includes, but is not limited to, virtual brain nerves for providing AD patients. The network image source and the optical module displaying the 3D picture of the virtual brain neural network can present the virtual brain neural network image on the display 13. The display 13 can display a 3D image, and can display a neural network image related to the pathogenesis of each AD patient's condition. The memory 14 may be a read-only memory ROM, an electrically erasable memory EEPROM, or a flash memory FLASH, etc., and is used to store a series of program instruction codes and an Alzheimer's disease hierarchical evaluation model creation system 10 Model for the evaluation of Zheimer's disease. The processor 15 may be a microprocessor, a data processing chip, or an information processing unit with a data processing function, and is configured to execute the Alzheimer's disease rating assessment model creation system 10 to complete the establishment of Alzheimer's. The function of the disease grade assessment model.

In this embodiment, the Alzheimer's disease rating evaluation model creation system 10 includes, but is not limited to, a medical record data collection module 101, a pathogenesis analysis module 102, a pathogenesis presentation module 103, a brain neuron recognition module 104, The illness degree grading module 105, the illness model creation module 106, and the health service customization module 107. The module in the embodiment of the present invention refers to a series of computer program instruction segments that can be executed by the processor 15 of the computer device 1 and can complete fixed functions, and is stored in the memory 14 of the computer device 1 In the following, the function of each module is described in detail with reference to FIG. 2.

As shown in FIG. 2, FIG. 2 is a flowchart of a preferred embodiment of a method for creating an Alzheimer's disease grade evaluation model according to the present invention. In this embodiment, the various method steps of the method for creating an Alzheimer's disease graded assessment model are implemented by a computer software program, and the computer software program is stored in a computer-readable storage medium in the form of computer program instructions (for example, Applied to the memory 14) of the computer device 1, the storage medium may include a read-only memory, a random access memory, a magnetic disk, or an optical disk, etc., and the computer program instructions can be loaded by a processor (for example, the processor 15 applied to the computer device 1). And the following steps S21 to S27 are executed. The method includes the following steps:

Step S21: Collect electronic medical record data of various diseases of Alzheimer's patients; specifically, the medical record data collection module 101 reads electronic medical records of various diseases of AD patients from the medical shared database 20 of the medical institution server 2 through the communication unit 11 data. In this embodiment, the various disorders of the AD patient are generally manifested as neurodegenerative disorders of brain cognitive dysfunction in terms of memory, execution, visual space, language communication, abstract thinking, learning and computing. The electronic medical record data of various conditions of the AD patient include, but are not limited to, individual conditions, living habits, education level, genetic history, mentality, cognition, positioning, limb mobility, MRI images, and brain PET scan images of AD patients. And other data information. When various AD patients go to regional medical institutions (such as hospitals at all levels, physical examination centers, or health management centers) for medical examination or treatment, the medical shared database 20 of the medical institution server 2 stores the electronic medical record information of each AD patient. As a result, a large amount of electronic medical record information is formed and can be shared with scientific research institutions or regional medical institutions.

In step S22, big data analysis is performed on the data of the electronic medical records of each disease to obtain the pathogenesis corresponding to each disease. Specifically, the pathogenesis analysis module 102 uses the results of brain scientific medical research on the electronic information of each disease. The data of the medical records are analyzed by big data to obtain the pathogenesis of each disease. In this embodiment, the pathogenesis analysis module 102 is used to analyze the spatial localization of various types of brain neuron cells using the results of brain science and medical research, and combine the data of the electronic medical records of each disease to perform big data mining analysis to obtain the pathological analysis results; according to The pathological analysis results obtained pathogenesis related to mood, anxiety, memory, execution, visual space, language communication, abstract thinking, learning ability and computing ability of AD patients.

In step S23, the brain neural network images related to the pathogenesis of each disease are sequentially presented on the display by the virtual reality device; specifically, the pathogenesis presentation module 103 presents the pathogenesis of each disease through the virtual reality device 12. Relevant brain neural network images are presented on the display 13 in turn. In this embodiment, the virtual reality device 12 is used to sequentially display the brain neural network images related to the pathogenesis of each disease on the display 13 through virtual reality (VR) technology. The virtual reality The device 12 provides an image source of the virtual brain neural network of various diseases of the AD patient and an optical module displaying a 3D picture of the virtual brain neural network, and can display the virtual brain neural network image on the display 13. The present invention uses the virtual reality device 12 to reconstruct and reproduce the working process of the “brain GPS” of AD patients, and determines the pathology with AD patients according to the information interaction mechanism between the spatial cells (header cells) and the head-orientation cells. The position coordinate information of each neuron in the related brain neural network forms a brain neural network image related to the pathogenesis of each disease of the AD patient, and each brain neural network image is sequentially presented on the display 13.

Step S24: Automatically obtain the brain nerve region that affects the pathogenesis from the image of the brain neural network of each disease; specifically, the brain neuron recognition module 104 automatically obtains the brain nerve that affects the pathogenesis from the brain neural network image of each disease. region. In this embodiment, the position coordinates formed by the spatial cells (Boundary cells) and the orientation cells (Head-orientation cells) of the pathogenesis of each disorder determine the composition of the brain neural network of each disorder, so the brain neuron recognition module 104 By identifying the position coordinates of each neuron cell in the image of the neural network of the brain, the region of the brain nerve that affects the pathogenesis of each disease can be automatically obtained.

Step S25, identifying the diseased tissue of each pathogenesis of the cerebral nerve region and combining the AD clinical grading scale to obtain the AD disease level classification; specifically, the disease level grading module 105 performs pathological changes on the cerebral nerve region of each pathogenesis Tissue recognition (that is, identifying the position coordinate information of each neuron cell in the nerve region of the brain), and combined with the AD clinical rating scale to obtain the disease severity rating of each patient. In this embodiment, the AD clinical rating scale includes a grade i (+): physiological mental aging, which is a critical state between normal and dementia, is easy to forget instantly, does not have sufficient resilience, and belongs to mild mental decline. Significant oblivion, sluggish understanding and expression, poor calculations, and a decline in the ability to analyze and judge are mainly manifested in memory loss, inability to remember or remember. In addition, the ability to analyze and judge is reduced, social function is reduced, emotional instability appears, feelings are cold, easy to be agitated, and suspicious; grade ii (+ +): moderate mental decline, mild misorientation, significant forgetfulness, Personality tends to instinct, lack of independent living ability, life needs people to take care of, mainly manifested as severely impaired memory, greatly reduced visual recognition ability, disorientation of time and place, patients can not remember the appearance and name of family members, easy to get lost; Grade iii (+++): It is severe mental depression, important experiences are forgotten, serious orientation obstacles, difficulty in understanding and expression, little contact with the outside world, basically no character display, numbness, no desire to sleep more, loss of basic living ability, need People take care, can't perceive the outside world, don't know their own existence, lose all, mainly manifested in patients with severe brain function decline, memory loss, behavior degradation, life can not take care of themselves, can not speak, incontinence, extreme dependence on caregivers, often die Complications such as pneumonia and urinary tract infection.

Step S26: Perform abstract modeling based on the collected electronic medical record data and the level of AD disease to construct an AD disease classification evaluation model. Specifically, the disease model creation module 106 performs abstraction based on the collected electronic medical record data and the level of AD disease. Modeling, for example, according to the individual information, living habits, education level, genetic history, mentality, cognition, positioning, limb mobility, MRI images, and brain PET scans of AD patients, from brain tissue changes, mental conditions, Cognitive ability, memory ability, localization ability, limb movement ability and other aspects have been established to evaluate the three AD disease graded models. The AD disease grade evaluation model includes a forgetfulness model, a disordered model, and a dementia model, wherein the amnestic model belongs to a class i (+) model, and the disordered model belongs to a class ii (+ +) model. The model belongs to the iii (++) model. The above three AD disease classification assessment models are adaptive to the clinical data of the AD patients and the clinical data in the AD clinical classification scale.

In step S27, a corresponding health management service is formulated for AD patients with different diseases by using the AD disease hierarchical evaluation model; specifically, the health service customization module 107 formulates corresponding health management services for AD patients with different diseases according to the AD disease hierarchical evaluation model. In this embodiment, the health service customization module 107 customizes the health management service of personalized memory enhancement training for AD patients with amnesia: according to the time of the event, category, involved characters, etc., using oral, photo, interactive Q & A, etc., using AI And machine learning in real-time, frequently, and vividly provide training for scene resuscitation, face recognition, voice simulation, memory stimulation, memory enhancement, etc. for the sick elderly; health service customization module 107 customizes personalized rehabilitation training for AD patients with confusion Health management services: Provide scene entry, voice prompts, medication guidance, telephone consultation, memory training, memory enhancement, memory recovery, anti-lost and fall prevention, and course tracking; health service customization module 107 is customized for AD patients with dementia Personalized family doctor service health management services: equipped with private doctors, health managers, psychological counselors, mainly medical treatment, provide care services, and provide psychological counseling, family care, hospice care and other services.

The method and computer device for creating a graded assessment model of Alzheimer's disease according to the present invention can obtain the pathogenesis of each disease through big data mining and analysis of the electronic medical record data of each AD disease. The pathogenesis of a disease combined with the AD clinical classification scale for abstract modeling to build a model for the assessment of AD disease classification, so as to provide an effective theoretical basis for the treatment of AD patients with different diseases, and to formulate corresponding health for AD patients with different diseases. Management services.

Those skilled in the art may understand that all or part of the steps of the various methods in the foregoing embodiments may be completed by related program instructions. The program may be stored in a computer-readable storage medium. The storage medium may include a read-only memory, a random access memory, Disk or CD, etc.

The above are only preferred embodiments of the present invention, and thus do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the description and drawings of the present invention, or directly or indirectly used in other related technical fields All are included in the patent protection scope of the present invention.

Industrial applicability

Claims

A computer device for creating an Alzheimer's disease grade assessment model, characterized in that the computer device includes a virtual reality device, a display, a processor suitable for implementing various computer program instructions, and a computer suitable for storing a plurality of computers. A memory of program instructions, said computer program instructions being loaded by a processor and performing the following steps:

Obtain electronic medical record data of various diseases of AD patients with Alzheimer's disease;

Perform big data analysis on the data of the electronic medical records of each disease to obtain the corresponding pathogenesis of each disease;

The images of the brain neural network related to the pathogenesis of each disease are presented on the display through the virtual reality device in order;

Automatically obtain brain nerve regions that affect the pathogenesis from the brain neural network images of each disease;

Identify the diseased tissue of each pathological brain region and combine it with the AD clinical grading scale to obtain the degree of AD disorder;

According to the data of the electronic medical records of each AD patient and the classification of the degree of the disease, abstract modeling was performed to construct a model for evaluating the grade of the AD disease.
The computer device for creating a graded assessment model of Alzheimer's disease according to claim 1, wherein the computer device is connected to a server of a medical institution through a communication network, and various types of AD patients with Alzheimer's disease are obtained. The steps of the electronic medical record data of the illness include the following steps:

Collect the electronic medical record information of each AD patient during the medical examination or treatment in the regional medical institution, and store the electronic medical record information of each AD patient in the medical shared database of the medical institution server;

Read electronic medical record data of various diseases of AD patients from the medical shared database of the medical institution server through the communication unit.
The computer device for creating a graded assessment model of Alzheimer's disease according to claim 1, wherein the big data analysis is performed on each piece of data of the electronic medical record of each disease to obtain a response corresponding to each disease. The pathogenesis steps include the following steps:

Use the results of brain science and medical research to analyze the spatial localization of various types of brain neurons and combine the data of the electronic medical records of each disease to perform big data mining analysis to obtain the results of pathological analysis;

According to the results of pathological analysis, the pathogenesis related to mood, anxiety, memory, execution, visual space, language communication, abstract thinking, learning ability and computing ability of AD patients was obtained.
The computer device for creating an Alzheimer's disease grade evaluation model according to any one of claims 1 to 3, wherein the computer program instructions are loaded by a processor and further perform the following steps: using AD disease grade evaluation The model develops corresponding health management services for AD patients with different conditions.
The computer device for creating a graded assessment model of Alzheimer's disease according to claim 4, wherein the graded assessment model of AD disease comprises a forgetfulness model, a disordered model, and a dementia model, and the health management Services include: customized health management services for AD patients with amnesia; customized health management services for AD patients with confusion; customized family doctor services for AD patients with dementia Management services.
A method for creating an Alzheimer's disease graded assessment model is applied to a computer device. The computer device includes a virtual reality device and a display. The method for creating an Alzheimer's disease graded assessment model includes the following steps:

Obtain electronic medical record data of various diseases of AD patients with Alzheimer's disease;

Perform big data analysis on the data of the electronic medical records of each disease to obtain the corresponding pathogenesis of each disease;

The images of the brain neural network related to the pathogenesis of each disease are presented on the display through the virtual reality device in order;

Automatically obtain brain nerve regions that affect the pathogenesis from the brain neural network images of each disease;

Identify the diseased tissue of each pathological brain region and combine it with the AD clinical grading scale to obtain the degree of AD disorder;

According to the data of the electronic medical records of each AD patient and the classification of the degree of the disease, abstract modeling was performed to construct a model for evaluating the grade of the AD disease.
The method for creating a hierarchical evaluation model of Alzheimer's disease according to claim 6, wherein the computer device is connected to a server of a medical institution through a communication network, and the electronic medical records of various diseases of AD patients with Alzheimer's disease are obtained. The data steps include the following steps:

Collect the electronic medical record information of each AD patient during the medical examination or treatment in the regional medical institution, and store the electronic medical record information of each AD patient in the medical shared database of the medical institution server;

Read electronic medical record data of various diseases of AD patients from the medical shared database of the medical institution server through the communication unit.
The method for creating a graded evaluation model of Alzheimer's disease according to claim 6, wherein the big data analysis is performed on each piece of data of the electronic medical record of each disease to obtain the corresponding pathogenesis of each disease. The steps include the following steps:

Use the results of brain science and medical research to analyze the spatial localization of various types of brain neurons and combine the data of the electronic medical records of each disease to perform big data mining analysis to obtain the results of pathological analysis;

According to the results of pathological analysis, the pathogenesis related to mood, anxiety, memory, execution, visual space, language communication, abstract thinking, learning ability and computing ability of AD patients was obtained.
The method for creating an Alzheimer's disease grade assessment model according to any one of claims 6 to 8, wherein the method further comprises the following steps:

Use the AD assessment model to develop corresponding health management services for AD patients with different disorders. The AD assessment model includes amnesia, confusion, and dementia models. The health management services include: for amnesia Health management services with personalized memory enhancement training for AD patients in the current period; health management services with personalized rehabilitation training for AD patients with the disorder period; health management services with personalized family doctor service for AD patients with dementia period.
A computer storage medium storing a plurality of computer program instructions, wherein the computer program instructions are loaded by a processor of a computer device and executed by the Alzheimer according to any one of claims 6 to 9. Method for creating a model for grading assessment of silent disease.