JPH09106427A - Disease preventing multi-check system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To make the model of checking and considering process which allows a specialist such as a doctor to make a diagnosis. SOLUTION: This system is provided with a file preparing part 3 provided with each master of the condition of a patient, a physiological effect, nutritive elements, a subjective sympton, etc., and preparing an individual file by relating each data on basic items, the contents of exercise, the contents of meals, the subjective symptons, etc., with each master, a necessary amount calculation part 5 calculation the necessary amount by the nutritive element, based on each master, an intake amount calculation part 6 calculating an intake amount by the nutritive element and an excess/shortage degree calculation part 7 calculating an excess/shortage degree by the nutritive element from the necessary amount and the intake amount. The system is also provided with a subjective sympton cause estimating part 8 estimating the cause of the subjective sympton by reasoning an affection of the state of excess/shortage to a human body, a living body abnormality developing region inferring part 10 inferring the existence of abnormality at each part based on the analytic result obtained by the bio-fluctuation analysis of hair, etc., and a coincident degree judging part 11 comparing the subjective aympton and the abnormality developing region inferred by the subjective sympton cause estimating part 8 and the living body abnormality developing region inferring part 10.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a disease prevention multi-check system in which a specialist such as a nutritionist or a doctor models a check and consideration process leading to a diagnosis and realizes a processing process according to the model.

[0002]

2. Description of the Related Art Due to the rapid progress of computer technology in recent years, computers are used in the field of medical technology for the management of enormous amounts of medical information and calculation of medical fees.

In addition to such an office work type, recently, there is also a type of a hospital lunch menu which is installed in a computer. In other words, the system is such that a menu for meals suitable for a medical condition is planned based on calorie calculation of each of 6,000 or more kinds of foods.

[0004]

As described above, although computerization is progressing in a field that is not directly related to the doctor's diagnosis, consideration is given to the inspection and consideration process leading to the diagnosis by an expert such as a doctor. No system has been provided so far.

This is because the inspection and consideration process leading up to the diagnosis is a complicated one related to human physiological action, and the number of data to be processed is enormous. In addition, since human nutrients such as vitamins, essential minerals, essential amino acids, and other nutrients such as macronutrients need to take into account the complex action that acts on the cell units of the human body, This is because there is a very technical and difficult problem of how to replace it with a form that can be processed on a computer.

It is verified by bioscience that essential nutrients are indispensable for maintaining life activity of human beings, and if any of these nutrients is deficiently deficient, life sustaining activity will be seriously affected. There is.

In recent years, the existence and necessity of these essential nutrients have been recognized in Japan as well, but compared with the United States and other countries, the current guidance system and response system have been delayed. In most cases, the actual nutrition guidance is mainly focused on calorie calculation. Regarding micronutrients, even in the field where detailed guidance is provided, there are around 10 types. It is the current situation.

The present invention was devised to solve such problems, and its purpose is to model the inspection and consideration process leading up to the diagnosis by an expert such as a doctor, and process the process according to the model. By making it possible to make more accurate judgment of disease prevention, and for daily health management, nutritional science, molecular orthodontic medicine, which is necessary for nutritional management executed by the will of the parties, It is to provide a disease prevention multi-check system that makes it possible to finely search verification results of bioscience and the like and provide them as compiled information.

[0009]

In order to solve the above-mentioned problems, the disease prevention multi-check system of the present invention stores each data such as disease name, symptom, complication, prescription drug, relation between physiological action involved and nutrients. Stored medical condition master, drug name,
A drug master that stores data such as display symbols, indications, target symptoms, side effects, target disease names, dose limiting conditions, inhibitory nutrients, and physiological action names, participating nutrient names, enzyme names produced, principle of action, subjective symptoms, occurrence Physiological action master that stores data such as range, disease, antagonistic ratio, nutrient name, intake reference amount, excessive risk value, dangerous value symptom, cause of excess and deficiency, cause of deficiency,
A nutrient master that stores data such as involved physiological actions, supplemental foods, and intake restrictions, and an external factor master that stores data such as external nutritional status influence items, affected nutrient names, and affected physiological action names, and foods. Name, contained nutrient name, nutrient content, energy amount, food master that stores each data such as intake restriction conditions, menu name, used food name, used food amount,
A menu master that stores data such as cooking method by food, cooking time by food, standard energy amount, standard nutrition amount, and a subjective symptom master that stores data such as target range and subjective symptoms. Of each symptom, which stores each data such as the coordination between the time and excess and the physiological action involved, and the energy metabolism rate (R
(MR) value, exercise intensity master, RMR total amount calculation formula, life activity intensity index calculation formula, life activity intensity calculation master that stores each data such as life activity intensity classification, basal metabolism, energy requirement, Energy requirement master storing each data such as basal metabolism reference value, energy requirement per body weight, nutrition requirement master storing requirement of each nutrient according to living activity intensity classification, height, weight, age, By inputting basic condition data such as disease status and medication, exercise content data, meal content data, subjective symptom data and hair test data, a personalized file creation section that creates a personalized file in association with each master. , According to the data entered in the individual file created by this individual file creation unit, the living activity intensity calculation master, the Based on the energy requirement master, the nutrition requirement master and the nutrient master, a requirement calculator that calculates the requirement for each nutrient for each individual, the food master, the nutrient master, the nutrient symptom master and the An intake amount calculation unit that calculates the intake amount for each nutrient based on the individual drug master, a required amount calculated by the required amount calculation unit, and an intake amount calculated by the corresponding intake amount calculation unit By calculating the difference between the excess and deficiency degree calculation unit for calculating the excess or deficiency degree of each nutrient for each individual, and the location and range in the body where the subjective symptom occurs based on the subjective symptom master, The nutrient group involved in the obtained place and range is extracted from the nutrient-specific symptom master, and the extracted nutrient group is divided into a storage function and a regulation function in the body. By performing the above, the subjective symptom cause estimation unit that estimates the cause of the subjective symptom together with the excess or deficiency degree of each nutrient calculated by the excess and deficiency degree calculation unit, and the biological wave analysis of the substance separated and discharged from the human body With the analysis result as input data, from the numerical value showing the bioactivity of each site in the body shown in the input data, the biological abnormality occurrence site estimation unit for estimating the presence or absence of abnormality of each site, and the subjective symptoms Comparing the subjective symptom estimated by the cause estimation unit and the coincidence determination unit for determining the estimated coincidence by comparing the abnormality occurrence site estimated by the biological abnormality occurrence site estimation unit, To do.

That is, the disease prevention multi-check system of the present invention includes a part for calculating (estimating) the excess or deficiency of nutrients, and a part for estimating the cause of subjective symptoms by analogizing the influence of the excess or deficiency state on the human body. It has a two-stage structure, which makes it possible to more accurately determine the occurrence of a disease by considering the physiological action of humans.

That is, it is possible to grasp the current state of nutrient deficiency of the target person at the level of excess or deficiency by comparing the required amount and the intake amount of each nutrient for each individual.
Based on nutrients in excess and deficiency, it is possible to estimate the meaning of excess and deficiency and analyze the state of overall physiological action at the cellular level by analyzing the range of influence including the functional area of the satisfied nutrients It is what

Further, the disease prevention multi-check system of the present invention includes a part for calculating (estimating) the excess or deficiency of nutrients,
In addition to the two-stage structure with the part that presumes the cause of subjective symptoms, the presence or absence of abnormality in each part of the body from the biological wave analysis results of substances (eg, hair, nails, urine) separated and excreted from the human body Since the configuration that includes the part that estimates the accuracy of the check system is compared with the results obtained by the subjective symptom cause estimation unit and the results obtained by the biological abnormality occurrence site estimation unit, Sex) is improved.

The disease prevention multi-check system of the present invention also estimates the latent deficiency of micronutrients. Here, the latent deficiency state of micronutrients means that the relevant subjective symptoms are not alive but the deficiency state is estimated. In other words, if it is left as it is, it means that the condition will eventually lead to some symptom or disease, and the system can be verified as preventive medicine.

That is, for the latent deficiency state, first, the subjective symptoms estimated to be related to the nutrient deficiency state estimated from the personal data in the available range are collated, and the nutrients in the estimated deficiency state are collated with the subjective symptoms. If there is not the range of the data stored in, it is assumed that there is a latent deficiency state.

[0015]

An embodiment of the present invention will be described below with reference to the drawings.

FIG. 1 is a block diagram showing the electrical construction of the disease prevention multi-check system of the present invention.

In the figure, the output of the data input section 1 for inputting basic item data, exercise content data, meal content data, subjective symptom data, hair test data, biological wave analysis data, etc. creates a management file for each individual. The file creation unit 2 and the biological abnormality occurrence site estimation unit 10 that estimates the presence / absence of abnormality of each site based on the biological wave analysis data are output, and the output of the file creation unit 2 stores the created file. To the file storage unit 3.

Further, the file creating section 2 includes a medical condition master, a drug master, a physiological action master, a nutrient master, an external factor master, a food master, a menu master, a subjective symptom master, a nutrient-specific symptom master, an exercise intensity master, which will be described later. It is bidirectionally connected to various master storage units 4 in which various masters such as a living activity intensity calculation master, an energy requirement master, and a nutrition requirement master are stored.

Further, the various master storage units 4 include a required amount calculation unit 5 for calculating a required amount for each nutrient for each individual, an intake amount calculation unit 6 for calculating an intake amount for each individual nutrient, and a required amount. By obtaining the difference from the intake amount, the excess / deficiency degree calculation unit 7 for calculating the excess / deficiency degree for each nutrient for each individual and the subjective symptom cause estimation unit 8 for estimating the cause of the subjective symptom are bidirectionally connected. There is.

A required amount calculation unit 5, an intake amount calculation unit 6, and a subjective symptom cause estimation unit 8 are bidirectionally connected to the file storage unit 3, and the output of the required amount calculation unit 5 and the intake The output of the amount calculation unit 6 is guided to the excess / deficiency degree calculation unit 7. The output of the excess / deficiency degree calculation unit 7 is guided to the subjective symptom cause estimation unit 8 and the data output unit 9,
The output of the subjective symptom cause estimation unit 8 is guided to the data output unit 9 and the coincidence determination unit 11.

The output of the rearrangement abnormality occurrence part estimation unit 10 is led to the data output unit 9 and the coincidence degree determination unit 11, and the output of the coincidence degree determination unit 11 is led to the data output unit 9. Has become.

The file creating section 2 is based on various data input from the data input section 1 and various master storage sections 4 are provided.
This is a block for creating a file for each individual while referring to various masters stored in. The individual file created here is stored for about 1-3 months after that.
After that, it will be updated sequentially. This is to make more accurate judgments for disease prevention by always managing with the latest data.

The required amount calculation unit 5 uses the file creation unit 2
According to the data entered in the individual file created in step 1, the nutrients are classified for each individual based on the living activity intensity calculation master, energy requirement master, nutrition requirement master and nutrient master stored in various master storage units 4. Is a block for calculating the required amount and the corrected required amount obtained by adding a certain amount of correction to the calculated required amount.

The intake amount calculation unit 6 includes various master storage units 4
Based on the food master, nutrient master, nutrient-specific symptom master, and drug master stored in, the intake for each nutrient is calculated for each individual, and the corrected intake is calculated by adding a certain correction to the calculated intake. This is a block to be calculated.

The excess / deficiency calculation unit 7 calculates the required amount (corrected required amount) calculated by the required amount calculating unit 5 and the corresponding intake amount (corrected intake amount) calculated by the intake amount calculating unit 6.
This is a block for calculating the degree of excess or deficiency for each individual nutrient by obtaining the difference between and.

Further, the subjective symptom cause estimating unit 8 obtains the place and range in the body where the subjective symptom is occurring based on the subjective symptom master stored in the various master storing units 4, and the obtained place The nutrient groups involved in the range and are extracted from the nutrient-specific symptom master. Then, a block for estimating the cause of the subjective symptom together with the excess / deficiency degree of each nutrient calculated by the excess / deficiency degree calculating unit 7 by analyzing the storage function and the adjusting function in the body of the extracted nutrient group. Is.

The biological abnormality occurrence part estimation unit 10 uses as input data the analysis result of the biological wave analysis of the substance separated and discharged from the human body, and calculates the vitality of each part in the body shown in the input data. This is a block for estimating the presence or absence of an abnormality in each part from the numerical values shown.

Further, the coincidence determination section 11 compares the subjective symptom estimated by the subjective symptom cause estimating section 8 with the abnormality occurrence site estimated by the biological abnormality occurrence site estimating section 10 to make an estimation. This is a block for determining the degree of coincidence.

Next, various masters stored in the various master storage section 4 will be described.

In the various master storage sections 4, as described above, the medical condition master, the drug master, the physiological action master,
Various masters such as nutrient master, external factor master, food master, menu master, subjective symptom master, nutritional symptom master, exercise intensity master, life activity intensity calculation master, energy requirement master and nutrition requirement master are stored. ing.

As shown in FIG. 2, the medical condition master stores data indicating disease names, symptoms, complications, prescription drugs, relations between involved physiological actions and nutrients, and the like. Here, the 3 to 5 digit continuous number entered in the parentheses in the figure means that it is a code number arbitrarily given. The same applies to the master shown below.

In FIG. 2, each column of symptoms and complications is a description in which the specific symptom and the range of occurrence are associated with each other, and the columns of physiological action involved and nutrients show necessary nutrients and physiological action involved. Is a related description. As described above, the characteristic of the pathological condition master is that each symptom is associated with the occurrence range and that necessary nutrients are associated with the physiological actions involved.

The drug masters are classified into prescription drug masters shown in FIG. 3 and commercially available drug masters (not shown).

The prescription drug master stores data indicating drug names, various display symbols, efficacy, target symptoms, side effects, target disease names, dose limiting conditions, inhibitory nutrients and the like. Also,
The over-the-counter drug master also has the same data structure as the prescription drug master.

Although not shown in the figure, the physiological action master stores data indicating physiological action names, participating nutrient names, producing enzyme names, action principles, subjective symptoms, occurrence range, disease diseases, antagonistic ratio and the like. There is.

As shown in FIG. 4, the nutrient master has nutrient names, reference intake amounts, excessive risk values (excessive attention intake amount),
It stores data indicating dangerous value symptoms, intake restrictions (intake prohibition conditions), causes of excess or deficiency (mainly deficiency causes), supplementary foods, physiological actions involved (absorption conditions, storage places), and the like.

The external factor master, as part of which is shown in FIG. 5, has data (external nutritional state influencing items) in which code numbers are associated with specific states, and unaffected nutrient names and It stores data indicating the name of the effect physiological action.

As shown in FIG. 6, the food master stores data indicating food names, contained nutrient names, nutrient contents, energy amounts, intake restriction conditions and the like. In this embodiment, there are 1600 kinds or more. It stores all the food data.

Although the menu master is not shown,
It stores data showing menu names, names of foods used, amounts of foods used, cooking methods by foods, cooking times by foods, standard energy amounts, standard nutrition amounts, and the like. Although not shown, the subjective symptom master also stores data in which the target range and the subjective symptom are associated with each other.

As shown in FIG. 7, the nutrient-by-nutrition symptom master stores data showing the adjustment between nutrient deficiency and excess and the physiological action involved, for each nutrient. FIG. 7 illustrates calcium.

The exercise intensity master, as shown in FIGS. 8 and 9, has an energy metabolism rate (RMR) according to the type of exercise.
Stores the value of.

The living activity intensity calculation master is, as shown in FIG. 10, an RMR total amount calculation formula, a living activity intensity index calculation formula,
It stores each data indicating the classification of life activity intensity.

As shown in FIG. 11, the energy requirement master stores data indicating basal metabolism, energy requirement, basal metabolism reference value, energy requirement per body weight and the like.

As shown in FIG. 12, the nutritional requirement master stores data showing the required amount of each nutrient according to the living activity intensity classification (4 types shown in FIG. 11).
In the table, data showing [1] [light] is shown as an example.

Next, the processing operation by the disease prevention multi-check system having the above-described configuration will be performed as follows: (1) individual file creation processing, (2) analysis data analysis processing, (3) biological wave analysis data analysis processing. I will explain separately.

(1) Individual File Creation Process As a premise of performing the above process (2), it is necessary to first create an individual file. Therefore, the operator inputs data necessary for analysis of basic item data, exercise content data, meal content data, subjective symptom data, hair examination data, etc. from the data input unit 1.

When inputting the basic item data, FIG.
The basic item input table (basic item) shown in is displayed on the screen, and then the basic item input table (external factor) shown in FIG. 14 is displayed on the screen. Enter each item using the keyboard, mouse, etc. according to the information you entered. The input contents are height,
The contents include weight, age, illness status, and whether or not medication is taken.

Based on these input data, a disease / disease file is created by referring to the medical condition master (FIG. 2), a medication drug file is created by referring to the prescription drug master (FIG. 3), and an external factor master ( External factor files are created with reference to FIG. 5) and are stored in individual files as basic item data files. An input processing procedure of such basic item data is shown in FIGS. 15 and 16.

The fact that this basic item data is input means that the following data (exercise content data, meal content data,
It is a prerequisite for inputting subjective symptom data and hair test data).

Next, when inputting the exercise content data,
Since the basic item input table (exercise content) shown in FIG. 17 is displayed on the screen, the operator follows the exercise content in the daily life that the subject has previously filled in on the exercise content input sheet.
Enter each item using the keyboard and mouse. The input contents are frequency, exercise time, exercise time per exercise, etc. for each item such as sleep, rest, discourse, education ...

When the target person is playing sports, the basic item input table (sports content) shown in FIG. 18 is displayed on the screen. Input each item using a keyboard, mouse, etc. according to the sports content. The input content is the same as that of the exercise content in daily life.

Based on these input data, the daily exercise file is created by referring to the exercise intensity master (FIG. 8), and the practice sports file is created by referring to the exercise intensity master 21 (FIG. 9). It is stored as a data file in a personal file. The procedure for inputting such exercise content data is shown in FIG.

Next, when inputting the meal content data,
Since the meal content input table shown in FIG. 20 is displayed on the screen, the operator can use a keyboard, a mouse, etc. in accordance with the meal content (at least the past week or so is required at least in the past) that the target person previously filled in on the meal content input form. Input each item by. The input contents are the food (food) number, the food (food) name, the intake amount, the cooking method, and the like.

Based on these input data, a meal content file is created by referring to the two groups of food group codes shown in FIG. 21 and the menu master, and then the content of each nutrient of the food master (FIG. 6) is calculated. A nutrient-specific intake file is created with reference to, and each is stored in the personal file as a basic item data file. FIG. 22 shows the input processing procedure of such meal content data.

Next, when inputting subjective symptom data,
First, since the subjective symptom input table range designation shown in FIG. 23 is displayed on the screen, the operator selects one subjective symptom written in the subjective symptom input form in advance from the screen displayed. In this example, the mental state is selected.
Therefore, since the subjective symptom input table (mental state) shown in FIG. 24 is displayed on the display screen, the operator uses a keyboard, a mouse or the like according to the content of the subjective symptom which the subject has previously filled in the subjective symptom input sheet. Enter each item. In this case, regarding the subjective symptom master, since there are relatively few corresponding items, the code is displayed in advance on the screen.

A subjective symptom file is created based on these input data, and is stored in the personal file as a basic item data file. The procedure for inputting such subjective symptom data is shown in FIG.

Next, when inputting the hair inspection data,
Since the hair inspection result input table shown in FIG. 26 is displayed on the screen, the operator inputs each item with a keyboard, a mouse or the like in accordance with the content of the hair inspection report of the hair inspection received by the subject in advance. The input contents are contents such as the detected amount of each mineral and its evaluation.

The hair inspection report is shown, for example, in the form shown in FIG. In other words, the evaluation is centered on the "reference value", "slightly low value", "improved low value", "extremely low" and "slightly high value".
It is a seven-level evaluation of "high price requiring improvement" and "extremely high".

Based on these input data, a hair inspection file is created with reference to the nutrient master (FIG. 4),
It is stored in the personal file as a basic item data file. FIG. 28 shows the procedure for inputting such hair inspection data.
Shown in

With the above processing, the creation of individual files is completed.

(2) Analysis processing of data to be examined Next, the operator selects a person to be examined. That is, the examination target data as shown in FIG. 29 is selected by inputting the code number of the examination subject.

The data to be examined is a set of the five items (basic items, exercise items, dietary contents, subjective symptoms, and hair examination) input in the above process (1), and one of them is selected. It is designed to select two combinations. Here, the numerical value entered in each item indicates the number of digits, respectively, and indicates that it is a display of 4 digits / 2 digits / 2 digits.

When one is selected from the data to be examined, the required amount calculation section 5 causes the life activity intensity calculation master (FIG. 10), the energy required amount master (FIG. 11),
Referring to the nutrient requirement master (FIG. 12) and the nutrient master (FIG. 4), the excess / deficiency estimation processing image data file shown in FIGS. 30 and 31 is created.

Specifically, as shown in the processing procedure in FIGS. 32 and 33, first, the life activity intensity index calculation formula of the life activity intensity calculation master, and the daily exercise file and the implementation sports file stored in the personal file. From this, the total amount of RMR is calculated and the activity level classification is determined for each exercise item (steps S1 to S4). Next, the energy requirement master (Fig. 1
The basal metabolic rate E requirement is calculated by referring to 1) and the data such as the age, weight, and sex of the subject stored in the basic item data file (step S5), and the calculation result is an intermediate file. (Step S6).

After this, the nutritional requirements by living activity intensity of the nutritional requirements master (FIG. 12) and the nutrients master (FIG. 4)
The nutrient requirement correction count is calculated from the reference requirement of (step S7), and the nutrient-specific requirement is calculated from the corrected count (step S8), and the excess / deficiency estimation processing image data shown in FIGS. A file is created (step S9). In the excess / deficiency estimation processing image data file shown in FIG. 30, finally, (6) the value indicated by the daily consumption E (the average energy consumption of the subject per day)
Is the most important value.

However, in the excess / deficiency estimation processing image data file shown in FIG. 31, the required amount is obtained by proportional division from the average required amount for each nutrient and the in-vivo composition ratio known in advance. Further, in the present embodiment, the low value of the average required amount is set as the minimum required amount for preventing deficiency symptoms. Further, the median value indicates an intermediate value between the low value and the high value of the average required amount.

Further, the required amount calculating section 5 carries out a process for adding a certain amount of correction to the required amount obtained in steps S1 to S9. That is, referring to the nutritional requirement master (FIG. 12) and the living activity intensity calculation master (FIG. 10), FIG.
Create the excess / deficiency estimation processing image data file shown in.

Specifically, as shown in an example of the processing procedure in FIG. 35, the sex of the subject is first checked from the personal file (step S11), and in the case of a woman, the presence or absence of pregnancy, the presence or absence of breastfeeding, etc. Is determined (steps S12 to S14), and the required amount is corrected using the correction coefficient of the nutrient master (FIG. 4) during pregnancy and breastfeeding (step S15). next,
The estimated reference weight is calculated by referring to the living activity intensity calculation master (FIG. 10) and the data such as the weight, age, and height of the personal file (step S16), and comparison with the current weight is performed (step S17). If the comparison result between the estimated reference weight and the current weight is not within the reference weight, the nutritional requirement by living activity intensity of the nutritional requirement master (FIG. 12) is referred to and the required value according to the reference value is set in the personal file. (Step S19).

In the correction process described above, corrections at the time of pregnancy and nursing are explained, but in addition to this,
Corrections based on the amount of sports, corrections to athletes, corrections based on disease states, inspections and corrections of drugs in use, corrections based on obesity, and the like are performed as necessary.

The above is the processing operation in the required amount calculation unit 5.

Next, the processing operation of the intake amount calculating section 6 will be described.

The intake amount calculating section 6 refers to the food master (FIG. 6), the nutrient master (FIG. 4) and the prescription drug master (FIG. 3) to create an excess / deficiency estimation processing image data file shown in FIG. .

Specifically, as shown in the processing procedure in FIG. 37 and FIG. 38, first, from the energy amount and the nutrient amount of the food master (FIG. 6) and the meal content file stored in the personal file, Calculate another content (step S21), then refer to the loss rate by cooking method of the nutrient master (Fig. 4), perform loss correction for each cooking method for each nutrient, and total intake by nutrient Is calculated (step S2
2 to S25).

Next, referring to the absorption rate of the nutrient master (FIG. 4), the absorption rate for each nutrient is corrected to calculate the average daily intake, which is stored in a personal file (steps S26 to S28). ). After that, referring to the prescription medicines taken in the medicine file stored in the personal file, the presence or absence of the medicines is checked (steps S29, S).
30) If there is a drug to be taken, the prescription drug master is referred to search for the inhibited nutrient (step S31).
Then, if there is a corresponding inhibited nutrient, that nutrient is stored in the personal file as the inhibited nutrient (steps S32 to S34).

The above is the processing operation in the intake amount calculating section 6.

Next, the processing operation of the excess / deficiency degree calculation unit 7 will be described.

In the excess / deficiency degree calculation unit 7, the difference between the corrected required amount for each nutrient calculated by the required amount calculation unit 5 and the intake amount for each nutrient calculated by the intake amount calculation unit 6 is calculated. An excess / deficiency estimation processing image data file shown in FIG. 39 is created. In FIG. 39, the value of the required amount is shown in FIG.
The average requirement is low (that is, the minimum requirement to prevent deficiency symptoms).

The excess / deficiency estimation processing image data file shown in FIG. 39 makes it possible to know the excess / deficiency state of each nutrient. These data are used by the subjective symptom cause estimation unit 8
Given to.

Next, the processing operation of the subjective symptom cause estimating unit 8 will be described.

In the subjective symptom cause estimating unit 8, the subjective symptom data file stored in the personal file, the nutrient master (FIG. 4), the nutrient-specific symptom master (FIG. 7), the medical condition master (FIG. 2) and the drug master (FIG. With reference to 3),
The subjective symptom analysis image data file shown in FIGS. 40 and 41 is created.

Specifically, as shown in the processing procedure in FIGS. 42 to 44, first, the symptoms are classified according to the occurrence range based on the subjective symptom data file stored in the personal file (step S41), and the classification is performed. An intermediate data file for each occurrence range is created by the amount (steps S42 to S42)
45). Next, with reference to the created subjective symptom data for each occurrence range and the classification of minerals in the symptom master for each nutrient (FIG. 7), a physiological action related to the symptom according to each occurrence range is searched (step S46). ). Then, referring to the mineral classification of the symptom master according to nutrient (Fig. 7) from the involved physiological action, the corresponding mineral name is extracted, a code is added to this, and added to the intermediate data file ( Steps S47, S48).

Next, referring to the in-vivo composition ratio of the nutrient master (FIG. 4), minerals are selected in descending order of composition ratio (step S49), and the selected mineral is analyzed (step S50).

In the mineral analysis, an inhalation place investigation routine (step S51), a storage place investigation routine (step S52), an insufficient adjustment function investigation routine (step S5)
3), excessive adjustment function investigation routine (step S54),
An antagonistic relationship investigation routine (step S55) and an excess / deficiency degree verification routine (step S56) are sequentially performed, chain factor data is organized, and the presence or absence of the corresponding vitamin is determined (steps S7 and S58). As a result, if there is a corresponding vitamin, the process proceeds to vitamin analysis (step S5).
9).

The vitamin analysis is performed in the same manner as the mineral analysis described above (steps S60 to S66).

On the other hand, if there is no corresponding vitamin in step S58, it is judged whether or not there is a corresponding macronutrient (step S67). If there is a corresponding macronutrient, the operation proceeds to steps S68 and S69. To execute the macronutrient survey routine (step S6).
8, S69).

If there is no corresponding macronutrient in step S67, the following routine for consolidating survey results is executed (step S70), and when the analysis for all minerals is completed (step S71), the survey is finally omitted. The inspection routine is executed (step S72), and all the processes are ended. Incidentally, each processing procedure in steps S51 to S57 and step S70 is shown in FIGS. 45 to 53.

By the above processing, the subjective symptom analysis image data file shown in FIGS. 40 and 41 is created.

That is, the above-mentioned subjective symptom cause estimating unit 8
The outline of the treatment process in is as follows.

The subjective symptom cause estimating unit 8 extracts the nutrients directly acting on the place where the subjective symptom occurs, by referring to the symptom master by nutrient (FIG. 7), and extracts the extracted nutrients as macronutrients. And vitamins and minerals.
Then, the presence or absence of a disease in the storage location is checked in order from the mineral with the highest required amount by the subject data, and if there is a disease, the storage location dysfunction of related nutrients is recorded as one of the causes of subjective symptoms.

Further, it is checked whether the subjective symptom to be treated is a symptom of excessive minerals or a deficiency of related minerals, and then, the individual / nutrition / overage already created. Check for a match with the scarcity data. And
If they match, record the lack of the relevant mineral as the cause of the subjective symptom.

Next, the excess adjusted hormone name and the insufficient adjusted hormone name are extracted in order from the most used mineral, and the presence or absence of a disease in the secretory organs of the extracted adjusted hormone is checked by the subject data. If there is a disease, the dysfunction of the secretory hormone secretory site is recorded as one of the causes of subjective symptoms.

As described above, the subjective symptom cause estimating unit 8 analyzes the range of influence including the functional region of the fulfilled nutrients based on the nutrients in the current subject's excess and deficiency states, thereby It is possible to estimate the meaning of the deficiency state, and it is also possible to estimate the state of the entire physiological action at the cellular level.

In other words, the subjective symptom cause estimating unit 8 conceptually determines that the cause of the subjective symptom is not simply excess or deficiency of nutrients, but the direct cause is an dysfunction of any physiological action for maintaining life activity. Is an internal processing process that builds a thought path that verifies the set hypothesis. In addition, since the disease symptom master including infectious diseases is built in, it is possible to infer the possibility that the cause is other than nutrient deficiency. In addition, as a result of data processing at the physiological action level, the nutrients directly involved in the physiological action presumed to be the cause of subjective symptoms and indirectly involved in those nutrients required for normal functioning are associated. It is possible to infer the causal relationship including the nutrients that exist. In addition, each of the nutrients that was the subject of the investigation as a cause of the subjective symptom can be investigated as a cause of excess or deficiency in its absorption function, storage function, and adjustment function.

The data output unit 9 outputs the processing result of the required amount calculation unit 5 in the form of, for example, a nutrient required amount list for each individual, an exercise amount insufficiency degree estimation table, an obesity degree estimation table, etc. to calculate the intake amount. The processing result in the unit 6 is output, for example, in the form of a nutrient-specific estimated intake amount list for each individual, and the processing result in the excess / deficiency degree calculating unit 7 is output, for example, in the form of an individual-specific nutrient-specific excess / deficiency graph. To do. In addition, as a meal suggestion and an exercise suggestion, the general healthy person and the athlete are distinguished from the sick person,
Output in the form of recommended food intake and recommended intake list, recommended execution amount list by exercise item, recommended menu, etc. Further, the data processing result report shown in FIG. 54 shows the processing result in the subjective symptom cause estimating unit 8 in consideration of the processing results in the required amount calculating unit 5, the intake amount calculating unit 6, and the excess / deficiency degree calculating unit 7. , An estimated excess / deficiency degree list by nutrient shown in FIG. 55, a future disease prevention list shown in FIG. 56, and the like.

Here, the latent deficiency state entered in the column of the estimated excess / deficiency state in the list shown in FIG. 55 means that the relevant subjective symptom is not alive but the deficiency state is estimated. I mean. That is, it means that if left as it is, it will eventually lead to some symptom or disease. This latent deficiency state first collates subjective symptoms estimated to be related to the nutrient deficiency state estimated from personal data, and the nutrients in the estimated deficiency state fall within the range of data stored in the collation with subjective symptoms. If there is none, it is assumed to be a latent deficiency state.

(3) Analysis processing of bio-wave analysis data Next, the bio-abnormality occurrence part estimation unit 10 for estimating the presence or absence of an abnormality in each part of the body from the bio-wave analysis result of the substance separated and discharged from the human body. The processing operation will be described.

FIG. 57 shows the biological abnormality occurrence part estimation unit 10.
The biological wave analysis result data shown in is input from the data input unit 1. Therefore, the biological abnormality occurrence part estimation unit 10 estimates the presence / absence of an abnormality in each part from the numerical value indicating the bioactivity of each part in the body shown in the biological wave analysis result data.

Here, the biological wave analysis will be briefly described.

All substances are composed of atoms composed of atomic nuclei and electrons rotating around them. This rotation of the electrons can be understood as a wavelength, and the atomic structure emits a unique vibration. This vibration energy is a wave, and molecules such as an aggregate of atoms, cells, organs, and organs also generate unique waves. Harmful environment,
Substances and stress influence this wave and induce disturbance of the wave, causing illness.

In other words, the disease can be found by analyzing whether it is a normal wave.

In the biological wave analysis, the analysis result is ± 0.
It appears as a numerical value up to +21, and the closer it is to +21, the more normal it is judged.

That is, the range from ± 0 to +4 is currently
The range of +5 to +9, which is a range where some abnormality is predicted to require immediate improvement of the living body, is the range where it is predicted that some abnormality will appear in each site in the future, +10
The range of up to +21 is a range in which it is predicted that the wave of the living body will not be greatly disturbed at present.

The wave analysis item (wave analysis part) of the living body is
Actually, there are about 3000 or so, but in FIG. 57, there are 12 items for the nervous system, 15 items for the organ, 12 items for the disease, and 9 items from the hormone balance to the physical and mental region as analysis target items. There is. In particular, the 12 items of the nervous system are important organs that control biological functions, and the vital values of each nervous system (±
From the numerical values from 0 to +21), the activity of each part of the living body shown in FIG. 58 can be known.

In other words, the biological abnormality occurrence part estimation unit 10 detects some abnormality in the part within the range of ± 0 to +4 from the numerical value showing the bioactivity of each part in the body shown in the biological wave analysis result data. It is estimated that there is a part, +5
It is presumed that the part in the range up to +9 is a part where an abnormality may appear in the future.

These estimation results are sent to the matching degree judging section 11.

In the coincidence determination section 11, the subjective symptom estimated by the subjective symptom cause estimating section 8 (physiological action estimated as its cause: occurrence range) and the abnormality occurrence estimated by the biological abnormality occurrence site estimating section 10 are described. The estimated degree of coincidence is determined by comparing with the site.

If the estimation by the subjective symptom cause estimating unit 8 follows a correct route, the two estimation results should almost match, and the subjective symptom cause estimating unit 8 can confirm the matching degree. The reliability of the estimation result of, that is, the reliability of the entire system is improved.

For the non-coincidence portion, the data output unit 9 outputs the non-coincidence data as the non-coincidence data to further improve the reliability of the system such as the investigation of the cause of the non-coincidence data and the estimation of a new abnormality occurrence site. Can contribute.

[0109]

INDUSTRIAL APPLICABILITY The disease prevention multi-check system of the present invention is used for a disease condition master, a drug master, a physiological action master, a nutrient master, an external factor master, a food master, a menu master, a subjective symptom master, a nutritional symptom master, and exercise intensity. Enter various masters such as master, life activity intensity calculation master, energy requirement master and nutrition requirement master, and various data such as basic item data, exercise content data, diet content data, subjective symptom data and hair test data. According to the individual file creation unit that creates individual files based on the various masters, and the data entered in the created individual files, the required amount for each nutrient is calculated for each individual based on each master. Based on the required amount calculation unit and each master, The insufficiency degree that calculates the excess or deficiency degree for each individual nutrient by obtaining the difference between the intake amount calculation unit that calculates the intake amount and the calculated required amount and the corresponding intake amount The calculation unit and the place and range in the body where the subjective symptom is generated based on the subjective symptom master, and the nutrient groups involved in the obtained place and range are extracted from the nutrient-specific symptom master, and By analyzing the storage function and adjusting function in the body of the extracted nutrient group, the subjective symptom cause estimation unit that estimates the cause of the subjective symptom together with the excess and deficiency degree of each nutrient calculated by the excess and deficiency degree calculation unit And the analysis results of biological wave analysis of substances separated and discharged from the human body as input data,
From the numerical value showing the bioactivity of each site in the body shown in this input data, the biological abnormality occurrence site estimation unit to estimate the presence or absence of abnormality of each site, and the subjective symptoms estimated by the subjective symptom cause estimation unit and By comparing with the abnormality occurrence site estimated by the biological abnormality occurrence site estimation unit,
It is configured to include a matching degree determination unit that determines the estimated matching degree. In other words, a system that can estimate the nutritional excess and deficiency status and model the so-called physiological effects of vitamins and minerals acting on the cellular level of the human body, and the causal relationship between subjective symptoms and nutrient deficiency status Since it has been constructed, it is possible to make more accurate recommendations for disease prevention through a processing process that is similar to the inspection and consideration process by which an expert such as a doctor makes a diagnosis. In addition to the two-stage structure of the part that calculates the excess and deficiency of nutrients and the part that estimates the cause of subjective symptoms, biological vibrations of substances (eg, hair, nails, urine, etc.) separated and discharged from the human body are also included. From the analysis results, the configuration that includes the part that estimates the presence or absence of abnormality in each part in the body is compared, so the results obtained by the subjective symptom cause estimation part and the results obtained by the biological abnormality occurrence part estimation part are compared. By performing the above, the accuracy and reliability of the check system are improved.

[Brief description of the drawings]

FIG. 1 is a block diagram showing an electrical configuration of a disease prevention multi-check system of the present invention.

FIG. 2 is a table showing an example of data contents of a medical condition master.

FIG. 3 is a table showing an example of data contents of a prescription drug master.

FIG. 4 is a table showing an example of data content of a nutrient master.

FIG. 5 is a table showing an example of data contents of an external factor master.

FIG. 6 is a table showing an example of data contents of a food master.

FIG. 7 is a table showing an example of data contents of a nutrient-based symptom master.

FIG. 8 is a table showing an example of data content of an exercise intensity master.

FIG. 9 is a table showing an example of data contents of an exercise intensity master.

FIG. 10 is a table showing an example of data contents of a living activity intensity calculation master.

FIG. 11 is a table showing an example of data content of an energy requirement master.

FIG. 12 is a table showing an example of data contents of a nutritional requirement master.

[Fig. 13] Basic item input table displayed on the screen (basic items)
FIG.

FIG. 14: Basic item input table displayed on the screen (external factor)
FIG.

FIG. 15 is a flowchart showing an input processing procedure of basic item data.

FIG. 16 is a flowchart showing an input processing procedure of basic item data.

FIG. 17: Basic item input table displayed on screen (exercise content)
FIG.

FIG. 18 is a diagram showing a basic item input table (sports content) displayed on the screen.

FIG. 19 is a flowchart showing an input processing procedure of exercise content data.

FIG. 20 is a diagram showing a meal content input table displayed on the screen.

FIG. 21 is a diagram showing a food group code table.

FIG. 22 is a flowchart showing an input processing procedure of meal content data.

[Fig. 23] Fig. 23 is a diagram showing a subjective symptom input table range specification displayed on the screen.

[Fig. 24] Input table of subjective symptoms displayed on the screen (mental state)
FIG.

FIG. 25 is a flowchart showing a procedure for inputting subjective symptom data.

FIG. 26 is a diagram showing a hair inspection result input table displayed on the screen.

FIG. 27 is a diagram showing an example of a hair inspection result report.

FIG. 28 is a flowchart showing a procedure for inputting hair inspection data.

FIG. 29 is a diagram showing examination target data.

FIG. 30 is a diagram showing an excess / deficiency estimation processing image data file.

FIG. 31 is a diagram showing an excess / deficiency estimation processing image data file.

FIG. 32 is a flowchart showing an excess / deficiency estimation processing procedure.

FIG. 33 is a flowchart showing an excess / deficiency estimation processing procedure.

FIG. 34 is a diagram showing an excess / deficiency estimation processing image data file.

FIG. 35 is a flowchart showing an excess / deficiency estimation processing procedure.

FIG. 36 is a diagram showing an excess / deficiency estimation processing image data file.

FIG. 37 is a flowchart showing an excess / deficiency estimation processing procedure.

FIG. 38 is a flowchart showing an excess / deficiency estimation processing procedure.

FIG. 39 is a diagram showing an excess / deficiency estimation processing image data file.

FIG. 40 is a diagram showing a subjective symptom analysis image data file.

FIG. 41 is a diagram showing a subjective symptom analysis image data file.

FIG. 42 is a flowchart showing a subjective symptom analysis processing procedure.

FIG. 43 is a flowchart showing a subjective symptom analysis processing procedure.

FIG. 44 is a flowchart showing a subjective symptom analysis processing procedure.

FIG. 45 is a diagram showing an absorption site inspection routine.

FIG. 46 is a diagram showing a storage location investigation routine.

FIG. 47 is a diagram showing a drug influence investigation routine.

FIG. 48 is a diagram showing an antagonistic relationship investigation routine.

FIG. 49 is a diagram showing an excess / deficiency degree verification routine.

FIG. 50 is a diagram showing an excess / deficiency degree verification routine.

FIG. 51 is a diagram showing a chain factor investigation routine.

FIG. 52 is a diagram showing a chain factor investigation routine.

FIG. 53 is a diagram showing an investigation result collecting routine.

FIG. 54 is a diagram showing an example of a data processing result report output from the data output unit.

FIG. 55 is a diagram showing an example of a nutrient-specific estimated excess / deficiency degree list output from a data output unit.

FIG. 56 is a diagram showing an example of a future disease prevention list output from the data output unit.

FIG. 57 is a table showing an example of biological wave analysis result data.

FIG. 58 is a table showing a correspondence relationship between each nervous system and an abnormality occurrence site of a living body, which can be known by a numerical value of vitality.

[Explanation of symbols]

 1 data input section 2 file creation section 3 file storage section 4 various master storage section 5 required amount calculation section 6 intake calculation section 7 excess / deficiency degree calculation section 8 subjective symptom cause estimation section 9 data output section 10 biological abnormality occurrence site estimation section 11 Matching degree determination unit

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[Procedure amendment]

[Submission date] February 5, 1996

[Procedure amendment 1]

[Document name to be amended] Statement

[Correction target item name] Brief description of drawings

[Correction method] Change

[Correction contents]

[Brief description of the drawings]

FIG. 1 is a block diagram showing an electrical configuration of a disease prevention multi-check system of the present invention.

FIG. 2 is a chart showing an example of data contents of a medical condition master.

FIG. 3 is a table showing an example of data contents of a prescription drug master.

FIG. 4 is a chart showing an example of data contents of a nutrient master.

FIG. 5 is a chart showing an example of data contents of an external factor master.

FIG. 6 is a chart showing an example of data contents of a food master.

FIG. 7 is a table showing an example of data contents of a nutrient-based symptom master.

FIG. 8 is a chart showing an example of data contents of an exercise intensity master.

FIG. 9 is a chart showing an example of data contents of an exercise intensity master.

FIG. 10 is a chart showing an example of data contents of a living activity intensity calculation master.

FIG. 11 is a chart showing an example of data content of an energy requirement master.

FIG. 12 is a chart showing an example of data contents of a nutritional requirement master.

[Fig. 13] Basic item input table displayed on the screen (basic items)
FIG.

FIG. 14: Basic item input table displayed on the screen (external factor)
FIG.

FIG. 15 is a flowchart showing an input processing procedure of basic item data.

FIG. 16 is a flowchart showing an input processing procedure of basic item data.

FIG. 17: Basic item input table displayed on screen (exercise content)
FIG.

FIG. 18 is a diagram showing a basic item input table (sports content) displayed on the screen.

FIG. 19 is a flowchart showing an input processing procedure of exercise content data.

FIG. 20 is a diagram showing a meal content input table displayed on the screen.

FIG. 21 is a diagram showing a food group code table.

FIG. 22 is a flowchart showing an input processing procedure of meal content data.

[Fig. 23] Fig. 23 is a diagram showing a subjective symptom input table range specification displayed on the screen.

[Fig. 24] Input table of subjective symptoms displayed on the screen (mental state)
FIG.

FIG. 25 is a flowchart showing a procedure for inputting subjective symptom data.

FIG. 26 is a diagram showing a hair inspection result input table displayed on the screen.

FIG. 27 is a diagram showing an example of a hair inspection result report.

FIG. 28 is a flowchart showing a procedure for inputting hair inspection data.

FIG. 29 is a diagram showing examination target data.

FIG. 30 is a diagram showing an excess / deficiency estimation processing image data file.

FIG. 31 is a diagram showing an excess / deficiency estimation processing image data file.

FIG. 32 is a flowchart showing an excess / deficiency estimation processing procedure.

FIG. 33 is a flowchart showing an excess / deficiency estimation processing procedure.

FIG. 34 is a diagram showing an excess / deficiency estimation processing image data file.

FIG. 35 is a flowchart showing an excess / deficiency estimation processing procedure.

FIG. 36 is a diagram showing an excess / deficiency estimation processing image data file.

FIG. 37 is a flowchart showing an excess / deficiency estimation processing procedure.

FIG. 38 is a flowchart showing an excess / deficiency estimation processing procedure.

FIG. 39 is a diagram showing an excess / deficiency estimation processing image data file.

FIG. 40 is a diagram showing a subjective symptom analysis image data file.

FIG. 41 is a diagram showing a subjective symptom analysis image data file.

FIG. 42 is a flowchart showing a subjective symptom analysis processing procedure.

FIG. 43 is a flowchart showing a subjective symptom analysis processing procedure.

FIG. 44 is a flowchart showing a subjective symptom analysis processing procedure.

FIG. 45 is a diagram showing an absorption site inspection routine.

FIG. 46 is a diagram showing a storage location investigation routine.

FIG. 47 is a diagram showing a drug influence investigation routine.

FIG. 48 is a diagram showing an antagonistic relationship investigation routine.

FIG. 49 is a diagram showing an excess / deficiency degree verification routine.

FIG. 50 is a diagram showing an excess / deficiency degree verification routine.

FIG. 51 is a diagram showing a chain factor investigation routine.

FIG. 52 is a diagram showing a chain factor investigation routine.

FIG. 53 is a diagram showing an investigation result collecting routine.

FIG. 54 is a diagram showing an example of a data processing result report output from the data output unit.

FIG. 55 is a diagram showing an example of a nutrient-specific estimated excess / deficiency degree list output from a data output unit.

FIG. 56 is a diagram showing an example of a future disease prevention list output from the data output unit.

FIG. 57 is a chart showing an example of biological wave analysis result data.

FIG. 58 is a table showing a correspondence relationship between each nervous system and an abnormality occurrence site of a living body, which can be known by a numerical value of vitality.

[Explanation of symbols] 1 data input unit 2 file creation unit 3 file storage unit 4 various master storage units 5 required amount calculation unit 6 intake amount calculation unit 7 excess / deficiency degree calculation unit 8 subjective symptom cause estimation unit 9 data output unit 10 living body Abnormality occurrence part estimation unit 11 Matching degree determination unit

Claims (1)

[Claims] 1. A medical condition master that stores each data such as disease name, symptom, complication, prescription drug, relation between physiological action involved and nutrient, drug name, indication symbol, efficacy, target symptom, side effect, target disease Name, drug limiting condition, inhibitory nutrients, and other drug masters are stored, along with physiological action names, participating nutrient names, produced enzyme names, action principles, subjective symptoms, range of occurrence, disease, antagonist ratio, etc. The physiological action master, the nutrient master that stores data such as nutrient name, intake reference amount, excessive risk value, dangerous value symptom, cause of deficiency, cause of deficiency, physiological action involved, food of supplement source, intake restriction, and external External factor master that stores data such as nutrition status influence item, affected nutrient name, affected physiological action name, food name, contained nutrient name, nutrient content, energy amount,
A food master that stores each data such as intake restriction conditions, and a menu that stores each data such as menu name, used food name, used food amount, food-based cooking method, food-based cooking time, standard energy amount, standard nutrition amount, etc. A master, a subjective symptom master that stores each data such as the range of occurrence and subjective symptoms, and a nutrient-specific symptom master that stores each data such as the coordination between the lack and excess of nutrients and related physiological actions by nutrients, Exercise intensity master that stores the value of energy metabolism rate (RMR) according to the type of exercise, and life activity intensity calculation master that stores each data such as RMR total calculation formula, life activity intensity index calculation formula, life activity intensity classification, etc. And an energy requirement master that stores data such as basal metabolism, energy requirement, basal metabolism reference value, energy requirement per body weight, and life activity strength A nutritional requirement master that stores the required amount of each nutrient according to the degree classification, and basic item data such as height, weight, age, disease status, medications, exercise content data, diet content data, subjective symptom data, and hair examination By inputting the data, the personalized file creation section that creates individualized files based on each of the masters and the data entered in the individualized file created by this individualized file creation section are used to determine the intensity of the living activity. Based on the calculation master, the energy requirement master, the nutrition requirement master and the nutrient master, a requirement calculation unit that calculates the requirement for each nutrient for each individual, the food master, the nutrient master, and the nutrient An intake amount calculating unit for calculating an intake amount for each nutrient based on the symptom master and the drug master; An excess / deficiency degree calculation for calculating the excess / deficiency level for each individual nutrient by obtaining the difference between the required amount calculated by the required amount calculation section and the corresponding intake amount calculated by the intake amount calculation section Part, and obtain the location and range in the body where the subjective symptoms are generated based on the subjective symptom master, extract the nutrient group involved in the obtained location and range from the nutrient-specific symptom master, By analyzing the storage function and the adjusting function in the body for this extracted nutrient group, the cause of the subjective symptom to estimate the cause of the subjective symptom together with the excess or deficiency degree of each nutrient calculated by the excess or deficiency degree calculation unit. The estimation part and the analysis results of the biological wave analysis of the substances separated and discharged from the human body are used as input data, and from the numerical value showing the bioactivity of each part in the body shown in this input data, A biological abnormality location estimating section for estimating the presence or absence of, and symptoms estimated by the subjective symptoms cause estimation part,
A disease prevention multi-check system, comprising: a coincidence degree determination unit that determines the degree of coincidence of estimation by comparing with the abnormality occurrence site estimated by the biological abnormality occurrence site estimation unit.