JP6973802B2 - Cognitive function evaluation system - Google Patents

Description

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

With the aging of the population in recent years, the number of patients with dementia is increasing rapidly. While it is difficult to completely cure dementia once it develops, early treatment can slow the progression or alleviate the symptoms, so early detection is important. However, the number of specialists who diagnose dementia has not increased so much, and there is a demand for diagnostic support tools that can easily evaluate cognitive function.

Conventionally, for example, a diagnostic support tool as disclosed in Document 1 has been developed. With this diagnostic support tool, it is possible to dynamically change the questions according to the subject's prior information, environmental information, and the subject's response content, and evaluate the subject's cognitive function.

Japanese Unexamined Patent Publication No. 2007-282922

However, dementia is caused by various underlying diseases such as Alzheimer-type dementia and cerebrovascular dementia, and the symptoms, prognosis, response, and treatment method differ depending on the underlying diseases that cause these.

Nevertheless, diagnostic support tools such as those disclosed in Patent Document 1 merely evaluate the extent to which cognitive function functions as a whole, and risk of such various underlying diseases. Since it has not been possible to evaluate (which underlying disease is most likely to be affected), the current situation is that specialists cannot provide meaningful information for diagnosis. In addition, dementia has a progressive course, the degree of progression of which depends on the underlying disorder. Since the same evaluation is performed for the entire dementia with conventional tools, it is not possible to predict the progression of each underlying disease. Therefore, even when some kind of intervention, for example, the effect of a drug is verified, it is not possible to evaluate by underlying disease.

The present invention has been made in view of such circumstances, and an object of the present invention is to provide a diagnostic tool capable of evaluating the risk of each underlying disease of dementia when examining the cognitive function of a subject. To do. In addition, it provides a progression prediction for each underlying disease. Further, in the present invention, it can be carried out even if the person is physically handicapped.

The cognitive function evaluation system of the present invention evaluates the risk of a plurality of underlying diseases in the cognitive function of a subject based on a test item for the cognitive function, and measures the test value of the test item, and the measurement. It is characterized by comprising a calculation means for calculating the risk based on the test value measured by the means and a selection means for selecting the underlying disease having the highest risk from the calculated risks. Factors that affect cognitive function evaluation are also evaluated at the same time to improve the accuracy of judgment. You can answer with the upper limbs (fingers), lower limbs, vocalizations, or blinking eyes.

According to the present invention, it is possible to calculate the risk for each underlying disease that causes dementia, and it is possible to more effectively support the diagnosis of dementia.

It is a perspective view of the computer which implemented the cognitive function evaluation system which concerns on embodiment of this invention. It is a functional block diagram of a cognitive function evaluation system. This is an example of a question in a cognitive function evaluation system. This is an example of a question in a cognitive function evaluation system. This is an example of a question in a cognitive function evaluation system. This is an example of a question in a cognitive function evaluation system. This is an example of a question in a cognitive function evaluation system. This is an example of a question in a cognitive function evaluation system. This is an example of a question in a cognitive function evaluation system. This is an example of a question in a cognitive function evaluation system. This is an example of a radar chart output by the cognitive function evaluation system. It is an example of logistic regression analysis in an example. It is an example of the handling of the correction term in an Example.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. It should be noted that the following description of the embodiment is essentially merely an example, and is not intended to limit the present invention, its application, or its use.

(Embodiment)
An embodiment of the present invention is a cognitive function evaluation system implemented in a computer that does not require a communication line and functions as a so-called stand-alone type. FIG. 1 schematically shows a computer 1 on which the cognitive function evaluation system of the present embodiment is implemented. The cognitive function evaluation system as an embodiment of the present invention is realized by a combination of the hardware of the computer 1 and the software executed inside the computer 1.

The computer is equipped with a main body containing a CPU, RAM memory, ROM, hard disk, etc., a liquid crystal display for displaying a screen, and a keyboard and a mouse for a medical worker to input various settings and the like. Then, when the cognitive function evaluation system is installed as software in the hard disk of the computer 1 and the installed cognitive function evaluation system is activated, the computer 1 functions as the cognitive function evaluation system of the present embodiment.

FIG. 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 question storage unit 11, a screen display unit 12, a question change unit 13, a test value measurement unit 14, a risk calculation unit 15, a risk selection unit 16, and a result output unit 17. It is composed of.

The question storage unit 11 stores the contents of about 70 questions executed by the cognitive function evaluation system 10, the points assigned to each question, the answering time, and the inspection items corresponding to each question. Details will be described later, but the inspection items consist of nine items: memory, orientation, aphasia, agnosia, calculation, comprehension, judgment, executive function, and correction items, and each question is one of these inspection items. Corresponds to any of.

The screen display unit 12 is composed of a liquid crystal display, and displays each question to the subject. The subject will answer the questions displayed on the screen display unit 12 in sequence using the touch panel, keyboard, foot sensor, blink sensor or mouse. The screen display unit 12 is provided with an imaging unit 23, and changes in the frequency of blinks and changes in facial expressions during the examination of the subject are detected.

The question change unit 13 is a function that allows the medical staff to change the question contents according to the subject. Specifically, it is provided with a score changing means that can change the score of each question and a response time setting means that can set the answer time of each question.

The inspection value measuring unit 14 as a measuring means determines whether the answer to each question of the subject is correct or incorrect, and scores based on the points assigned to each question. Then, the score for each of the above-mentioned inspection items is measured as an inspection value.

The risk calculation unit 15 as a 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 this embodiment, as underlying diseases, Alzheimer-type dementia (AD), cerebrovascular dementia (VAD), Lewy body dementia (DLBD), Parkinson dementia complex (PDD), frontotemporal dementia ( FTD), cortical basal nucleus dementia, encephalitis (aftereffects), metabolic encephalopathy, and normal pressure hydrocephalus are targeted for risk calculation. In addition, the degree to which diseases and pathological conditions that should be differentiated from dementia, specifically depression, psychogenic reactions, neurosis, etc., affect cognitive function is calculated, and the accuracy of risk calculation of underlying diseases due to dementia is calculated. Increase.

The details will be described later, but the risk of each underlying disease is the risk of the underlying disease: X ₁ = A ₁ × (memory test value: X ₁ ) + A ₂ × (agnosia test value: X ₂ ) + A ₃ × ( Agnosia test value: X ₃ ) + A ₄ x (Agnosia test value: X ₄ ) + A ₅ x (Computational power test value: X ₅ ) + A ₆ x (Comprehension test value: X ₆ ) + A ₇ x (Inspection value of judgment: X ₇ ) + A ₈ x (Inspection value of execution function: X ₈ ) + (Inspection value of correction item: C), and so on, with the inspection value of each inspection item and A _{1 to} A ₈ . It is calculated by a linear equation with the expressed slope coefficient (for memory, A ₁ is _{treated as A 1A} , A _1B , and X ₁ is treated as _{X 1A} , X _1B). The combination of slope coefficients represented by _{(A 1} , A ₂ , ..., A ₈ ) differs depending on each underlying disease. The risk calculation unit 15 calculates the risk of the underlying disease by using the combination of the slope coefficients corresponding to each underlying disease.

When the correction term is not taken into consideration, the calculation means calculates the risk Y of the underlying disease for each underlying disease by the formula represented by the 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 the correction term is taken into consideration, the calculation means calculates the risk Y of the underlying disease for each underlying disease by the formula represented by the 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')

Although the details will be described later, the risk calculation unit 15 accumulates the test results of the subjects and corrects the combination of the slope coefficients of each underlying disease by multivariate analysis based on the accumulated data. I have. As the multivariate analysis, for example, a generally well-known logistic regression analysis can be used.

Further, the risk calculation unit 15 includes a calculation result correction unit 22 that corrects the calculated risk of each underlying disease. This is corrected so that, for example, if the subject answers the low difficulty item incorrectly but answers the high difficulty item correctly, the wrong answer of the low difficulty item is judged to be a mere error and the risk of the underlying disease is evaluated low. Or, if the subject makes a negative input to a question about their physical condition, the risk of the underlying illness is corrected to be underestimated.

In the risk selection unit 16 as a selection means, among the risks for each underlying disease calculated by the risk calculation unit 15, there is a Y that is lower than the predetermined underlying disease standard value that is determined to have the risk of the underlying disease. If so, the underlying disease is selected and displayed on the screen display unit 12. That is, from the calculated risks of the underlying disease, the ones with high risk for the subject are displayed. Alternatively, all the calculated underlying diseases can be displayed in order from the one with the highest risk.

The risk selection unit 16 as a selection means is Y, which is equal to or higher than the underlying disease standard value among the calculated risks for each underlying disease, but is lower than the predetermined MCI standard value determined to be at risk of MCI. If there is, select it as being at risk of MCI for the underlying disease.

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

Further, the risk calculation unit 15 is provided with a correct / incorrect recording means within the answering time for recording whether or not the correct answer was made within the answering time for each inspection item. If the answer is incorrect even if the answer is made within the answer time for each test item, it does not mean that the answer was correct within the answer time. Further, the risk calculation unit 15 is provided with a problem processing ability evaluation means for evaluating the problem processing ability within the time limit for the examination of the subject based on the number of inspection items that can be answered correctly within the response time. The evaluation of the problem processing ability of the subject is output to the result output unit 17.

Specifically, the response time for each test item is the response time for the memory test T _{1 , the response time for the orientation test T 2} , the response time for the disorientation test _{T 3} , and the response time for the disapproval test T _4. , computing power test answer time T _5, the response times T ₆ of comprehension _test, the judgment test response times T _7, and is set as a reply time T ₈ of the examination of the execution function. Then, for example, if the correct answer was given within the answering time in the memory test, the aphasia test, the comprehension test, and the executive function test, but the correct answer was not given within the answering time in the other tests. The correct / incorrect recording means within the response time records this.

The problem-handling ability evaluation means evaluates the problem-handling ability of the subject in comparison with a predetermined standard. For example, if four or less of the eight test items of memory, orientation, aphasia, agnosia, calculation, comprehension, judgment, and executive function cannot be answered correctly within the response time, the subject's problem. Processing power is evaluated negatively. In addition, for example, among the eight test items of memory, orientation, aphasia, agnosia, calculation, comprehension, judgment, and executive function, the test items that can be answered in a short time are particularly emphasized in the problem processing of the subject. You can also evaluate your abilities. For example, agnosia is a question that can be answered in a short time, although the question shown in Fig. 6 is asked, but the calculation power is a question as shown in FIGS. 7A and 7B, and the answer is short. It's not a problem that can be done.

The cognitive function evaluation system according to the present invention can diagnose the risk of dementia for each underlying disease of dementia. For example, according to the cognitive function evaluation system according to the present invention, Alzheimer-type cognition is given to a subject. Although it can be evaluated as corresponding to the disease, the pathological condition of Alzheimer-type dementia varies. According to the cognitive function evaluation system according to the present invention, since the problem handling ability evaluation means is provided, not only the underlying disease of dementia can be determined, but also the pathological condition can be evaluated. For example, Alzheimer-type dementia is a disease based on exacerbation of memory impairment, and its pathological conditions include intellectual disability, learning disability, attention disorder, impaired visuospatial cognition, and problem-handling ability disorder in addition to memory disorder. And so on. For example, some people who are judged to have Alzheimer's disease have memory impairment, disorientation, and problem-handling ability, while others have memory impairment, disorientation, and problem-handling. Some people are judged to have disorientation.

According to the cognitive function evaluation system of the present invention, when the subject is judged to have a high risk of, for example, Alzheimer's dementia, and the evaluation of the problem handling ability is negatively evaluated (that is, this subject). Was judged to have a high risk of Alzheimer's disease, memory impairment, and problem-handling ability impairment.) The care of people should be strengthened. If the subject is determined to be at high risk of, for example, Alzheimer's disease, and the evaluation of problem-solving ability is positively evaluated (ie, this subject is at high risk of Alzheimer's disease and has memory). It was judged that there was a disability and there was no problem-handling ability disorder.) Since the subject's memory ability was reduced but his daily life behavioral ability was not reduced, the subject's care was strengthened. It shouldn't be too important.

Further, the risk calculation unit 15 includes a safe driving ability evaluation 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 evaluation means. The evaluation of the safe driving ability of the subject is output to the result output unit 17. If the subject's problem-handling ability is negatively evaluated, the subject's safe driving ability is also negatively evaluated. If the subject's problem-handling ability is positively evaluated, the subject's safe driving ability is also positively evaluated. Japan's automobile license system prohibits people with dementia from driving a car, but MCI (mild cognitive impairment) does not prohibit driving a car. In the case of MCI, which is not dementia but has a decline in cognitive function and may develop dementia in the future, it is possible that dementia will occur thereafter, so an extraordinary aptitude test is only required after a certain period of time. .. According to the cognitive function evaluation system of the present invention, driving a car should be avoided if the subject is judged to be at high risk of, for example, Alzheimer's disease. If the subject is judged to be at high risk of MCI for Alzheimer's disease, for example, and the problem-solving ability is negatively evaluated, the safe driving ability is also negatively evaluated. That should be avoided. If the subject is judged to be at high risk of MCI for Alzheimer's disease, for example, and the problem-solving ability is positively evaluated, the safe driving ability is also positively evaluated. There is little need to do.

(Inspection flow)
The flow of the examination using the cognitive function evaluation system according to this embodiment will be described below. Approximately 70 questions are prepared in the cognitive function evaluation system according to this embodiment. The profile information is entered by the inspector. Profile information includes place of residence, date of birth, gender, and the like. The subject asked various questions prepared to evaluate cognitive function after answering the physical condition on the day of the examination, the sleep condition on the previous day, recent subjective symptoms, recent habits, etc. toward the liquid crystal display which is the screen display unit 12. Will be answered.

Here, as described above, the various questions correspond to any of the nine inspection items. The nine test items are memory (X ₁ ), orientation (X ₂ ), misrepresentation (X ₃ ), agnosia (X ₄ ), calculation (X ₅ ), comprehension (X ₆ ), and judgment (X 6). _It consists of 7), executive function (X ₈ ), and correction term (C), and memory (X ₁ ) is further classified into immediate memory (X _1A ) and recent memory (X _1B). Then, X _1, X ₂ , ... X ₈ have 10 points each and 20 points for C, and are evaluated with a total of 100 points. Here, it is evaluated that the higher the score, the higher the cognitive function is maintained and the risk of dementia is low, and the lower the score, the lower the cognitive function and the higher the risk of dementia.

Each item will be described. Memory is the ability to memorize things, and this system measures immediate memory and recent memory. Immediate memory is the ability to memorize a few seconds ago, and questions such as those in FIGS. 3A and 3B are asked. Recent memory is the ability to memorize a few minutes ago, and for example, just before the end of this examination, the question shown in FIG. 3C is asked.

Orientation is a basic understanding of the situation, such as the current year, month, time, and where you are. For example, the question shown in FIG. 4 is asked.

Aphasia refers to diminished ability to understand and manipulate words. Here, aphasia is inspected based on the knowledge of words, and for example, the question shown in FIG. 5 is asked.

Agnosia means that the cognitive ability through the five senses is reduced, and for example, the question shown in FIG. 6 is asked.

Computational power is the ability to perform calculations such as four arithmetic operations, and questions such as those in FIGS. 7A and 7B are asked.

Comprehension is the ability to understand things, and questions such as those in FIGS. 8A and 8B are asked.

Judgment is the ability to judge a situation, and questions such as those shown in FIG. 9 are asked.

Executive function is the ability to execute things in order. For example, the question shown in FIG. 10 will be asked.

The correction term is a test item other than the above, and for example, it is asked whether or not there is awareness of dementia. This correction term will be described in detail in the processing of the calculation result correction unit 22 described later.

In this way, when the subject answers about 70 questions, the answers are graded and the scores for each test item (X _1A , X _1B , X ₂ , ... X ₈ , C) are calculated. .. Then, the risk of the underlying disease is calculated using the score (test value) for each of the calculated test items.

Here, when calculating the risk of the underlying disease, nine slope coefficients (A _1A, A _1B, A _{) for each score (X 1A} , X _1B , X ₂ , ... X _{8) other than the correction term. 2,} ..., it is calculated as a linear equation obtained by multiplying the a _8). If this is written in a general formula, it is expressed 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 slope coefficients (A _1A, A _1B, A ₂ , ..., A ₈ ) differs 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

・ 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

・ Lewy body dementias (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 dementia complex (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

・ Frontotemporal 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

・ Corticobasal 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 (sequelae)
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 formula for each underlying disease. The combination of the slope coefficients (A _1A, A _1B, A ₂ , ..., A ₈ ) as the initial value is determined by medical knowledge, but can be corrected by multiple regression analysis as described later. It is possible. The initial slope coefficient can be calculated by expressing how easily each cognitive function is impaired in each underlying disease, then dividing each coefficient by the sum of them, and then multiplying each by 10. In multiple regression analysis, the independent variable may be binary, category, order, or numerical variable. The dependent variable may be binary or ordered. The model may be a linear model or a non-linear model. Further, the numerical variable may be subjected to logarithmic conversion. The underlying disease is not necessarily limited to the above. This is because the same processing can be performed if there is information.

Then, when the risk of the underlying disease is calculated by the above processing, the result calculated by the calculation result correction unit 22 is corrected by using the test value C of the correction term. As for the content of the correction, for example, if the subject makes a negative input to the question about the physical condition on the day of the test and the sleep condition on the previous day, which is asked at the beginning of the test, a predetermined score is given as the correction item C to determine the risk of the underlying disease. I rate it low. This is because people with dementia often do not have a negative view of their sleep status or their physical condition. Depression and psychogenic reactions tend to be negative. As a correction term, it is desirable to give a plus 2 points for a negative opinion and a plus 1 point for a slightly negative opinion, and to describe these evaluation items separately.

In addition, when the subject makes a positive input to the question regarding the awareness of dementia, which is asked at the beginning of the examination, a predetermined score is given as the correction item C to evaluate the risk of the underlying disease low. This is because most people with dementia are unaware of dementia. Depression and neurosis tend to be accompanied by excessive anxiety. As a correction term, it is desirable to give a plus 2 points for a negative opinion and a plus 1 point for a slightly negative opinion, and to describe these evaluation items separately.

Further, when the subject has a long educational history of the subject who is asked at the beginning of the examination, the slope coefficient of the calculation power is increased as the correction term C. This is because there is a possibility of dementia that the calculation power is reduced despite the long education history, so the influence of the calculation power in the risk calculation is increased to make it easier to expose the risk of dementia. Because. The length of the educational history can be defined, for example, by whether or not the subject has graduated from university. Alternatively, it can be defined as the number of years of schooling in elementary school, junior high school, high school, university, and graduate school. As the information used for multivariate analysis, numerical information is superior in terms of the amount of information to the information summarized in categories. The effect of educational history on cognitive function differs depending on the cognitive function area and questions, so it is desirable to make corrections according to each. For example, educational history does not affect the cognitive function of recognizing an apple as an apple and the cognitive function of identifying facial expressions. The slope coefficient of the cognitive function area can be corrected.

Further, when the subject is old, the inclination coefficient of orientation is increased as the correction term C. This is because orientation tends to decline with age, and the influence of orientation on risk calculation is increased to make it easier to expose the risk of dementia. For example, in the case of 60 years or older, it can be defined that the subject is older. The actual age (numerical information) is desirable. This is because the amount of information increases in multivariate analysis. The slope coefficient of the cognitive function area can be corrected. Since age also affects hearing, visual acuity, and movement speed, age information can be acquired and corrected at the same time when cognitive function is evaluated. The slope coefficient of each cognitive functional area can be corrected.

In addition, it is possible to enter "I don't know" in each question (for example, at the right end of Fig. 3B), and if the percentage of subjects entering "I don't know" is more than a certain frequency, it is specified as correction item C. Give a score of to underestimate the risk of underlying disease. This is because if the percentage of "I don't know" is entered more than a certain frequency, other diseases such as depression are suspected instead of dementia. The meaning is different from not being able to select and answer "I don't know." The reason for choosing "I don't know" is to understand the meaning, not to choose another option, and to understand that it will be a wrong answer before choosing it. In dementia, the choice of "don't know" is reduced, and as the dementia progresses, the choice of "don't know" is further reduced. At altitude, there is no "I don't know" choice. Therefore, if a low score is obtained, for example due to an "unknown" choice, it is not appropriate to immediately determine it as a risk of dementia. The frequency of selecting "I don't know" differs depending on the underlying disease. For example, in AD, there is a repair reaction, so there are more "don't know" choices than in other underlying diseases. In FTD, the attitude of approaching the test changes, so the frequency of selecting "I don't know" is higher than that of other underlying dementia diseases. In NPH, the frequency of "don't know" selections is low. In this way, the "don't know" selection is a cognitive function evaluation that is useful for differentiating the underlying disease. The following combination of low-difficulty and high-difficulty "don't know" choices is effective in diagnosing and differentiating dementia. For example, if "I don't know" is selected for a low-difficulty problem and "I don't know" is not selected for a high-difficulty problem, the risk of dementia is low. Furthermore, if the answer is correct for a high-difficulty question, factors other than dementia, namely "depression" and psychogenic reactions, should be considered. The "don't know" selection, frequency, which question was selected, and distribution are useful in dementia risk assessment. Whether or not "I don't know" is selected can be displayed in a list. Multivariate analysis of the presence or absence of "don't know" selection can improve the accuracy of dementia risk determination.

Furthermore, in a question (for example, FIGS. 7A and 7B) in which a low difficulty item and a high difficulty item are prepared, the subject answered the low difficulty item (Fig. 7A) incorrectly, but the high difficulty item (Fig. 7A). If 7B) is answered correctly, a predetermined score is given as the correction item C, and the risk of the underlying disease is evaluated low. This is because the correct answer to the high difficulty item does not reduce the calculation power, and the wrong answer to the low difficulty item is considered to be a mere error.

Further, the imaging unit 23 provided on the screen display unit 12 is used to detect the blink frequency of the subject, and when the blink frequency is low, the predetermined score is reduced as the correction term C to highly evaluate the risk of the underlying disease. .. This is because patients with dementia tend to blink less frequently. Blink frequency detecting means for detecting the blink frequency, for example, B blink frequency during normal _U, When B _T blink frequency during the test, the blinking frequency B, B = B _U / B _T Can be calculated and detected by You may compare the frequency of blinks per unit time. Since the movement of the upper eyelid is larger than the movement of the lower eyelid, the movement of the upper eyelid can be detected and measured. The disappearance of the corneal reflex and the occlusion of the pupil may be evaluated. The decrease in blink frequency depends on the underlying disease of dementia. For example, in PDD, the degree of decrease is larger than that in AD. Therefore, the frequency of blinks is also useful for differentiating the underlying disease. It also depends on the stage of dementia. The slope coefficient of each cognitive functional area can be corrected. Further, correction can be performed using a non-linear model.

Further, the image pickup unit 23 is used to detect a change in the facial expression of the subject, and when the change in the facial expression is equal to or less than a predetermined reference, a predetermined score is reduced as a correction term C to highly evaluate the risk of the underlying disease. This is because patients with dementia tend to have less changes in facial expressions. Changes in facial expressions are evaluated by capturing the eyelids, eyebrows, chin, nose, nasolabial fold, and corners of the mouth in 3D. The decrease in facial expression changes depends on the underlying disease of dementia. For example, in PDD and DLDB, the degree of decrease is larger than that in AD. It also decreases in FTD and NPH. The degree of this decrease does not match the above-mentioned blink reduction. Since the change in facial expression differs depending on the underlying disease, it is naturally necessary to evaluate it according to the underlying disease. The evaluation of facial expressions is also useful for differentiating underlying diseases. It also depends on the stage of dementia. The slope coefficient of each cognitive functional area can be corrected. A nonlinear model can also be used for this correction.

After that, the risk selection unit 16 selects a high-risk one with respect to the corrected calculation result of the risk of each underlying disease. Among the calculated risks for each underlying disease, the selection method has a Y that is higher than the underlying disease standard value but lower than the predetermined MCI (mild cognitive impairment) standard value that is judged to be at risk of MCI. If so, select as having a risk of MCI for the underlying disease. Specifically, memory (X ₁ ), discretion (X ₂ ), aphasia (X ₃ ), agnosia (X ₄ ), calculation (X ₅ ), comprehension (X ₆ ), judgment (X _7). ), Execution function (X ₈ ), and correction item (C), X _1, X ₂ , ... X ₈ has 10 points each and C has 20 points, for a total of 100 points. When the score is evaluated on a perfect scale, the underlying disease standard value is 70 points, and the MCI standard value is 80 points, the underlying disease with a score of 69 points or less obtained by the above formula and correction is regarded as an underlying disease with a high risk of dementia. The underlying diseases of 70 to 79 points are displayed on the screen display unit 12 together with the calculation results, assuming that there is a risk of MCI. Alternatively, all the calculated underlying diseases may be displayed on the screen display unit 12 in descending order of risk (lowest score). At this time, it is possible to show the possibility of dementia and also show which underlying disease is the cause of dementia. The presence or absence of dementia is determined by the calculation result showing the highest risk. Specifically, when the underlying disease A is 50 points, B is 60 points, C is 70 points, and DH is 80 points, 50 points of A is adopted, and it is judged that this subject is highly likely to have dementia. In addition, it is determined that the possibility of A is the highest as the underlying disease, followed by the possibility of B. The underlying disease C is determined to be the status of MCI. Regarding the underlying diseases of DH, it is judged that the possibility of dementia due to them is low.

Further, as a method for determining the risk of MCI of each underlying disease, the subject corresponding to MCI has a risk Y or formula given by the above formula (1) more than the subject corresponding to the underlying disease of dementia. In order to show a high score (that is, a score that does not correspond to dementia) for the risk Y given in (1'), the gradient coefficients A _{1 to} A _{8 of the equations (1) and (1') are shown.} And the correction term C can be multiplied by the cutoff coefficient to determine the risk of MCI for each underlying disease. The cutoff coefficient can be set for each MCI of each underlying disease in consideration of the characteristics of each underlying disease, and can be set, for example, in the range of 0.5 to 0.9. More specifically, in the case of Alzheimer's dementia (AD), the cutoff coefficient is 0.9, and in the case of cerebrovascular dementia (VaD), the cutoff coefficient is 0.5, and Lewy body dementia. In the case of (DLBD), the cutoff coefficient is 0.6, in the case of Parkinson dementia complex (PDD), the cutoff coefficient is 0.6, and in the case of frontotemporal dementia (FTD), the cutoff coefficient is set. The cutoff coefficient is 0.8 for cortical basal nucleus dementia, 0.6 for encephalitis (sequel), and 0 for metabolic encephalopathy. In the case of normal pressure hydrocephalus, the cutoff coefficient can be set to 0.6.

At the same time, the result output unit 17 creates and outputs a radar chart for the calculation result. Here, in this radar chart, the calculation result and the type of each underlying disease can be compared and confirmed. By doing so, the medical staff can visually and easily evaluate the cognitive function and the risk of the underlying disease based on the test results of the subject. At the same time, the probability of each disease type can be displayed, and quantitative judgment can be made. Radar charts can also be magnified while preserving similarity. Judgment becomes easier when compared with the radar chart of each disease type. It is also possible to display the area of the radar chart, the sum of intercept values, and the sum of squares of intercept values. It is also possible to display the intercept difference sum and the intercept value square sum with each disease type radar chart. The radar chart in the present invention is useful for determining dementia particularly in the early stage. Since the outline of the radar chart differs depending on the underlying disease, the underlying disease can be estimated from the shape of the radar chart of the subject. Even if the score is high, when the shape resembles the radar chart of the underlying disease, it can be estimated that the underlying disease is, is in the early stage, or is a reserve force of the underlying disease.

In this cognitive function evaluation system, the medical staff can change the question contents according to the subject. Specifically, the question changing unit 13 including the score changing means capable of changing the score of each inspection value and the answering time setting means capable of setting the answering time of each question is used to perform the changing work.

By changing the score, it is possible to reduce the number of questions and lower the score of difficult questions. In addition, by setting the response time, it is possible to introduce as a correction item that the response was not possible within the predetermined response time, and it is possible to prevent the required time from becoming too long and the inspection not proceeding smoothly. can. Furthermore, if there are many questions that are not answered due to the time limit, the person's willingness to answer is low, and it is possible to assume the risk of "depression" and "neurosis" instead of dementia.

Further, the cognitive function evaluation system is provided with a coefficient correction unit 21 that stores the risk calculation result of the subject who has completed the test and corrects the combination of the slope coefficients by multiple regression analysis. As a result, the accuracy of the combination of slope coefficients can be further improved, and the reliability of the cognitive function evaluation system can be improved.

Furthermore, this cognitive function evaluation system can store the results for each underlying disease and compare the results of multiple tests. Therefore, the degree of progression for each underlying disease can be predicted.

Furthermore, in this cognitive function evaluation system, it is possible to store the risk calculation results of the subjects who have completed the test and compare the test results multiple times. Therefore, the degree of progression of cognitive dysfunction can be quantitatively evaluated. From the evaluation results of a plurality of cases, the degree of progression as a whole or by underlying disease can be calculated as a slope coefficient by multiple regression analysis. This makes it possible to predict the progression of cognitive dysfunction in the subject. In addition, it is possible to evaluate the deviation of the measured value of the cognitive function of the subject with respect to the prediction.

(effect)
As described above, in the cognitive function evaluation system according to the embodiment of the present invention, the risk for each underlying disease is calculated, so that the subject can evaluate which underlying disease is at high risk, and the subsequent specialist doctor. It is possible to provide useful information for diagnosis and medical practice by. Cognitive dysfunction varies according to the underlying disorder. Disorder progression also differs depending on the underlying disease. With this system, it is possible to predict the progression of each underlying disease. In addition, the intervention effect according to the underlying disease can be evaluated.

(Other embodiments)
The present invention is not limited to each of the above embodiments, and may be implemented as a client-server system, for example, and the client side may be a tablet terminal, a smartphone, or the like. The display color can be changed for people with color vision deficiency. Audio output is possible and the volume can be adjusted. In addition to finger operations, you can perform blink operations, foot taps (foot switches), and voice input. The foot sensor and voice sensor can be connected to the measurement site via USB or the like. For example, options are highlighted in sequence on the screen, and when the selected location is highlighted, the options can be selected by sending a signal such as a blink of an eye. This signal may be sent by foot tap or voice.

Further, as a correction term, the gender of the subject, the amount of drinking, the amount of smoking, and the like can be adopted. By doing so, it becomes possible to further eliminate the bias of cognitive function evaluation due to these factors. Even in these cases, the present invention can obtain the same effects as those in the above embodiment. In addition, the subject's motor, intellectual and social activity can be evaluated. For example, it may be evaluated based on walking time, reading time, club activities, and participation in the senior citizens' association. In dementia, these activities are reduced. Therefore, if it is low, the risk of dementia is high. Since these activities are effective in preventing dementia, it can be useful for dementia countermeasures by simultaneously acquiring and evaluating the information. Information may be obtained from a person other than the subject who knows the situation of the subject. The subjective evaluation and the evaluation obtained from other than this subject may be different. By simultaneously obtaining multiple cognitive function evaluations that are judged to be correct or incorrect, and subjective symptoms, self-evaluations of the subjects themselves, or objective evaluations, the accuracy of evaluations based on only one piece of information can be improved and corrected, and the relationship between the two can be corrected. Can be considered.

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

(Example 1)
Two subjects were examined using the cognitive function evaluation system of the present invention. Then, a risk assessment was calculated for each underlying disease.

Table 1 shows the test values of each test item of subjects A and B. Table 1 shows the test values of the test items in Example 1 using the cognitive function evaluation system.

As can be read from this table, the total score of both subjects A and B is 70 points. Here, in this cognitive function evaluation system, the criteria of 80 points or more are normal, 70 points to 79 points are mild cognitive impairment (MCI), and 69 points or less are dementia. Therefore, in the conventional method of simply evaluating the total score, both subjects A and B have the same degree of mild cognitive impairment, and no further information can be obtained when a specialist examines the patient thereafter.

However, in the present invention, the risk of the underlying disease is calculated based on the test values of the test items as described above. The calculation results are shown in Table 2. Table 2 shows the risk calculation results of the underlying disease in Example 1 using the cognitive function evaluation system.

As can be read from this table, according to the present invention, subject A has a risk calculation result of 54.2 points for Alzheimer-type dementia (AD), which is 69 points or less, which is the standard for dementia. That is, subject A can be evaluated as having a high risk of dementia with Alzheimer's disease as an underlying disease. Then, since the risk calculation result of cerebrovascular dementia (VaD) is 66.7 points, which is 69 points or less, which is the standard of dementia, subject B considers cerebrovascular dementia as an underlying disease. It can be evaluated that the risk of dementia is high. As described above, according to the present invention, it is possible to more precisely evaluate the risk of the underlying disease of the subject, which has not been understood by the conventional method.

In this subject A, for example, AD54.2 (high possibility of dementia due to Alzheimer's disease), VaD79.4 (not vascular dementia), DLBD79.7 (not dementia due to Levy body disease), and the like. PDD79.6 (not PDD), FTD80.7 (not dementia due to FTD), cortical basal nucleus degeneration 79.7 (not cortical basal nucleus dementia), encephalitis sequelae 81.2 (dementia due to encephalitis sequelae) Not), metabolic encephalopathy 82.5 (not dementia due to metabolic encephalopathy), normal pressure hydrocephalus 82.5 (not dementia due to normal pressure hydrocephalus). You may sort and display them in order from the one with the highest possibility. The general formula for this case is Y = A _1A + A ₂ + 6A ₃ + 8A ₄ + 10A ₅ + 10A ₆ + 7A ₇ + 9A ₈ + C for subject A.
About subject B Y = 2A _1B + 10A ₂ + 2A ₃ + 5A ₄ + 10A ₅ + 10A ₆ + 10A ₇ + 3A ₈ + C
Will be.

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

First, when the AD group is set to 1, the VaD group is set to 0, and T is set as the number of positive items, it can be confirmed from Tables 3 and 4 that there is no significant difference between the two groups. Table 5 shows the results of logistic regression analysis between these two groups. Exp (B) is the odds ratio, and AD is the reciprocal of the odds ratio estimated by the itemized evaluation. Here, the AD group may be set to 1 and the normal group may be set to 0 for comparison between the groups.

The AD group may be further classified into severity, in which case an ordinal logistic regression model may be used instead of the binomial logistic regression model.

In this way, by performing logistic regression analysis based on the accumulated data of the subjects, the combination of slope coefficients (A _1A, A _1B, A ₂ , ..., A ₈ ) when calculating the underlying disease is refined. This makes it possible to improve the reliability of this system.

(Example 3)
FIG. 11 shows a radar chart created by the cognitive function evaluation system of the present invention.
FIG. 11A is for subject A of the underlying disease A (Alzheimer's disease). (B) is that of the subject A of the underlying disease B. (C) belongs to subject C. (D) belongs to subject D. (E) belongs to subject O. (F) belongs to the subject.

It can be seen from the shape that (B) is different from the underlying disease A. In (C), the area of the radar chart is small and it can be seen that it is dementia, and since the shape is similar to (A), it is known that the underlying disease is A. In (D), the area of the radar chart is small and it can be seen that it is dementia, and since the shape is similar to (B), it can be seen that the underlying disease is B.

In (E), the underlying disease is A from the shape of the radar chart, but the area is larger than that of the subjects A and C, so it can be seen that the degree is light. In (F), the area of the radar chart is wide and it cannot be said that it is dementia because it is in the range of MCI.

(Example 4)
A logistic regression analysis was performed using the coefficient correction unit 21 of the cognitive function evaluation system in the present invention, and the results are shown in FIG. 12 (A). Dashed line 1 shows the temporal change of the underlying disease I by linear regression. The temporal transition of the subject belonging to the underlying disease I is predicted as the broken line 1. The solid lines 2 and 3 show the transitions of the scores of subjects A and B belonging to the underlying disease I by linear regression, respectively. In the case of the solid line 2, that is, when it exceeds the broken line 1, it indicates that the prognosis of A is better than expected, and if there is an intervention a during this period, it can be considered that this a was effective.

When the transition of B's score over time is solid line 3, that is, when it falls below the broken line 1, it indicates that the prognosis of B was worse than expected, and if there was intervention a during this period, that a was a deterioration factor. It can be considered that there was. Therefore, this is useful, for example, when examining the effectiveness of treatment. This prediction is superior to the existing prediction because the prognosis prediction by underlying disease is more accurate than the prognosis prediction for dementia as a whole. Here, linear regression is shown as an example, but curved regression may be used. Whether linear regression or curved regression is used, a notch may be provided depending on the stage of the disease to divide the arithmetic expression (see FIG. 12B).

(Example 5)
A specific method for correcting factors that affect cognitive function is shown.
FIG. 13 (A) shows the relationship between lifestyle-related diseases, for example, the presence or absence of walk A and cognitive function (other factors are corrected). It can be seen that the cognitive function of the walking group is good by the "difference" shown in the figure. At this time, logistic regression analysis is performed with A group and non-group as independent factors and cognitive function as a dependent factor. If this "difference" becomes significant when the presence or absence of a walk is added to the adjusting factor, it can be seen that the presence or absence of a walk is a mediating factor in the cognitive function evaluation. Therefore, it is not necessary to correct the cognitive function evaluation depending on the presence or absence of a walk. On the contrary, if the "difference" remains significant, it is considered that the part is not a mediator, and it is necessary to correct the cognitive function evaluation. This "difference" differs depending on the underlying disease of dementia.

FIG. 13B shows the relationship between lifestyle-related diseases, for example, walking time A and cognitive function (other factors are corrected). The relationship between the two draws an S-shaped curve as shown in the figure. That is, the cognitive function improves as A increases, but the relationship does not become linear, and when the amount of A is below a certain level and when the amount of A is above a certain value, no improvement in cognitive function is seen, and conversely, a constant amount. At the following times, deterioration of cognitive function is not seen. As described above, it is desirable to consider the range of A in the above correction. Specifically, for example, in FIG. 13B, when the differential coefficient of the curve is less than 0.2, it is desirable not to correct it. The outline of this figure also differs depending on the underlying disease of dementia.

(Example 6)
An example of the inspection using the blink sensor is shown. In neuromuscular diseases such as amyotrophic lateral sclerosis (ALS), the movement of limbs is restricted, speech cannot be made, and it becomes difficult to determine the presence or absence of dementia. The subject is in bed rest, but can communicate his / her intention in a blink of an eye. This system can be used because it is less likely to impair vision and hearing. In addition, since the orbicularis oculi muscle is not easily damaged in ALS, it is possible to perform blinking motion. The problem presentation will be done in the same way.

Answers are choices, so you can highlight one of them. The subject blinks when the option he or she wants to select is highlighted. The system senses the moment and the highlighting stops moving. Then, if the subject decides that the option is acceptable, the selection is confirmed by blinking again. If it is selected by mistake and the highlight stops, it is considered as a misselection by not blinking, and the highlight moves to the next option. In this way, the answers are decided one by one.

Since the required time differs depending on the medical condition, here, the cognitive function is evaluated and the dementia is evaluated according to the underlying dementia disease, as in the other embodiments, depending on the correctness of selection, distribution, and the like. For example, even ALS may be complicated by dementia. In this way, dementia can be diagnosed even for a bedridden subject, which was difficult in the past.

As described above, the present invention is extremely useful in the field of dementia treatment. Specifically, the present invention makes it possible to determine dementia, which originally consists of a plurality of underlying diseases, by the underlying disease, so that the following can be achieved.

The presence or absence of dementia can be determined at an early stage. Since no assistance is required for the inspection part, anyone can carry out the screening for dementia at public institutions such as city halls, waiting areas at medical institutions, and welfare facilities.

It can indicate the underlying 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 can show the pathological condition that should be distinguished from dementia. For example, dementia may not be evaluated correctly due to "depression" or "neurosis". It is possible to predict the prognosis of dementia. Since the prognosis of dementia depends on the underlying disease of dementia, it is desirable to consider each underlying disease.

It can show the therapeutic / intervention effect for dementia. As described above, the treatment differs depending on the underlying dementia disease, and its effect also differs depending on the underlying dementia disease. Therefore, it is desirable to evaluate cognitive function in consideration of the underlying dementia disease.

In addition, cognitive function can be evaluated even for those who have difficulty in manual operation. Specifically, it can be carried out even for those who are in bed. Even in bed rest, some people have dementia and some have normal cognitive function.

1 Computer 11 Question storage unit 12 Screen display unit 13 Question change unit 14 Inspection value measurement unit 15 Risk calculation unit 16 Risk selection unit 17 Result output unit 21 Coefficient correction unit 22 Calculation result correction unit 23 Imaging unit

Claims (24)

It is a cognitive function evaluation system that evaluates the risk of multiple underlying diseases in the cognitive function of a subject based on the test items for each cognitive function.
A measuring means for measuring the inspection value of the inspection item and
A calculation means for calculating the risk based on the inspection value measured by the measurement means, and a calculation means.
It is equipped with a selection means for selecting a high-risk underlying disease from the calculated risks.
A response time setting means that can set the response time of the inspection item is provided .
The calculation means is a cognitive function evaluation system including a correction function for correcting the risk calculation formula by multivariate analysis. The underlying disease may be Alzheimer's disease, cerebrovascular dementia, Lewy body dementia, Parkinson's dementia complex, frontotemporal dementia, cortical basal nucleus degeneration, encephalitis (aftereffects), metabolic encephalopathy, or , The cognitive function evaluation system according to claim 1, wherein the person has one or more of normal pressure hydrocephalus. The invention according to claim 1 or 2, wherein the inspection item is one or more of memory, orientation, aphasia, agnosia, calculation, comprehension, judgment, and executive function. Cognitive function evaluation system. The cognitive function evaluation system according to any one of claims 1 to 3, further comprising a point allocation changing means capable of changing the allocation of test values. The cognitive function evaluation system according to claim 1 , wherein the multivariate analysis is a multiple regression analysis. The inspection items consist of high-difficulty items and low-difficulty items.
The cognitive function evaluation according to any one of claims 1 to 5 , wherein when the subject answers the low difficulty item incorrectly but answers the high difficulty item correctly, the risk of the underlying disease is corrected and evaluated. system. Further having an input means for inputting matters relating to the physical condition of the subject,
The cognitive function evaluation system according to any one of claims 1 to 6 , wherein when the subject is negatively input to the matter relating to the physical condition, the risk of the underlying disease is corrected and evaluated. Further having an input means for inputting matters related to the subject's awareness of dementia,
The cognitive function evaluation system according to any one of claims 1 to 7 , wherein when the subject is positively input to the matter relating to the awareness of dementia, the risk of the underlying disease is corrected and evaluated. Further having an input means for inputting the matters related to the educational history of the subject,
The cognitive function evaluation system according to claim 3, wherein when the subject has a long educational history, the slope coefficient of the computational power is increased. The subject further has an input means for inputting that the test item is unknown.
The cognitive function evaluation system according to any one of claims 1 to 9 , wherein when the rate of inputting unknown information is more than a certain frequency, the risk of the underlying disease is corrected and evaluated. Further having a blink frequency detecting means for detecting the blink frequency of the subject,
The cognitive function evaluation system according to any one of claims 1 to 10 , wherein the risk of the underlying disease is corrected and evaluated when the frequency of blinking is low. Further having a facial expression detecting means for detecting a change in the facial expression of the subject,
The cognitive function evaluation system according to any one of claims 1 to 11 , wherein the risk of the underlying disease is corrected and evaluated when the change in facial expression is equal to or less than a predetermined standard. 13 . The cognitive function evaluation system described in any one. The cognitive function evaluation system according to any one of claims 1 to 13 , wherein the prognosis is predicted for each underlying disease by recording the test values of the test items a plurality of times. The cognitive function evaluation system according to any one of claims 1 to 14 , wherein the degree of progression is predicted for each type of underlying disease from the test results of the test items. The invention according to any one of claims 1 to 15 , wherein the degree of progression is evaluated in comparison with the prediction of the prognosis for each type of the underlying disease by recording the test values of the test items a plurality of times. Cognitive function evaluation system. The present invention according to any one of claims 1 to 16 , further comprising a means for recording correctness / error within the answering time, which measures the answering time in each of the inspection items and records whether or not the answering time is correct within the answering time. Cognitive function evaluation system. It is a cognitive function evaluation system that evaluates the risk of multiple underlying diseases in the cognitive function of a subject based on the test items for each cognitive function.
The plurality of underlying diseases include Alzheimer-type dementia (AD), cerebrovascular dementia (VAD), Lewy body dementia (DLBD), Parkinson dementia complex (PDD), and frontotemporal dementia (FTD). , Cortical basal nucleus dementia, encephalitis (aftereffect), metabolic encephalopathy, or normal pressure hydrocephalus,
The inspection items consist of memory, orientation, aphasia, agnosia, calculation, comprehension, judgment, and executive function.
Each test value of each test item is X ₁ (memory test value), X ₂ (orientation test value), X ₃ (misword test value), X ₄ (agnosia test value), X ₅ ( Computational ability test value), X ₆ (comprehensive ability test value), X ₇ (judgment test value), and X ₈ (executive function test value) are high if cognitive function is maintained. With a score, if cognitive function is not maintained, a measurement method that measures with a low score,
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 has a slope coefficient (A ₁ , 1,) for each underlying disease. _{a 2, a 3, a 4} , a 5, a 6, a 7 and, to calculate the risk Y underlying disease which is the sum of all the values obtained by multiplying the _{a 8)} respectively for each underlying disease, the following formula The calculation means shown in (1) and
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)
If there is a Y that is lower than the predetermined underlying disease standard value that is judged to be at risk of the underlying disease in the calculated Y for each underlying disease, the underlying disease of that Y is selected as the underlying disease with a high risk. Selection means to be selected and
A cognitive function evaluation system characterized by being equipped with. A radar 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 of the above test items, and a predetermined underlying disease. Display means to display in comparison with each radar chart,
18. The cognitive function evaluation system according to claim 18. The inspection items consist of memory, orientation, aphasia, agnosia, calculation, comprehension, judgment, executive function, and correction items.
The calculation means is inclined 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 for each underlying disease. Add all the values obtained by multiplying the coefficients (A ₁ , A ₂ , A ₃ , A ₄ , A ₅ , A ₆ , A ₇ , and A ₈ ), and then C (inspection value of the correction term). The following formula (1') to calculate the risk Y of the underlying disease, which is the sum of the above, for each underlying 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')
18. The cognitive function evaluation system according to claim 18 or 19. When the selection means has a calculated Y for each underlying disease that is equal to or higher than the underlying disease reference value but lower than a predetermined MCI reference value that is determined to be at risk of MCI. The cognitive function evaluation system according to any one of claims 18 to 20 , wherein the cognitive function evaluation system is selected as being at risk of MCI of the underlying disease. The cognitive function evaluation system according to any one of claims 18 to 21 , further comprising a response time setting means capable of setting the response time of each test item. An answer time correctness recording means for recording whether or not a correct answer was made within the answer time for each of the inspection items,
A 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 inspection items that can be answered correctly within the answer time.
22. The cognitive function evaluation system according to claim 22. 23. The cognitive function according to claim 23, wherein the cognitive function is provided with a safe driving ability evaluation means for evaluating the safe driving ability of a vehicle based on the problem handling ability within the time limit of the subject evaluated by the problem processing ability evaluation means. Rating system.

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