KR20190101458A - Cognitive function evaluation system - Google Patents

Abstract

A cognitive function evaluation system for evaluating a risk of a plurality of basic diseases in a cognitive function of a subject based on a test item for the cognitive function, the measuring means for measuring a test value of a test item and a measurement means Calculation means for calculating the risk on the basis of the calculated test value, and selection means for selecting a basic disease having the highest risk among the calculated risks.

Description

Cognitive function evaluation system

The present invention relates to a cognitive function evaluation system for evaluating the cognitive function of a subject and presenting dementia risk for each basic disease of dementia.

With the recent aging, the number of patients with dementia has increased rapidly. While it is difficult to cure dementia once it occurs, it is important to find it at an early stage because it is possible to slow progression or alleviate symptoms by early treatment. However, the number of specialists who diagnose dementia does not increase so much that there is a need for a diagnostic support tool that can easily evaluate cognitive function.

Conventionally, for example, a diagnostic support tool as disclosed in Document 1 has been developed. This diagnostic support tool can change a questionnaire dynamically according to a subject's prior information, environmental information, and the response content of a subject, and can evaluate the cognitive function of a subject.

Japanese Patent Publication No. 2007-282992

However, dementia is caused by various basic diseases such as Alzheimer's dementia and cerebrovascular dementia, and the symptoms, prognosis, response, and treatment methods differ depending on the underlying diseases.

Nevertheless, the diagnostic support tool disclosed in Patent Literature 1 only evaluates the extent to which the cognitive function functions as a whole, and the risk of these various basic diseases (which basic diseases are likely to suffer from). ), It is not possible to provide meaningful information for the diagnosis by the specialist. In addition, dementia takes a progressive course, but the degree of progression varies depending on the underlying disease. Since the conventional tools perform the same evaluation on the entire dementia, it is impossible to predict the progression of the underlying disease. For this reason, even when verifying the intervention of something, for example, the effect of a drug, evaluation by basic disease is impossible.

This invention is made | formed in view of such a situation, Comprising: It aims at providing the diagnostic tool which can evaluate the risk by the basic disease of dementia when examining the cognitive function of a subject. Furthermore, it provides a prediction of progression by basic disease. In addition, in the present invention, it is also possible to impart retardation.

The cognitive function evaluation system of the present invention includes measurement means for evaluating risks of a plurality of basic diseases in a cognitive function of a subject based on a test item for the cognitive function, and measuring test values of the test item; And calculation means for calculating the risk based on the inspection value measured by the measurement means, and selection means for selecting the most basic disease having the highest risk among the calculated risks. Factors affecting cognitive function evaluation are also evaluated at the same time, and the accuracy of the judgment is increased. You may respond with an upper limb (finger) or lower limb or vocalization or blinking.

According to the present invention, the risk can be calculated for each of the underlying diseases causing dementia, and it becomes possible to more effectively support the diagnosis of dementia.

1 is a perspective view of a computer mounted with a cognitive function evaluation system according to an embodiment of the present invention.
2 is a functional block diagram of a cognitive function evaluation system.
3 is an example of a questionnaire in a cognitive function evaluation system.
4 is an example of a questionnaire in a cognitive function evaluation system.
5 is an example of a questionnaire in a cognitive function evaluation system.
6 is an example of a questionnaire in a cognitive function evaluation system.
7 is an example of a questionnaire in a cognitive function evaluation system.
8 is an example of a questionnaire in a cognitive function evaluation system.
9 is an example of a questionnaire in a cognitive function evaluation system.
10 is an example of a questionnaire in a cognitive function evaluation system.
11 is an example of a radial chart output from the cognitive function evaluation system.
12 is an illustration of logistic regression analysis in an example.
13 is an illustration of handling of a correction term in the embodiment.

EMBODIMENT OF THE INVENTION Hereinafter, embodiment of this invention is described in detail based on drawing. In addition, the following description of the embodiments is merely exemplary, and is not intended to limit the present invention, its application, or its use.

Embodiment

Embodiment of this invention is a cognitive function evaluation system mounted in the computer which functions as what is called standalone which does not require a communication line. 1 schematically shows a computer 1 on which the cognitive function evaluation system of the present embodiment is mounted. A cognitive function evaluation system as an embodiment of the present invention is realized by a combination of hardware of the computer 1 and software executed therein.

The computer includes a main body having a CPU, a RAM memory, a ROM, a hard disk, and the like, a liquid crystal display for displaying a screen, a keyboard for a medical worker to input various settings, and the like. And if the cognitive function evaluation system is installed in the hard disk of the computer 1 as software, and the installed cognitive function evaluation system is started, the computer 1 will function as the cognitive function evaluation system of this embodiment.

2 is a functional block diagram of the cognitive function evaluation system 10 in the present embodiment. The cognitive function evaluation system 10 includes a questionnaire storage unit 11, a screen display unit 12, a questionnaire changing unit 13, a test value measuring unit 14, a risk calculation unit 15, and a risk line. It consists of a government unit 16 and a result output unit 17.

In the questionnaire storage unit 11, approximately 70 questionnaire contents executed by the cognitive function evaluation system 10, respective points, response times, and inspection items corresponding to each questionnaire are stored. Although details will be described later, the test items include memory, material formula, knowledge, seal, calculation, understanding, judgment, execution function, and nine correction terms, and each questionnaire corresponds to any of these test items.

The screen display part 12 is comprised by the liquid crystal display, and displays each questionnaire to a subject. The subject sequentially answers the questionnaire displayed on the screen display unit 12 using a touch panel, a keyboard, a foot sensor, a blinking sensor, or a mouse. The screen display unit 12 is provided with an image capturing unit 23, and changes in the frequency of blinking and expression during the test of the subject are detected.

The questionnaire change unit 13 is a function that allows medical personnel to change the questionnaire content according to the subject. Specifically, it is provided with the point change means which can change the point | rate of each questionnaire, and the response time setting means which can set the answer time of each questionnaire.

The test value measuring unit 14 as the measuring means makes a noon judgment on the answers to each questionnaire of the subject, and scores the scores based on the points assigned to each question. And the score | count for every test item mentioned above is measured as a test value.

The risk calculation unit 15 as the calculation means calculates the risk of the underlying disease in the cognitive function of the subject based on the test value (score for each test item) measured by the test value measurement unit 14. In the present embodiment, Alzheimer's dementia (AD), cerebrovascular dementia (VaD), Lewy body dementia (DLBD), Parkinson's disease dementia (PDD), frontal temporal lobe dementia (FTD), cortical basal nucleus degeneration, Encephalitis (aftereffect), metabolic encephalopathy, and normal pressure hydrocephalus are targeted for risk calculation. In addition, by calculating the extent to which diseases and conditions to be distinguished from dementia, specifically depression, psychogenic response, neurosis, etc. affect cognitive function, the accuracy of risk calculation of the underlying disease caused by dementia is increased.

Although details will be described later, the risk of each basic disease is the risk of the underlying disease: X ₁ = A ₁ × (memory test value: X ₁ ) + A ₂ x (material expression test value: X ₂ ) + A ₃ × : X ₃ ) + A ₄ × (Acknowledgment test value: X ₄ ) + A ₅ × (Calculation power test value: X ₅ ) + A ₆ × (Understanding test value: X ₆ ) + A ₇ × (Determination test value: X ₇ ) + A ₈ As in × (execution function test value: X ₈ ) + (correction term test value: C), the test value of each test item is calculated by the first equation with the inclination coefficient represented by A _{1 to} A ₈ (where the memory , A ₁ is treated as A _1A , A _1B , and X ₁ is treated as X _1A , X _1B ). The combination of the inclination coefficients represented by (A ₁ , A ₂ ,..., A ₈ ) differs depending on each underlying disease. In the risk calculator 15, the risk of the underlying disease is calculated using a combination of the inclination coefficients corresponding to the respective underlying diseases.

When a correction term is not considered, the calculation means calculates the risk Y of a basic disease for every basic disease by the formula shown by following formula (1).

Y = A ₁ X ₁ + A ₂ X ₂ + A ₃ X ₃ + A ₄ X ₄ + A ₅ X ₅ + A ₆ X ₆ + A ₇ X ₇ + A ₈ X _{8 ...} (1)

When a correction term is considered, the calculation means calculates the risk Y of a basic disease for every basic disease by the formula shown by following formula (1 ').

Y = A ₁ X ₁ + A ₂ X ₂ + A ₃ X ₃ + A ₄ X ₄ + A ₅ X ₅ + A ₆ X ₆ + A ₇ X ₇ + A ₈ X ₈ + C _... (1 ')

In addition, although it mentions in detail later, the risk calculation part 15 accumulates the test result of a subject, and corrects the combination of the inclination coefficient of each basic disease by multivariate analysis based on the accumulated data by multivariate analysis 21 ). As multivariate analysis, for example, generally known logistic regression analysis can be used.

In addition, the risk calculation unit 15 includes a calculation result correction unit 22 for correcting the calculated risk of each basic disease. This is, for example, if a subject incorrectly answers a low difficulty item but corrects a high difficulty item, the incorrect answer of the low difficulty item is simply a mistake and is corrected to underestimate the risk of the underlying disease. In the case of negative inputs, corrections are made to underestimate the risk of underlying disease.

When the risk selection unit 16 as the selection means has a Y lower than a predetermined basic disease reference value determined to be the risk of the underlying disease among the risks for the underlying diseases calculated by the risk calculator 15, the underlying disease is determined. Is selected and displayed on the screen display unit 12. That is, among the calculated risks of the underlying disease, the risk is high for the subject. Alternatively, the calculated basic diseases may be displayed in order from the highest risk.

The risk selecting unit 16 as the selection means, in the calculated risk for each basic disease, has a Y that is equal to or greater than the basic disease reference value but lower than the predetermined MCI reference value determined to be at risk of MCI, the MCI of the basic disease is determined. Select that there is a risk.

The result output unit 17 creates a radial chart about the test result of the subject calculated by the risk calculation unit and outputs it as the test result.

In addition, the risk calculation part 15 is equipped with the noon-recording means in the response time which records whether each test item was correct within the response time. And if an answer is incorrect even if it answers within the response time of each test item, it cannot be said that it was correct within the response time. In addition, the risk calculation unit 15 includes problem processing capability evaluation means for evaluating the problem processing ability within the time limit for the test of the subject based on the number of test items that are correct within the response time. The evaluation of the subject's problem handling ability is output to the result output unit 17.

Specifically, the answer time of each test item is the answer time T ₁ of the memory test, the answer time T ₂ of the material test, the answer time T ₃ of the test, and the answer time T of the seal test. ₄ ), the response time T ₅ of the calculation test, the response time T ₆ of the comprehension test, the response time T ₇ of the judgment test, and the response time T ₈ of the execution function test. For example, when the memory test, the load test, the comprehension test, and the execution function test are correct during the response time, but the other tests cannot answer correctly within the response time, the noon recording means within the response time records this.

The problem handling ability evaluation means evaluates the subject's problem handling ability in comparison with a predetermined predetermined criterion. For example, if four or less of the eight test items, such as memory, subject matter, knowledge, visual recognition, calculation, understanding, judgment, and execution function, cannot be answered within the response time, the subject's problem-solving ability is negative. Is evaluated. For example, the subject's problem-solving ability can be evaluated by focusing on the test items that can be answered in a short time, for example, among the eight test items of memory, material expression, knowledge, seal, comprehension, comprehension, judgment, and execution. . For example, the seal person is asked a question as shown in FIG. 6 and can be answered in a short time, but the computational power is questioned as shown in FIGS. 7A and 7B and is not a problem to be answered in a short time.

The cognitive function evaluation system according to the present invention is capable of diagnosing the risk of dementia for each basic disease of dementia. For example, according to the cognitive function evaluation system according to the present invention, a subject may be evaluated as being Alzheimer's type dementia. Alzheimer's dementia can vary in its condition. According to the cognitive function evaluation system according to the present invention, since the problem processing ability evaluation means is provided, not only the basic disease of dementia can be determined, but also the condition can be evaluated. For example, Alzheimer's dementia is a disease based on aggravation of memory disorders, but the conditions include memory disorders, material disorders, learning disorders, attention disorders, space-time cognitive disorders, and problem processing ability disorders. For example, depending on the person determined to be Alzheimer's dementia, there is a person who is determined to have a memory disorder, a food disorder, or no problem processing ability, a memory disorder, a material disorder, or problem processing. Some people are judged to be impaired.

According to the cognitive function evaluation system of the present invention, when the subject is judged to have a high risk of Alzheimer's dementia, for example, and the evaluation of problem-solving ability is negatively evaluated (that is, the subject has a risk of Alzheimer's dementia). High, impaired in memory and impaired in ability to handle problems) may intensify the subject's care because there is a risk of the subject's daily behavior. If the subject is judged to have a high risk of Alzheimer's dementia, for example, and the evaluation of problem-solving ability is positive (ie, the subject has a high risk of Alzheimer's dementia, memory impairment, and problem-handling disorder) Since it is judged that there is no memory capacity of the subject, but the ability of daily living behavior is not reduced, the reinforcement of the nursing care of the subject should not be so important.

The risk calculator 15 also includes safe driving ability evaluating means for evaluating the safe driving ability of the vehicle based on the problem handling ability within the time limit of the subject evaluated by the problem handling ability evaluating means. The evaluation of the test subject's safe driving ability is output to the result output unit 17. If the subject's problem handling ability is negative, the subject's safe driving ability is also negatively assessed. If the subject's problem handling ability is positive, the subject's safe driving ability is also positive. In Japan's auto licensing system, people with dementia are not allowed to drive a car, but in the case of MCI, it is not forbidden to drive a car. MCI, which is not dementia but is likely to be dementia in the cognitive decline, is likely to become dementia in the future, so only a temporary aptitude test is mandatory after a certain period of time. According to the cognitive function evaluation system of the present invention, if a subject is determined to have a high risk of Alzheimer's dementia, for example, driving of a vehicle should be avoided. If the subject is judged to have a high risk of MCI of Alzheimer's disease, for example, and the problem handling ability is negatively evaluated, the driving safety should be refrained from driving. If the test subject is judged to have a high risk of MCI of Alzheimer's disease, for example, and the problem handling ability is positive, there is little need to familiarize himself with driving a car.

(Flow of inspection)

Below, the flow of the test | inspection using the cognitive function evaluation system which concerns on this embodiment is demonstrated. Approximately 70 questionnaires are prepared in the cognitive function evaluation system according to the present embodiment. Profile information is entered by the inspector. Profile information includes residence, date of birth and gender. The subject faces the liquid crystal display, which is the screen display unit 12, and responds to various questionnaires prepared for evaluating cognitive function after responding to the condition on the day of the test, the sleep situation on the previous day, the recent subjective symptoms, the recent habits, and the like.

Here, the various questionnaires correspond to any of nine inspection items as described above. Nine test items are memory (X ₁ ), material formula (X ₂ ), seal (X ₃ ), seal (X ₄ ), calculation (X ₅ ), understanding (X ₆ ), judgment (X ₇ ), execution It consists of a function X ₈ and a correction term C, and the memory force X ₁ is further classified into an immediate memory X _1A and a recent memory X _1B . And X ₁ , X ₂ ,... X <_8> has 10 points | pieces and 20 points | pieces of C, and it is evaluated by 100 points in total. Here, the higher the score, the higher the cognitive function is maintained, the lower the risk of dementia, and the lower the score, the lower the cognitive function and the higher the risk of dementia.

Each item is explained. Memory is the ability to remember things. In this system, immediate and recent memories are measured. Immediate memory refers to the ability to remember a few seconds ago, for example, a questionnaire as shown in FIGS. 3A and 3B is asked. Recent memory is the ability to remember a few minutes ago. For example, immediately before the end of this test, a question as in FIG. 3C is asked.

The location formula refers to the basic situation grasping such as the current year, month, and time, and where he is. For example, the questionnaire of FIG. 4 is asked.

A written word means that the function for understanding and operating a word falls. Here, the actual word is examined based on the knowledge of the word. For example, a questionnaire as shown in FIG. 5 is asked.

The real seal indicates that the cognitive power through the five senses is lowered. For example, the questionnaire as shown in FIG. 6 is asked.

The computational power refers to the ability to perform arithmetic calculations and the like. For example, a questionnaire as illustrated in FIGS. 7A and 7B is asked.

Understanding ability refers to the ability to understand things, for example, a questionnaire as shown in FIGS. 8A and 8B is asked.

Judgment power refers to the ability to judge the situation, for example, the questionnaire as shown in FIG.

The execution function refers to a function of executing things in order. For example, the questionnaire is asked as shown in FIG. 10.

The correction term is an inspection item other than the above and, for example, it is asked whether there is awareness of dementia. This correction term will be described in detail in the processing of the calculation result correcting unit 22 described later.

In this way, when a subject answers about 70 questionnaires, the answers are scored, and scores (X _1A , X _1B , X ₂ ,... X ₈ , C) for each test item are calculated. And the risk of a basic disease is computed using the score (test value) for this calculated test item.

Here, when calculating the risk of the underlying disease, nine inclination coefficients A _1A , A _1B , A ₂ , ..., A _{8 for} each score (X _1A , X _1B , X ₂ , ... X ₈ ) other than the correction term It is calculated as a linear expression multiplied by When this is written in a general formula, it is represented by the following formula.

Y = A ₁ X ₁ ｛= A _1A X _1A + A _1B X _1B ｝ + A ₂ X ₂ + A ₃ X ₃ + A ₄ X ₄ + A ₅ X ₅ + A ₆ X ₆ + A ₇ X ₇ + A ₈ X ₈

Here, the combination of the inclination coefficients A _1A , A _1B , A ₂ ,..., A ₈ is different depending on each underlying disease, and the following formula is used as an initial value as a specific calculation formula.

_, Alzheimer's disease (AD)

Y = 3.7X ₁ (= 1.2X _1A + 2.5X _1B ) + 2.0X ₂ + 1.2X ₃ + 0.8X ₄ + 0.2X ₅ + 0.7X ₆ + 1.1X ₇ + 0.3X ₈

_· Cerebral vascular dementia (VaD)

Y = 1.6X ₁ (= 0.7X _1A + 0.9X _1B ) + 1.0X ₂ + 1.9X ₃ + 1.4X ₄ + 0.8X ₅ + 0.6X ₆ + 0.6X ₇ + 2.1X ₈

_· Louis Small Body Dementia (DLBD)

Y = 1.3X ₁ (= 0.5X _1A + 0.8X _1B ) + 1.4X ₂ + 1.1X ₃ + 1.6X ₄ + 0.5X ₅ + 0.9X ₆ + 1.2X ₇ + 2.0X ₈

Parkinson's disease dementia (PDD)

Y = 1.4X ₁ (= 0.5X _1A + 0.9X _1B ) + 1.3X ₂ + 1.1X ₃ + 1.6X ₄ + 0.5X ₅ + 0.9X ₆ + 1.2X ₇ + 2.0X ₈

Frontal temporal lobe dementia (FTD)

Y = 1.6X ₁ (= 0.7X _1A + 0.9X _1B ) + 1.0X ₂ + 1.1X ₃ + 1.5X ₄ + 0.5X ₅ + 1.1X ₆ + 1.2X ₇ + 2.0X ₈

Cortical basal nucleus degeneration

Y = 1.6X ₁ (= 0.5X _1A + 1.1X _1B ) + 1.0X ₂ + 1.5X ₃ + 0.9X ₄ + 0.5X ₅ + 0.9X ₆ + 1.2X ₇ + 2.4X ₈

Encephalitis (aftereffect)

Y = 1.4X ₁ (= 0.7X _1A + 0.7X _1B ) + 1.2X ₂ + 1.1X ₃ + 1.6X ₄ + 1.1X ₅ + 0.9X ₆ + 1.2X ₇ + 1.5X ₈

Metabolic encephalopathy

Y = 1.2X ₁ (= 0.5X _1A + 0.7X _1B ) + 1.2X ₂ + 1.1X ₃ + 1.6X ₄ + 1.0X ₅ + 1.1X ₆ + 1.4X ₇ + 1.4X ₈

Normal pressure hydrocephalus

Y = 1.2X ₁ (= 0.5X _1A + 0.7X _1B ) + 1.2X ₂ + 1.3X ₃ + 1.6X ₄ + 1.2X ₅ + 1.1X ₆ + 1.2X ₇ + 1.2X ₈

In other words, the risk is calculated using a different calculation formula for each underlying disease. The combination of the inclination coefficients A _1A , A _1B , A ₂ ,..., A ₈ as the initial values is determined by medical knowledge, but can be corrected by polyregression analysis as described later. The initial slope coefficient may be calculated by expressing how much each cognitive function is prone to damage by each underlying disease, and then dividing each coefficient by their sum, and then multiplying each by 10. In the multiple regression analysis, the independent variable may be any of binary, category, order, and numerical variables. The dependent variable can be either rational or ordered. The model may be a linear or nonlinear model. Further, the numerical variable may be logarithmic converted. The underlying disease is not necessarily limited to the above. This is because the same processing can be performed with the information.

And when the risk of a basic disease is calculated by the said process, correction | amendment of the result computed by the calculation result correction part 22 is performed using the test value C of a correction term. As the content of correction, for example, when a subject negatively inputs a question about the condition of the test day or the question of sleep situation on the day before the question is asked at the beginning of the test, a predetermined score is given as the correction term (C) to determine the underlying disease. Low risk This is because dementia patients often do not have a negative view of the sleeping situation or their condition. Depression or psychogenic reactions are likely to be negative. As a correction term, while giving a positive 2 point of negative viewpoint and a positive 1 point of negative viewpoint, it is preferable to express separately about these evaluation items.

In addition, when a subject positively inputs a questionnaire regarding dementia awareness to be asked at the beginning of the test, a predetermined score is given as a correction term (C) to lower the risk of the underlying disease. This is because most people with dementia have no awareness of dementia. Depression or neurosis is often accompanied by excessive anxiety. As a correction term, while giving a positive 2 point of negative viewpoint and a positive 1 point of negative viewpoint, it is preferable to express separately about these evaluation items.

In addition, when the subject has a long history of education in which the subject is inquired at the beginning of the examination, the inclination coefficient of the calculation force is increased as the correction term (C). This is because, in spite of the long training history, the deterioration of the computational power is considered to be a possibility of dementia, so that the influence of the computational power in the risk calculation is increased so that the dementia risk can be easily expressed. The long and short of the educational history can be defined by, for example, whether or not the subject graduated from university. Alternatively, it may be defined as the schooling training of elementary school, junior high school, high school, university, and graduate school. As the information used for the multivariate analysis, numerical information is superior as the information amount than the information gathered into categories. Since the influence of the educational history on the cognitive function varies depending on the cognitive function area or the questionnaire, correction according to each is preferable. For example, the educational history does not affect the cognitive function of recognizing an apple as an apple and the cognitive function of identifying an expression. The tilt coefficient of the cognitive function region can be corrected.

In addition, when the age of the subject is high, the inclination coefficient of the material formula is increased as the correction term (C). This is because the tendency for the material formula to decrease when the age is high is to increase the influence of the formula in the risk calculation so that the dementia risk can be easily displayed. For example, when it is 60 years old or more, it is possible to define that the subject's age is high. The age is preferably the actual age (numeric information). This is because the amount of information increases in the multivariate analysis. The tilt coefficient of the cognitive function region can be corrected. Since age also affects hearing, visual acuity, and operation speed, age information can be simultaneously acquired and corrected at the time of cognitive function evaluation. The inclination coefficient of each cognitive function region can be corrected.

In addition, it is possible to input "I do not know" to each questionnaire (for example, the right end of FIG. 3B), and when the rate at which the subject enters "I do not know" is a certain frequency or more, the correction term (C) is used. A predetermined score is assigned to lower the risk of underlying disease. This is because, if the rate at which "I don't know" is input is more than a certain frequency, other diseases such as depression and not dementia are suspected. The meaning of the choice and answer of "I do not know" was different. I chose "I do not know" because I chose it after understanding the meaning, not choosing another option, and understanding what would be the wrong answer. In the case of dementia, the "don't know" choice decreases, and as dementia progresses, the choice of "don't know" decreases. For altitude, the "don't know" option is gone. Thus, for example, if a low score is achieved because of a "don't know" choice, it is not appropriate to immediately determine it as the risk of dementia. Depending on the underlying disease, the frequency of selection of "not sure" is different. For example, in AD, the choice of “I don't know” is more than any other underlying disease because of the murky response. In FTD, the posture of responding to the test changes, so the frequency of choosing "Don't Know" is higher than in other dementia-based diseases. In NPH, the "not sure" selection frequency is low. As such, the "don't know" choice is an assessment of cognitive function, which helps in the differentiation of the underlying disease. The following combination of the "I don't know" choice for low and high difficulty problems is useful for the diagnosis and differentiation of dementia. For example, the risk of dementia is lowered if "I don't know" for low difficulty and "I don't" for high difficulty. In addition, if the answer is correct for the problem, consideration should be given to factors other than dementia, such as "depression" or psychogenic reactions. The "don't know" choice is useful for evaluating dementia risk, frequency, which questionnaire was selected, and the distribution. The presence or absence of the "don't know" choice can be displayed as a list. By multivariate analysis of the presence or absence of the "don't know" choice, the accuracy of the dementia risk determination can be improved.

In addition, in a questionnaire (for example, FIGS. 7A and 7B) in which a low difficulty item and a high difficulty item are prepared, the subject corrects when the low difficulty item (FIG. 7A) is incorrect but the high difficulty item (FIG. 7B) is correct. A predetermined score is given as the item (C) to lower the risk of the underlying disease. This is because the fact that the correct answer to the high difficulty item is not degraded and the incorrect answer of the low difficulty item is considered to be a simple mistake.

In addition, the subject's blink frequency is detected using the imaging unit 23 provided in the screen display unit 12, and when the blink rate is small, the predetermined score is subtracted as a correction term (C) to increase the risk of the underlying disease. Evaluate. This is because dementia patients tend to have less blinking frequency. Blink frequency detection means for detecting a blink frequency is, for example, if the blink frequency of the normal eye blink frequency at the time of B _U, test to B _T, the blinking frequency (B), B = B _U / and it can be detected by calculating B _T. You may compare the blinking frequency per unit time. Since the movement of the upper eyelid is larger than that of the lower eyelid, the movement of the upper eyelid may be detected and measured. The loss of corneal reflection and the shielding of the pupil may be evaluated. The decrease in the blink frequency varies depending on the dementia underlying disease. For example, in PDD the reduction is greater than in AD. Therefore, the frequency of blinking is also useful for differentiating the underlying disease. It also depends on the stage of dementia. The inclination coefficient of each cognitive function region can be corrected. It can also be calibrated using a nonlinear model.

In addition, the change of the facial expression of the test subject is detected using the imaging unit 23, and when the change of the facial expression is equal to or less than a predetermined criterion, a predetermined score is subtracted as the correction term C to evaluate the risk of the underlying disease high. This is because dementia patients tend to have less facial expression changes. The expression change is evaluated by grasping the eyelids, eyebrows, jaw, nose, nasolabial folds, and corners in 3D. The decrease in facial expressions varies depending on the dementia underlying disease. For example, in PDD and DLDB, the degree of reduction is greater than in AD. It also decreases in FTD and NPH. The extent of this reduction is inconsistent with the blink reduction. Since the expression change varies depending on the underlying disease, evaluation of the underlying disease is naturally necessary. Evaluation of facial expressions is also useful for differentiating underlying diseases. It also depends on the stage of dementia. The inclination coefficient of each cognitive function region can be corrected. Also in this correction, a nonlinear model can be used.

Thereafter, the risk selecting unit 16 selects the one having a high risk with respect to the calculation result of the risk of each basic disease corrected. The selection means is more than the basic disease reference value among the calculated risks for the underlying disease, but when there is a Y lower than the predetermined MCI (hard cognitive impairment) reference value determined to be the risk of MCI, the risk of the MCI of the basic disease is Select it. Specifically, memory (X ₁ ), material formula (X ₂ ), seal (X ₃ ), seal (X ₄ ), calculation (X ₅ ), understanding (X ₆ ), judgment (X ₇ ), execution function ( X ₈ ), and the inspection item consisting of the correction term (C), wherein X ₁ , X ₂ ,. X ₈ has 10 points each and C is 20 points, and the total score is 100 points, and if the basic disease reference value is 70 points and the MCI reference value is 80 points, the score obtained by the above calculation and correction is 69. A basic disease of less than or equal to a point is a basic disease having a high risk of dementia, and a basic disease of 70 to 79 points is displayed on the screen display unit 12 together with the calculation result, indicating that there is a risk of MCI. Alternatively, the calculated basic diseases may be displayed on the screen display unit 12 in the order of high risk (lowest score). At this time, while showing the possibility of dementia, it is possible to indicate which underlying disease is dementia. Judgment of the presence or absence of dementia is carried out by calculation result of thing which showed the highest risk. Specifically, when the base disease A 50 points, B 60 points, C 70 points, and DH 80 points are used, 50 points of A are employed, and the subject is determined to have a high possibility of dementia. In addition, it is determined that A is most likely as a basic disease, and then B is likely. As for the basic disease C, it is determined that the situation of MCI. As for the underlying diseases of DH, it is determined that the likelihood of dementia by these is low.

In addition, as a method for determining the risk of MCI of each basic disease, a subject corresponding to MCI is a risk (Y) or formula (1 ') calculated by the above-described formula (1) than a subject corresponding to a basic disease of dementia. Since the high score (that is, the score which does not correspond to dementia) is shown with respect to the risk (Y) computed by (b), the inclination coefficients (A _{1 to} A ₈ ) and the correction term ( It is also possible to multiply C) by a cutoff coefficient, respectively, and determine the risk of MCI of each basic disease. The cutoff coefficient can be set for each MCI of each basic disease in consideration of the characteristics of each basic disease, and can be set in the range of 0.5 to 0.9, for example. More specifically, the cutoff coefficient is 0.9 for Alzheimer's dementia (AD), the cutoff coefficient is 0.5 for cerebrovascular dementia (VaD), and the cutoff coefficient for Lewy body dementia (DLBD). The cutoff coefficient is 0.6 for Parkinson's disease dementia (PDD), the cutoff coefficient is 0.9 for prefrontal temporal dementia (FTD), and the cutoff coefficient is 0.8 for cortical basal nucleus degeneration. In the case of encephalitis (aftereffects), the cutoff coefficient may be 0.6, in the case of metabolic encephalopathy, 0.6, and in the case of normal pressure hydrocephalus, the cutoff coefficient may be 0.6.

At the same time, the result output unit 17 generates and outputs a radial chart with respect to the calculation result. In this radial chart, it is possible to compare and confirm the calculation result and the disease type of each basic disease. By doing so, the healthcare practitioner can visually easily assess the risk of cognitive function and underlying disease with respect to the test results of the subject. At the same time, the probability for each bottle type can be displayed, and quantitative judgment is also possible. Radar charts can be zoomed in while maintaining similarity. Judgment becomes easy when comparing with the radial chart of each bottle shape. You can also display the area, the sum of intercepts, and the sum of squares of the radar charts. The sum of the intercept values and the sum of squares of the intercept values with the radial chart for each bottle type may be displayed. The radial chart of the present invention is particularly useful for the initial determination of dementia. Since the modification of the radial chart differs for each basic disease, the basic disease can be estimated from the shape of the subject's radial chart. Even if the score is high, when the shape is similar to the radial chart of the underlying disease, it can be estimated that the underlying disease, or the initial stage or the reserve group of the basic disease.

In addition, in the present cognitive function evaluation system, the medical staff can change the questionnaire contents according to the subject. Specifically, a change operation is performed by using a questionnaire change unit 13 including a point change means capable of changing a point value of each check value and a reply time setting means capable of setting a reply time of each questionnaire. .

By changing the scoring, it is possible to reduce the number of problems or to reduce the scoring of problems with high difficulty. In addition, by setting the response time, it is possible to introduce a correction term for not being able to answer within a predetermined response time, and it is possible to prevent the test from progressing smoothly because the required time is too long. In addition, when there are many questions that are not answered by the time limit, one's willingness to answer is low and the risk of "depression" and "neuropathy" can be expected, not dementia.

In addition, the present cognitive function evaluation system includes a coefficient correction unit 21 for storing the risk calculation result of the test subject who has been examined and correcting the combination of the gradient coefficients by the multiple regression analysis. As a result, the accuracy of the combination of the inclination coefficients can be further improved, and the reliability of the cognitive function evaluation system can be improved.

In addition, in the present cognitive function evaluation system, the result can be stored for each basic disease, and a plurality of test results can be compared. Therefore, it is possible to predict the progress of each disease.

In addition, in this cognitive function evaluation system, the risk calculation result of the test subject who completed the test can be memorize | stored, and the test result of multiple times can be compared. Therefore, the progress of cognitive dysfunction can be quantitatively evaluated. From the evaluation results of a plurality of examples, the degree of progression by total or basic disease can be calculated as the gradient coefficient by multiple regression analysis. This makes it possible to predict the progression of the subject's cognitive dysfunction. Moreover, with respect to the prediction, the difference of the measured value of the cognitive function of the subject can be evaluated.

(effect)

As described above, in the cognitive function evaluation system according to the embodiment of the present invention, since each risk is calculated for each basic disease, the subject can evaluate which risk of which basic disease is high, and the diagnosis and subsequent diagnosis by a specialist Provide informative information about medical practices. Cognitive dysfunction is different for each underlying disease. Disability progression also varies by underlying disease. By this system, the progression prediction for each basic disease can be performed. In addition, it is possible to evaluate the effects of interventions on underlying diseases.

(Other Embodiments)

In addition, this invention is not limited to said each embodiment, For example, you may implement as a client-server system, and a client side can also be set as a tablet terminal, a smartphone, etc. For people with color blindness, the display color can be changed. Audio output is possible and the volume can be adjusted. In addition to finger operation, blink operation, foot tap (footswitch), and voice input are possible. The foot sensor and voice sensor can be connected to the measuring part by USB. For example, the selections may be sequentially highlighted on the screen, and the selections may be selected by transmitting a signal such as blinking when the selection place is highlighted. The signal may be transmitted by foot tap or voice.

As the correction term, the sex, the drinking amount, the smoking amount, and the like of the test subject may be adopted. By doing in this way, it becomes possible to remove the bias of cognitive function evaluation by these factors further. Also in these cases, the present invention can obtain the same effects as in the above embodiment. In addition, the subject's athletic, intellectual and social activities can be assessed. For example, you may evaluate by the walking time, the reading time, the club activity, and the small and small number of group meetings. In dementia, this activity decreases. Therefore, the risk of dementia increases if less. Since these activities are effective for the prevention of dementia, the information can be obtained and evaluated at the same time, which can be useful for the measures against dementia. Information may be obtained from a non-subject who knows the subject's situation. The subjective evaluation may be different from the evaluation obtained from the non-subject. By acquiring a plurality of cognitive function evaluations determined at noon, subjective symptoms, subject's own self-assessment, or objective evaluation at the same time, the accuracy of the evaluation based on only one information can be improved and corrected. Can be reviewed.

Example

Hereinafter, specific embodiments of the present invention will be described.

(Example 1)

Two subjects were examined using the cognitive function evaluation system of the present invention. And risk assessment was computed about each basic disease.

Table 1 shows the test values of each test item of test subjects A and B. Table 1 is the inspection value of the inspection item of Example 1 which used the cognitive function evaluation system.

Table 1

As can be seen from this table, subjects A and B both had a total score of 70 points. Here, in this cognitive function evaluation system, the criterion that 80 or more points are normal, 70-79 points are mild cognitive impairment (MCI), and 69 or less are dementia is used. Therefore, as a method of simply evaluating the total point as conventionally, the subjects A and B both have a test result of mild cognitive impairment of the same degree, and after that, no further information is obtained in the examination by a specialist.

However, in the present invention, the risk of the underlying disease is calculated based on the test value of the test item as described above. Table 2 shows the results of this calculation. Table 2 is a risk calculation result of the basic disease of Example 1 using the cognitive function evaluation system.

Table 2

As can be seen from this table, according to the present invention, subject A has a risk calculation result of Alzheimer's dementia (AD) of 54.2 points and 69 points or less, which is a criterion for dementia. That is, test subject A can evaluate that the risk of dementia which has Alzheimer-type dementia as a basic disease is high. Subject B has a risk calculation result of cerebrovascular dementia (VaD) of 66.7 points and is 69 or less, which is a criterion for dementia. Therefore, it can be evaluated that the risk of dementia that is based on cerebrovascular dementia is high. As described above, according to the present invention, it becomes possible to more accurately evaluate the risk of the underlying disease of the subject, which is not known by the conventional method.

In subject A, for example, AD 54.2 (likely dementia due to Alzheimer's disease), VaD 79.4 (not vascular dementia), DLBD 79.7 (not dementia due to Lewy body disease), PDD 79.6 (not PDD) ), FTD 80.7 (not dementia due to FTD), cortical hyponucleus degeneration 79.7 (not cortical basal degeneration), encephalopathy 81.2 (not dementia due to encephalopathy), metabolic encephalopathy 82.5 (not dementia due to metabolic encephalopathy) , Normal pressure hydrocephalus 82.5 (not dementia due to normal pressure hydrocephalus). You may rearrange and display in order from the highest possibility. In addition, the general formula in the case of this case is about subject A

Y = A _1A + A ₂ + 6A ₃ + 8A ₄ + 10A ₅ + 10A ₆ + 7A ₇ + 9A ₈ + C

About Subject B

Y = 2A _1B + 10A ₂ + 2A ₃ + 5A ₄ + 10A ₅ + 10A ₆ + 10A ₇ + 3A ₈ + C

Becomes

(Example 2)

Logistic regression analysis was performed using the coefficient correction unit 21 of the cognitive function evaluation system of the present invention, and the results are shown in Tables 3 to 5. Table 3-Table 5 are the preconditions of the logistic regression analysis of Example 2 using the cognitive function evaluation system. Here, the correct answer is 1 and the wrong answer is 0 for each term.

Table 3

Table 4

First, when the AD group is 1, the VaD group is 0, and the T is a positive item number, it can be confirmed from Table 3 and Table 4 that there is no significant difference between the two groups. Table 5 shows the results of the logistic regression analysis between the two groups. Exp (B) is the odds ratio, and the odds ratio for which AD is estimated in the itemized evaluation is the inverse. Here, the AD group may be 1 and the normal group may be 0 to be compared between groups.

The AD group may be classified more severely, and in this case, an ordered logistic regression model may be used instead of the binary logistic regression model.

In this way, by performing a logistic regression analysis on the basis of the accumulated data of the subject, it is possible to make accurate the combinations of the inclination coefficients A _1A , A _1B , A ₂ ,..., A ₈ when calculating the basic disease. The reliability of this system can be improved.

Table 5

(Example 3)

The radial chart created by the cognitive function evaluation system of this invention is shown in FIG.

Fig. 11A shows the subject (a) of the basic disease A (Alzheimer's disease). (B) belongs to the subject (I) of the basic disease B. (C) is the subject (C). (D) is the subject (D). (E) is the subject (e). (F) is subject (f).

(B) shows that it is different from basic disease A from a shape. (C) shows that the area of the radial chart is small and dementia, and the underlying disease is A because the shape is similar to (A). It can be seen that (D) has a small area of the radial chart and is dementia, and that the underlying disease is B because the shape is similar to (B).

From the shape of the radial chart (E), the underlying disease is A, but the degree is light because the area is larger than that of the subjects (a) and (c). (F) is a large area of the radar chart, a range of MCI and not dementia, but from the shape can be considered to be a higher risk of dementia due to basic disease B than dementia due to basic disease A in the future.

(Example 4)

Logistic regression analysis was performed using the coefficient correction unit 21 of the cognitive function evaluation system of the present invention, and the results are shown in FIG. Dashed line 1 shows the revelatory changes in underlying disease I by linear regression. The revealing trend of subjects belonging to basic disease I is predicted as dashed line 1. Solid lines 2 and 3 show linear regression of the revelation scores of subjects A and B belonging to the basic disease I, respectively. In the case of solid line 2, i.e., above dashed line 1, the prognosis of A indicates that it was better than expected, and in the meantime, if there was an intervention, it can be regarded as valid.

If the trend of B's revelation score is solid 3, that is, below dashed line 1, it indicates that B's prognosis is worse than expected, and if there is an intervention (B) in between, that (B) is a deteriorating factor. Can be considered. Thus, this is effective, for example, when investigating the effectiveness of a treatment. Compared with the overall prediction of dementia, this prediction is superior to the previous prediction because the prediction of the underlying disease is more accurate. Although linear regression is shown here as an example, curve regression may be used. Even when linear regression is used or curve regression is used, notches may be formed according to stages and the calculation equation may be divided (see FIG. 12B).

(Example 5)

The correction method of the factor which influences a cognitive function is shown concretely.

FIG. 13A illustrates the relationship between lifestyle, for example, the presence or absence of walking A, and cognitive function (other factors are corrected). It can be seen that the cognitive function of the walking group is as good as the "car" shown in the figure. At this time, logistic regression analysis is performed using A-group and no-group as independent factors and cognitive function as dependent factors. When the presence or absence of a walk is added to the adjustment factor, when this "car" becomes insignificant, it can be seen that the presence or absence of a walk becomes an intermediary factor in cognitive function evaluation. Therefore, it is not necessary to correct the cognitive function evaluation with or without walking. On the contrary, if the "difference" remains significant, it is considered that the part is not an intermediary factor, and thus correction of cognitive function evaluation is necessary. This "tea" is different for each dementia underlying disease.

Fig. 13B shows the relationship between lifestyle, for example, walking time A and cognitive function (other factors are corrected). The relationship between them draws an S-shaped curve as shown in the figure. In other words, as A increases, the cognitive function is improved, but the relationship is not linear. When the amount is less than a certain amount and when the amount of A is above a certain value, the cognitive function is not improved. Deterioration is also not seen. In this manner, it is preferable to consider the range of A in the correction. Specifically, for example, in Fig. 13B, when the differential coefficient of the curve is less than 0.2, it is preferable not to correct it. The remodeling in this figure also differs for each dementia underlying disease.

(Example 6)

An example of a test using an eye blink sensor is shown. In a neuromuscular disease represented by amyotrophic lateral sclerosis (ALS), movement of the limbs is limited, making speech impossible, and it is difficult to determine whether dementia is present. Subject is lying in bed but can communicate his wishes with blinking eyes. Since the visual and hearing impairments are rare, use of this system is possible. In addition, in the ALS, the eye ring muscles are less likely to become obstructive, and thus the blinking operation is possible. Do the same for problem presentation.

Since the answer is an option, one of the options can be highlighted. The subject blinks when the option to be selected is highlighted. The system detects the blink and the shift of emphasis stops once. Subsequently, when the subject determines that the option is okay, the selection is confirmed by blinking again. If the highlighting stops because of a wrong selection, by not blinking, it is considered a wrong choice and the highlighting moves to the next choice. In this way, answers are determined sequentially.

Since the time required varies depending on the condition, the cognitive function and the dementia evaluation according to the dementia-based disease are performed in the same manner as in the other embodiments, according to noon, distribution, and the like of selection. For example, even ALS may merge dementia. In this way, diagnosis of dementia can be made even in a subject in a lying state which was conventionally difficult.

As described above, the present invention is very useful in the field of dementia treatment. Specifically, according to the present invention, since dementia originally composed of a plurality of basic diseases can be divided and judged by the basic diseases, the following becomes possible.

The presence of dementia can be determined early. Since the inspection part does not need assistance, anyone can do it anywhere, and screening examination of dementia can be performed in public institutions such as city halls, waiting rooms of medical institutions, welfare facilities, and the like.

It may indicate a basic disease of dementia. Since treatment differs depending on the underlying disease, it is desirable to know the underlying disease of dementia when considering treatment.

It may indicate a condition to distinguish from dementia. For example, dementia may not be properly evaluated by "depression" or "neuropathy".

The prognosis of dementia can be predicted. Since the prognosis of dementia differs according to the dementia basic disease, it is preferable to consider it for each basic disease.

The treatment _and nursing effect on dementia can be shown. As described above, since treatment differs according to the dementia basic disease, and the effect also varies according to the dementia basic disease, cognitive function evaluation considering the dementia based disease is preferable.

In addition, cognitive function can be assessed by a person who is difficult to manipulate by hand. Specifically, it can also be carried out on a person lying down. There are people with dementia and people with normal cognitive function even when lying down.

1: computer
11: Survey Memory
12: screen display unit
13: questionnaire change
14: check value measurement unit
15: risk calculation
16: risk selection unit
17: result output unit
21: coefficient correction
22: calculation result correction unit
23: imaging unit

Claims (25)

As a cognitive function evaluation system for evaluating the risk of a plurality of basic diseases in a cognitive function of a subject based on a test item for each cognitive function,
Measuring means for measuring an inspection value of the inspection item;
Calculation means for calculating the risk based on the inspection value measured by the measurement means;
The cognitive function evaluation system characterized by including the selection means which selects the basic disease with a high risk from the said risk. The method of claim 1,
The basic disease is at least one of Alzheimer's dementia, cerebrovascular dementia, Lewy body dementia, Parkinson's dementia, frontal temporal lobe dementia, cortical hyponucleus degeneration, encephalitis (aftereffects), metabolic encephalopathy, or normal pressure hydrocephalus. Cognitive function evaluation system. The method according to claim 1 or 2,
The test item is any one or more of the memory, material expression, load, seal, calculation, understanding, judgment, and execution function. The method according to any one of claims 1 to 3,
A cognitive function evaluation system, characterized by further comprising a point change means for changing the point of the inspection value. The method according to any one of claims 1 to 4,
A cognitive function evaluation system, further comprising a response time setting means for setting the response time of the test item. The method according to any one of claims 1 to 5,
The calculating means has a cognitive function evaluation system, characterized in that it comprises a correction function for correcting the calculation formula of the risk by multivariate analysis. The method of claim 6,
The multivariate analysis is a cognitive function evaluation system, characterized in that the regression analysis. The method according to any one of claims 1 to 7,
The inspection item is composed of a high difficulty item and a low difficulty item,
A cognitive function evaluation system, wherein the subject corrects and evaluates the risk of the underlying disease when the low difficulty item is incorrect but the high difficulty item is correct. The method according to any one of claims 1 to 8,
And further comprising input means for inputting matters relating to the subject's physical condition,
A cognitive function evaluation system, wherein the subject corrects and evaluates the risk of the underlying disease when the subject negatively inputs the matter concerning the physical condition. The method according to any one of claims 1 to 9,
It further has an input means for inputting the matter regarding the subject's dementia awareness,
A cognitive function evaluation system, characterized by correcting and evaluating the risk of the underlying disease when the subject positively inputs the matter related to the dementia awareness. The method of claim 3, wherein
It further has an input means for inputting the matter regarding the education history of the subject,
Cognitive function evaluation system, characterized in that for increasing the training history of the subject, the coefficient of inclination of the computing power is increased. The method according to any one of claims 1 to 11,
The subject further has an input means for inputting unknown in the test item,
A cognitive function evaluation system for correcting and evaluating the risk of the underlying disease when the rate at which unknown is input is more than a certain frequency. The method according to any one of claims 1 to 12,
Further comprising a blink frequency detecting means for detecting a blink frequency of the subject,
Cognitive function evaluation system characterized in that the correction of the risk of the underlying disease when the blink frequency is low. The method according to any one of claims 1 to 13,
Further comprising a facial expression detecting means for detecting a change in the subject's facial expression,
A cognitive function evaluation system for correcting and evaluating the risk of the underlying disease when the expression change is less than or equal to a predetermined criterion. The method according to any one of claims 1 to 14,
A cognitive function evaluation system, characterized by further comprising display means for creating a radial chart based on the test value of the test item and displaying it in comparison with a predetermined radial disease-specific radial chart of the underlying disease. The method according to any one of claims 1 to 15,
A cognitive function evaluation system for performing a prognostic prediction for each underlying disease by recording a test value of the test item a plurality of times. The method according to any one of claims 1 to 16,
A cognitive function evaluation system, characterized by predicting the progression for each disease type of the underlying disease from the test result of the test item. The method according to any one of claims 1 to 17,
A cognitive function evaluation system, characterized by evaluating the progression in comparison with the prognostic prediction for each disease type of the underlying disease by recording the test value of the test item a plurality of times. As a cognitive function evaluation system for evaluating the risk of a plurality of basic diseases in a cognitive function of a subject based on a test item for each cognitive function,
The plurality of basic diseases include Alzheimer's dementia (AD), cerebrovascular dementia (VaD), Lewy body dementia (DLBD), Parkinson's disease dementia (PDD), frontal temporal lobe dementia (FTD), cortical hyponuclear degeneration and encephalitis ( Sequelae), metabolic encephalopathy, or normal pressure hydrocephalus,
The test item comprises a memory, a material formula, a load, a seal, a calculation, an understanding, a judgment, and an execution function,
X ₁ (memory test value), X ₂ (material test value), X ₃ (real test value), X ₄ (real test value), X ₅ (calculation test value), X ₆ (Understanding test), X ₇ (Decision test), and X ₈ (Performance test) with high scores if cognitive function is maintained and low scores if cognitive function is not maintained. With measurement means to say,
Inclination coefficient (A ₁ ,) for each basic disease, to each test value (X ₁ , X ₂ , X ₃ , X ₄ , X ₅ , X ₆ , X ₇ , and X ₈ ) measured by the measuring means. Equation (1) below, which calculates the risk (Y) of the underlying disease for each of the underlying diseases by adding up each value multiplied by A ₂ , A ₃ , A ₄ , A ₅ , A ₆ , A ₇ , and A ₈ ), respectively. Calculation means represented by
Y = A ₁ X ₁ + A ₂ X ₂ + A ₃ X ₃ + A ₄ X ₄ + A ₅ X ₅ + A ₆ X ₆ + A ₇ X ₇ + A ₈ X ₈ (1)
Selection means which selects the basic disease of Y as a basic disease with high risk, when there exists Y among the calculated Y for each basic disease, and there is Y lower than the predetermined basic disease reference value judged that there is a risk of a basic disease.
A cognitive function evaluation system comprising: a. The method of claim 19,
A radial chart is created based on each test value (X ₁ , X ₂ , X ₃ , X ₄ , X ₅ , X ₆ , X ₇ , and X ₈ ) of each test item, Display means for displaying against the radar chart
A cognitive function evaluation system comprising: a. The method of claim 19 or 20,
The test item comprises a memory, a material formula, a load, a seal, a calculation, an understanding, a judgment, an execution function, and a correction term,
The calculation means is inclined for each basic disease to each test value X ₁ , X ₂ , X ₃ , X ₄ , X ₅ , X ₆ , X ₇ , and X ₈ measured by the measuring means. The sum of each value multiplied by the coefficients (A ₁ , A ₂ , A ₃ , A ₄ , A ₅ , A ₆ , A ₇ , and A ₈ ), and the basis of the addition of C (correction term check) Formula (1 ') which calculates risk (Y) of disease for every basic disease
_{Y = A 1 X 1 + A} 2 X 2 + A 3 X 3 + A 4 X 4 + A 5 X 5 + A 6 X 6 + A 7 X 7 + A 8 X 8 + C ··· (1 ')
A cognitive function evaluation system comprising: a. The method of claim 19 or 20,
The selection means selects that there is a risk of MCI of the underlying disease when there is a Y that is higher than the basic disease reference value but lower than a predetermined MCI reference value determined to be the risk of MCI among the calculated Y for each basic disease. A cognitive function evaluation system, characterized in that. The method according to any one of claims 19 to 22,
A cognitive function evaluation system, further comprising a response time setting means for setting the response time of each test item, respectively. The method of claim 23,
Noon recording means in the response time for recording whether or not the answer is correct within the response time in each of the inspection items;
Problem processing ability evaluation means for evaluating the problem processing ability within the time limit for the test of the subject based on the number of test items corrected within the response time.
A cognitive function evaluation system comprising: a. The method of claim 24,
Safe driving ability evaluating means for evaluating the safe driving ability of the vehicle on the basis of the problem handling ability within the time limit of the subject evaluated by the problem handling ability evaluating means.
A cognitive function evaluation system comprising: a.

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