阅读说明：本技术 过敏处方搜索系统和方法以及过敏处方搜索程序 (Allergy prescription search system and method, and allergy prescription search program ) 是由 岩田淳 于 2018-03-30 设计创作，主要内容包括：能够不经由专家而根据花粉症的患者的症状等自动搜索最适合的处方。其特征在于,具有如下步骤：输入步骤,输入实际的花粉症的症状；以及搜索步骤,参照存储于数据库(3)的花粉症的各症状与该各症状的处方之间的3个阶段以上的第1关联度,根据在输入步骤中输入的症状,搜索1个以上的处方,数据库(3)在新取得花粉症的各症状与该症状的处方的关系的情况下,通过使该关系反映在第1关联度中而进行更新。(The most suitable prescription can be automatically searched for from the symptoms of the patient with pollinosis or the like without going through a specialist. The method is characterized by comprising the following steps: an input step of inputting the actual symptoms of pollinosis; and a search step of searching for 1 or more prescriptions based on the symptoms input in the input step by referring to 1 st degree of association of 3 stages or more between each symptom of pollinosis and the prescription of each symptom stored in the database (3), wherein the database (3) is updated by reflecting the relationship between each symptom of pollinosis and the prescription of the symptom in the 1 st degree of association when the relationship is newly acquired.)

1. An allergy prescription search system comprising:

a database in which 1 st correlation degrees of 3 stages or more between each symptom of an allergy and a prescription for each symptom of the allergy are stored in advance;

an input unit that inputs an actual allergy symptom; and

a search means for searching for 1 or more prescriptions from the symptom input by the input means with reference to the 1 st relevance stored in the database;

the database stores in advance the 2 nd correlation degrees of 3 stages or more between each improvement symptom of the prescription previously performed and the new prescription of the patient who performed the prescription, which are searched by the search unit,

the input unit inputs actual improvement symptoms of a patient who has administered a previously administered prescription,

the search means searches for 1 or more new prescriptions based on the previously implemented prescription and the improvement symptom input through the input means with reference to the 2 nd degree of association stored in the database.

2. An allergy prescription search system comprising:

a database in which 3 or more stages of 2 nd correlation between each improvement symptom of a previously prescribed patient and a new prescription are stored in advance;

an input unit that inputs a prescription that has been actually performed before and actual improvement symptoms of a patient who has performed the prescription; and

and a search means for searching for 1 or more new prescriptions based on the previously performed prescription and the improvement symptom input by the input means with reference to the 2 nd degree of correlation stored in the database.

3. Allergy prescription search system according to claim 1 or 2,

when a relationship between each of the improvement symptoms of the prescription previously administered and the patient who administered the prescription and the new prescription is obtained, the database is updated by reflecting the relationship in the 2 nd degree of association.

4. The allergy prescription search system according to claim 3,

the 2 nd degree of association is constituted by a neural network,

the database is updated by performing the reflection on the 2 nd association degree by using artificial intelligence.

5. An allergy prescription search method is characterized by comprising the following steps:

an input step of inputting actual allergy symptoms;

a search step of searching for 1 or more prescriptions based on the symptoms input in the input step, with reference to 1 st degree of correlation between each symptom of an allergy and a prescription of each symptom of the allergy, the 1 st degree of correlation being 3 or more stages between each symptom of the allergy and the prescription stored in the database;

a 2 nd correlation degree obtaining step of obtaining 2 nd correlation degrees of 3 stages or more between each of the improvement symptoms of the prescription previously administered and the patient administered the prescription searched in the searching step and a new prescription;

an improvement symptom input step of inputting actual improvement symptoms of a patient who has performed a prescription previously performed; and

a new prescription searching step of searching for 1 or more new prescriptions based on a prescription previously carried out and the improvement symptom input in the improvement symptom inputting step with reference to the 2 nd correlation degree acquired in the 2 nd correlation degree acquiring step,

wherein each of the steps is executed by a computer.

6. An allergy prescription search method is characterized by comprising the following steps:

a 2 nd correlation degree obtaining step of obtaining 2 nd correlation degrees of 3 stages or more between each of the improvement symptoms of the prescription previously performed and the patient who performed the prescription and a new prescription;

an improvement symptom input step of inputting a prescription actually carried out before and an actual improvement symptom of a patient who carried out the prescription; and

a new prescription searching step of searching for 1 or more new prescriptions based on the prescription and the improvement symptom input in the improvement symptom inputting step and previously performed with reference to the 2 nd degree of correlation obtained in the 2 nd degree of correlation obtaining step,

wherein each of the steps is executed by a computer.

7. The allergy prescription search method according to claim 5 or 6,

the allergy prescription search method includes an update step of updating, when a relationship between each of improvement symptoms of a prescription previously administered and a patient administered the prescription and a new prescription is obtained, the relationship by reflecting the relationship in the 2 nd correlation degree.

8. The allergy prescription search method according to claim 7,

in the updating step, the updating is performed by performing the reflection with respect to the 2 nd degree of association constituted by a neural network using artificial intelligence.

9. An allergy prescription search program for causing a computer to execute the steps of:

an input step of inputting actual allergy symptoms;

a search step of searching for 1 or more prescriptions based on the symptoms input in the input step, with reference to 1 st degree of correlation between each symptom of an allergy and a prescription of each symptom of the allergy, the 1 st degree of correlation being 3 or more stages between each symptom of the allergy and the prescription;

a 2 nd correlation degree obtaining step of obtaining 2 nd correlation degrees of 3 stages or more between each of the improvement symptoms of the prescription previously administered and the patient administered the prescription searched in the searching step and a new prescription;

an improvement symptom input step of inputting actual improvement symptoms of a patient who has performed a prescription previously performed; and

a new prescription searching step of searching for 1 or more new prescriptions based on the prescription previously carried out and the improvement symptom input in the improvement symptom inputting step with reference to the 2 nd correlation degree acquired in the 2 nd correlation degree acquiring step.

10. An allergy prescription search program for causing a computer to execute the steps of:

a 2 nd correlation degree obtaining step of obtaining 2 nd correlation degrees of 3 stages or more between each of the improvement symptoms of the prescription previously performed and the patient who performed the prescription and a new prescription;

an improvement symptom input step of inputting a prescription actually carried out before and an actual improvement symptom of a patient who carried out the prescription; and

a new prescription searching step of searching for 1 or more new prescriptions based on the prescription and the improvement symptom input in the improvement symptom inputting step and previously performed with reference to the 2 nd degree of correlation obtained in the 2 nd degree of correlation obtaining step.

11. The allergy prescription search program according to claim 9 or 10,

the allergy prescription search program includes an update step of, when a relationship between each of improvement symptoms of a prescription previously performed and a patient who performed the prescription and a new prescription is obtained, reflecting the relationship in the 2 nd correlation and updating the relationship.

12. The allergy prescription search program according to claim 11,

in the updating step, the updating is performed by performing the reflection with respect to the 2 nd degree of association constituted by a neural network using artificial intelligence.

13. A prescription search system, comprising:

a database in which 1 st association degrees of 3 stages or more between combinations of 1 or more of each symptom, each lifestyle habit, and each attribute information, and prescriptions of the combinations are stored in advance;

an input unit that inputs information constituting the combination; and

a search means for searching for 1 or more prescriptions based on the information input by the input means with reference to the 1 st relevance stored in the database,

the database stores in advance the 2 nd correlation degrees of 3 stages or more between each of the previously performed prescription searched by the search means and each of the symptoms of change of the patient who performed the prescription and the new prescription,

the input unit inputs actual symptoms of change of a patient who has performed a previously performed prescription,

the search means searches for 1 or more new prescriptions based on the previously implemented prescription and the change symptom input through the input means with reference to the 2 nd degree of association stored in the database.

14. A prescription search system, comprising:

a database in which 3 or more stages of 2 nd association degrees between each change symptom and a new prescription from the past and the past lifestyle habits are stored in advance;

an input unit which inputs an actual lifestyle habit and an actual change symptom of a patient who has performed the lifestyle habit; and

and a search means for searching for 1 or more new prescriptions based on the past lifestyle habits and the change symptoms inputted by the input means with reference to the 2 nd degree of association stored in the database.

15. A prescription search program for causing a computer to execute the steps of:

a1 st association degree acquisition step of acquiring in advance 1 st association degrees of 3 stages or more between a combination of 1 or more of each symptom and each lifestyle habit and each attribute information and a prescription of the combination;

an input step of inputting information constituting the combination;

a search step of searching for 1 or more prescriptions based on the information input in the input step with reference to the 1 st relevance obtained in the 1 st relevance obtaining step;

a 2 nd correlation degree obtaining step of obtaining 2 nd correlation degrees of 3 stages or more between each of the prescription previously administered and the patient who administered the prescription, which are searched in the searching step, and a new prescription;

a change symptom input step of inputting an actual change symptom of the patient who has performed the prescription previously performed; and

a new prescription searching step of searching for 1 or more new prescriptions based on the prescription previously carried out and the change symptom input in the change symptom inputting step with reference to the 2 nd degree of correlation obtained in the 2 nd degree of correlation obtaining step.

16. A prescription search program characterized by executing, with a computer, the steps of:

a 2 nd association degree obtaining step of obtaining 2 nd association degrees of 3 stages or more between the previous lifestyle habits and each change symptom from the previous time and the new prescription;

a change symptom input step of inputting actual living habits and actual change symptoms of patients who have performed the living habits; and

a new prescription search step of searching for 1 or more new prescriptions based on the past lifestyle habits and the change symptoms input in the change symptom input step with reference to the 2 nd degree of relevance acquired in the 2 nd degree of relevance acquisition step.

17. A prescription search method, characterized in that the prescription search method comprises the steps of:

a1 st association degree acquisition step of acquiring in advance 1 st association degrees of 3 stages or more between a combination of 1 or more of each symptom and each lifestyle habit and each attribute information and a prescription of the combination;

an input step of inputting information constituting the combination;

a search step of searching for 1 or more prescriptions based on the information input in the input step with reference to the 1 st relevance obtained in the 1 st relevance obtaining step;

a 2 nd correlation degree obtaining step of obtaining 2 nd correlation degrees of 3 stages or more between each symptom of change of the patient who has performed the prescription and the prescription previously performed and the new prescription searched in the searching step;

a change symptom input step of inputting an actual change symptom of the patient who has performed the prescription previously performed; and

a new prescription searching step of searching for 1 or more new prescriptions based on a prescription previously carried out and the change symptom input in the change symptom inputting step with reference to the 2 nd correlation degree obtained in the 2 nd correlation degree obtaining step,

wherein each of the steps is performed by a computer.

18. A prescription search method, characterized in that the prescription search method comprises the steps of:

a 2 nd association degree obtaining step of obtaining 2 nd association degrees of 3 stages or more between the previous lifestyle habits and each change symptom from the previous time and the new prescription;

a change symptom input step of inputting actual living habits and actual change symptoms of patients who have performed the living habits; and

a new prescription search step of searching for 1 or more new prescriptions based on the past lifestyle habits and the change symptoms input in the change symptom input step with reference to the 2 nd degree of relevance acquired in the 2 nd degree of relevance acquisition step,

wherein each of the steps is executed by a computer.

Technical Field

The present invention relates to an allergy prescription search system and method and an allergy prescription search program which are suitable for searching for an optimum prescription based on symptoms of allergy patients including pollinosis and the like.

Background

Pollinosis is a relatively common allergic disease peculiar to japan, and is caused by contact of pollen of plants such as cedar with mucous membranes such as the nose and eyes. Among them, cedar pollen has an n-particle diameter of about 20 to 40 μm, and when the cedar pollen adheres to the mucous membrane of the nose, it causes symptoms such as sneezing, rhinorrhea, nasal obstruction, and the like. In addition, when pollen enters the eyes, symptoms such as itching of the eyes are caused. Because of the symptoms of these pollinosis, patients suffer significant discomfort for a long period of time, with more adverse effects on daily life and work.

Therefore, various agents for alleviating the symptoms of pollinosis have been developed (see, for example, patent documents 1 and 2). In addition, various masks have been developed to avoid inhalation of pollen (see, for example, patent document 3).

However, these agents are not taken in such a nature that they fundamentally treat pollinosis, but only temporarily alleviate the symptoms. Therefore, the mitigating effect exerted by the drug is weakened every time and also has to be continuously taken by the patient. In addition, the mask only physically prevents the inhalation of pollen, and the mask does not fundamentally treat pollinosis.

Generally, the amount of pollen scattered from the cedar is large for 3 to 4 months, and a patient is burdened by continuously taking a medicine for alleviating symptoms or continuously wearing a mask during this period. Further, even when the season of pollen scattering ends, pollinosis is not fundamentally treated, and therefore, when the season comes every year, a medicine for alleviating symptoms has to be continuously taken, and a mask has to be continuously worn.

It is estimated that there are nearly 3000 million people in patients with pollinosis in japan. The desire of these patients is not to temporarily alleviate pollinosis or to merely physically block pollen, but rather to be treated at a fundamental level. In order to fundamentally treat this pollinosis, it is important to gradually improve physical constitution and lifestyle habits by a prescription represented by diet, health products, exercise, sleep, and the like, regardless of the drug.

In addition, various studies have been made in order to try to provide a prescription that is optimal for various diseases such as diabetes, cancer, hypertension, and the like, and various symptoms of alopecia. In addition, various studies have been conducted in an attempt to provide a prescription most suitable for various symptoms of facial spots, acne, skin laxity, and rough skin, which may affect the appearance.

The most suitable prescription for these various symptoms is largely influenced by attribute information including the intake of the patient in the dietary life, the age, sex, heredity, and the like of the patient. Therefore, in order to provide an optimal prescription for each patient, it is necessary to detect the intake or attribute information for each patient, and provide an optimal prescription in consideration of the relationship between the detected intake and attribute information.

However, the patient's intake and attribute information each involve many aspects, up to millions of these combinations, and enormous quantities. Therefore, it is currently extremely difficult to manually search for a prescription that is most suitable for each of the ingesta, the attribute information, and a combination thereof.

Conventionally, a medical support system for checking interaction of prescription drugs with respect to attribute information of a patient has been disclosed. In addition, the following techniques are proposed: the degree of suitability of a prescription for a symptom is quantified and stored in a computer, and a prescription most suitable for a patient is proposed based on the inputted symptom of the patient and the degree of suitability (for example, see patent document 4).

Disclosure of Invention

Problems to be solved by the invention

However, in order to improve the constitution and the living habits, it is necessary to select the most suitable prescription in accordance with the symptoms of the patient and the living habits of the patient. The selection of the most suitable prescription requires professional knowledge and therefore the advice of an expert with this knowledge. Therefore, there are problems as follows: it takes time and cost to get the expert's advice each time the most appropriate prescription is selected against the patient's symptoms.

In addition, it is needless to say that the prescriptions given by the experts are different from each other in the same symptom in many cases, considering that the knowledge and skill of the experts are different. The difference in advice between experts sometimes causes confusion in the patient and therefore requires homogenization of the prescription presented to the patient. Thus, there are problems as follows: when homogenization is achieved for such a proposed recipe, there is no effective countermeasure for the improvement, and therefore the speed and accuracy of improvement become low.

The present invention has been made in view of the above problems, and an object of the present invention is to provide an allergy prescription search system and method, and an allergy prescription search program, which can automatically search for an optimum prescription based on symptoms of an allergic patient including pollinosis and the like without the aid of experts. Further, it is an object of the present invention to provide a prescription search system and method and a prescription search program which can propose a prescription that is most suitable for each symptom, each ingestion and each combination of attribute information, and particularly can search for a prescription that is most suitable for each symptom of baldness and each symptom that affects the appearance.

Means for solving the problems

An allergy prescription search system according to the present invention is characterized by comprising: a database in which 1 st correlation degrees of 3 stages or more between each symptom of an allergy and a prescription for each symptom of the allergy are stored in advance; an input unit that inputs an actual allergy symptom; and a search means for searching for 1 or more prescriptions based on the symptom input by the input means with reference to the 1 st relevance stored in the database; the database stores in advance a 2 nd degree of correlation between each of the improvement symptoms of the prescription previously performed and the patient who performed the prescription, which is searched by the search means, and a new prescription, which is 3 or more stages, the input means inputs an actual improvement symptom of the patient who performed the prescription previously performed, and the search means searches for 1 or more new prescriptions based on the prescription previously performed and the improvement symptom input by the input means, with reference to the 2 nd degree of correlation stored in the database.

An allergy prescription search system according to the present invention is characterized by comprising: a database in which 3 or more stages of 2 nd correlation between each improvement symptom of a previously prescribed patient and a new prescription are stored in advance; an input unit that inputs a prescription that has been actually performed before and actual improvement symptoms of a patient who has performed the prescription; and a search means for searching for 1 or more new prescriptions based on the prescription and the symptom of improvement that have been previously performed and are input by the input means, with reference to the 2 nd degree of correlation stored in the database.

The allergy prescription search method of the present invention is characterized by comprising the steps of: an input step of inputting actual allergy symptoms; a search step of searching for 1 or more prescriptions based on the symptoms input in the input step, with reference to 1 st degree of correlation between each symptom of an allergy and a prescription of each symptom of the allergy, the 1 st degree of correlation being 3 or more stages between each symptom of the allergy and the prescription stored in the database; a 2 nd correlation degree obtaining step of obtaining 2 nd correlation degrees of 3 stages or more between each of the improvement symptoms of the prescription previously administered and the patient administered the prescription searched in the searching step and a new prescription; an improvement symptom input step of inputting actual improvement symptoms of a patient who has performed a prescription previously performed; and a new prescription searching step of searching for 1 or more new prescriptions based on the prescription previously carried out and the improvement symptom input in the improvement symptom inputting step with reference to the 2 nd correlation degree acquired in the 2 nd correlation degree acquiring step,

executing each step by a computer.

The allergy prescription search method of the present invention is characterized by comprising the steps of: a 2 nd correlation degree obtaining step of obtaining 2 nd correlation degrees of 3 stages or more between each of the improvement symptoms of the prescription previously performed and the patient who performed the prescription and a new prescription; an improvement symptom input step of inputting a prescription actually carried out before and an actual improvement symptom of a patient who carried out the prescription; and a new prescription searching step of searching for 1 or more new prescriptions based on the prescription and the improvement symptom input in the improvement symptom inputting step and previously performed with reference to the 2 nd degree of relevance acquired in the 2 nd degree of relevance acquiring step, and executing each step by a computer.

An allergy prescription search program according to the present invention is characterized by causing a computer to execute: an input step of inputting actual allergy symptoms; a search step of searching for 1 or more prescriptions based on the symptoms input in the input step, with reference to 1 st degree of correlation between each symptom of an allergy and a prescription of each symptom of the allergy, the 1 st degree of correlation being 3 or more stages between each symptom of the allergy and the prescription; a 2 nd correlation degree obtaining step of obtaining 2 nd correlation degrees of 3 stages or more between each of the improvement symptoms of the prescription previously administered and the patient administered the prescription searched in the searching step and a new prescription; an improvement symptom input step of inputting actual improvement symptoms of a patient who has performed a prescription previously performed; and a new prescription searching step of searching for 1 or more new prescriptions based on the prescription previously carried out and the improvement symptom input in the improvement symptom inputting step with reference to the 2 nd correlation degree acquired in the 2 nd correlation degree acquiring step.

An allergy prescription search program according to the present invention is characterized by causing a computer to execute: a 2 nd correlation degree obtaining step of obtaining 2 nd correlation degrees of 3 stages or more between each of the improvement symptoms of the prescription previously performed and the patient who performed the prescription and a new prescription; an improvement symptom input step of inputting a prescription actually carried out before and an actual improvement symptom of a patient who carried out the prescription; and a new prescription searching step of searching for 1 or more new prescriptions based on the prescription and the improvement symptom input in the improvement symptom inputting step and previously performed with reference to the 2 nd degree of relevance acquired in the 2 nd degree of relevance acquiring step.

The prescription search system to which the present invention is applied is characterized by comprising: a database in which 1 st association degrees of 3 stages or more between combinations of 1 or more of any of symptoms, lifestyle habits, and attribute information and prescriptions of the combinations are stored in advance; an input unit that inputs information constituting the combination; and a search unit which searches for 1 or more prescriptions based on the information input by the input unit with reference to a1 st degree of association stored in the database, wherein the database stores in advance 3 or more 2 nd degrees of association between a previously performed prescription searched by the search unit and each change symptom of a patient who has performed the prescription and a new prescription, the input unit inputs an actual change symptom of the patient who has performed the previously performed prescription, and the search unit searches for 1 or more new prescriptions based on the previously performed prescription and the change symptom input by the input unit with reference to the 2 nd degree of association stored in the database.

The prescription search system to which the present invention is applied is characterized by comprising: a database in which 3 or more stages of 2 nd association degrees between each change symptom and a new prescription from the past and the past lifestyle habits are stored in advance; an input unit which inputs an actual lifestyle habit and an actual change symptom of a patient who has performed the lifestyle habit; and a search means for searching for 1 or more new prescriptions based on the past lifestyle habits and the change symptoms inputted by the input means with reference to the 2 nd degree of association stored in the database.

The recipe search program to which the present invention is applied is characterized by causing a computer to execute the steps of: a1 st association degree acquisition step of acquiring in advance 1 st association degrees of 3 stages or more between combinations of 1 or more of each symptom and each lifestyle habit and each attribute information and prescriptions of the combinations; an input step of inputting information constituting the combination; a search step of searching for 1 or more prescriptions based on the information input in the input step with reference to the 1 st relevance obtained in the 1 st relevance obtaining step; a 2 nd correlation degree obtaining step of obtaining 2 nd correlation degrees of 3 stages or more between each of the prescription previously administered and the patient who administered the prescription, which are searched in the searching step, and a new prescription; a change symptom input step of inputting an actual change symptom of the patient who has performed the prescription previously performed; and a new prescription searching step of searching for 1 or more new prescriptions based on the prescription previously carried out and the change symptom input in the change symptom inputting step with reference to the 2 nd degree of correlation obtained in the 2 nd degree of correlation obtaining step.

The recipe search program to which the present invention is applied is characterized by executing, in a computer, the steps of: a 2 nd association degree obtaining step of obtaining 2 nd association degrees of 3 stages or more between the previous lifestyle habits and each change symptom from the previous time and the new prescription; a change symptom input step of inputting actual living habits and actual change symptoms of patients who have performed the living habits; and a new prescription search step of searching for 1 or more new prescriptions based on the past lifestyle habits and the change symptoms input in the change symptom input step with reference to the 2 nd degree of relevance acquired in the 2 nd degree of relevance acquisition step.

The prescription search method applied in the invention is characterized in that the prescription search method comprises the following steps: a1 st association degree acquisition step of acquiring in advance 1 st association degrees of 3 stages or more between combinations of 1 or more of each symptom and each lifestyle habit and each attribute information and prescriptions of the combinations; an input step of inputting information constituting the combination; a search step of searching for 1 or more prescriptions based on the information input in the input step with reference to the 1 st relevance obtained in the 1 st relevance obtaining step; a 2 nd correlation degree obtaining step of obtaining 2 nd correlation degrees of 3 stages or more between each symptom of change of the patient who has performed the prescription and the prescription previously performed and the new prescription searched in the searching step; a change symptom input step of inputting an actual change symptom of the patient who has performed the prescription previously performed; and a new prescription searching step of searching for 1 or more new prescriptions based on the prescription previously carried out and the change symptom input in the change symptom inputting step with reference to the 2 nd degree of correlation obtained in the 2 nd degree of correlation obtaining step, and executing each of the steps by a computer.

The prescription search method applied in the invention is characterized in that the prescription search method comprises the following steps: a 2 nd association degree obtaining step of obtaining 2 nd association degrees of 3 stages or more between the previous lifestyle habits and each change symptom from the previous time and the new prescription; a change symptom input step of inputting actual living habits and actual change symptoms of patients who have performed the living habits; and a new prescription search step of searching for 1 or more new prescriptions based on the past lifestyle habits and the change symptoms input in the change symptom input step with reference to the 2 nd degree of relevance acquired in the 2 nd degree of relevance acquisition step, and executing each of the steps by a computer.

Effects of the invention

According to the present invention having the above configuration, the prescription can be determined with reference to the 1 st relevance degree from the symptom newly obtained by the operation unit. Further, according to the present invention, these determination operations can be automatically performed without manual work. This eliminates the need for labor and time for a worker having expert knowledge to analyze the newly acquired symptoms.

According to the present invention configured as described above, the prescription can be determined with reference to the 1 st relevance degree from the symptom newly obtained via the operation unit. Further, according to the present invention, these determination operations can be automatically performed without manual work. This eliminates the need for labor and time for a worker having expert knowledge to analyze the newly acquired symptoms.

Drawings

Fig. 1 is a block diagram showing the overall configuration of a prescription search system 1 to which the present invention is applied.

Fig. 2 is a block diagram of a search device 2 constituting a prescription search system 1 to which the present invention is applied.

Fig. 3 is a diagram showing an example in which 1 st association degrees of 3 stages or more are defined in advance between each symptom and a prescription for a patient.

Fig. 4 is a diagram showing an example in which 1 or more prescriptions are associated with a combination of a plurality of symptoms via the 1 st association degree.

Fig. 5 is a diagram showing a case where 1 or more prescriptions are associated with a combination of symptoms and lifestyle habits and a combination of symptoms and characters via the 1 st association degree.

Fig. 6 is a diagram showing a case where 1 or more prescriptions are associated with 3 combinations of symptoms, lifestyle habits, and characters via the 1 st association degree.

Fig. 7 is a graph showing 3 or more stages of 2 nd correlation between each improvement symptom of a prescription previously administered and a patient administered the prescription and a new prescription.

Fig. 8 is a diagram showing an example in which 1 st association degrees of 3 stages or more are defined in advance between each symptom of a disease (lifestyle disease) and a prescription for a patient.

Fig. 9 is a diagram showing an example in which 1 or more prescriptions are associated with a combination of a plurality of symptoms of a disease (lifestyle disease) via the 1 st association degree.

Fig. 10 is a graph showing the degree of association 2 of 3 stages or more between a prescription previously administered and a new prescription and each change symptom of a patient who has administered the prescription, which are related to a disease (lifestyle disease).

Fig. 11 is a diagram showing an example in which 1 st association degrees of 3 stages or more are predefined between each symptom of baldness and a prescription for a patient.

Fig. 12 is a diagram showing an example in which 1 or more prescriptions are associated via the 1 st association degree for a combination with a plurality of symptoms of baldness.

Fig. 13 is a graph showing 3 or more stages of 2 nd correlation degrees between each of the previously implemented prescription corresponding to baldness and the varied symptoms of the patient who implemented the prescription and the new prescription.

Fig. 14 is a diagram showing an example in which 1 st association degrees of 3 stages or more are previously defined between each symptom related to an appearance and a prescription for a patient.

Fig. 15 is a diagram showing an example in which 1 or more prescriptions are associated via the 1 st association degree for a combination of a plurality of symptoms related to an appearance.

Fig. 16 is a graph showing 3 or more stages of 2 nd correlation between each change symptom of a previously implemented prescription and a new prescription of a patient who implemented the prescription in relation to the appearance.

Detailed Description

Hereinafter, an allergy prescription search system to which the present invention is applied will be described in detail with reference to the accompanying drawings.

Embodiment 1

Fig. 1 is a block diagram showing the overall configuration of a prescription search system 1 to which the present invention is applied. The prescription search system 1 automatically searches for an optimum prescription based on symptoms of allergic patients including pollinosis and the like without the help of experts. The allergy referred to herein includes not only pollinosis but also all allergies to foods, environments and the like, but hereinafter, pollinosis which is an allergy based on pollen of cedar and the like will be described as an example.

The prescription search system 1 has a database 3 and a search device 2 connected to the database 3. The database 3 constructs a database relating to prescriptions to be provided to patients. Information transmitted via the public communication network or information input by the user of the present system is accumulated in the database 3. The database 3 transmits the accumulated information to the search device 2 in response to a request from the search device 2. The database 3 may also be controlled by artificial intelligence. The artificial intelligence can be artificial intelligence based on any of the well-known techniques of artificial intelligence.

The search device 2 is configured by an electronic device typified by a Personal Computer (PC), for example, but may be implemented by any other electronic device other than a PC, such as a mobile phone, a smartphone, a tablet terminal, and a wearable terminal.

Fig. 2 shows a specific configuration example of the search device 2. A control unit 24 for controlling the entire search apparatus 2, an operation unit 25 for inputting various control commands via operation buttons, a keyboard, or the like, a communication unit 26 for performing wired communication or wireless communication, a search unit 27 for searching for optimum detection algorithm information, and a storage unit 28 for storing a program for performing a search to be executed, are connected to the internal bus 21 of the search apparatus 2, respectively, and the storage unit 28 is represented by hardware or the like. A display unit 23 as a monitor for actually displaying information is connected to the internal bus 21.

The control unit 24 is a so-called central control unit for controlling each component mounted in the search device 2 by transmitting a control signal via the internal bus 21. The control unit 24 transmits various control commands via the internal bus 21 in accordance with an operation performed via the operation unit 25.

The operation unit 25 is implemented by a keyboard or a touch panel, and an execution command for executing a program is input by a user. When the execution command is input by the user, the operation unit 25 notifies the control unit 24 of the execution command. The control unit 24 that has received the notification causes the search unit 27 to execute the desired processing operation in cooperation with each component.

Further, symptoms, intake, lifestyle habits, attribute information of the patient, and characters of the patient who actually performed the prescription are input via the operation unit 25. If the user is a patient himself, the patient himself inputs the information using the operation unit 25, and if the user is an advisor who advises the patient, the advisor inputs the information heard from the patient using the operation unit 25.

The search unit 27 searches for a prescription most suitable for the patient based on the information input via the operation unit 25. The search unit 27 reads various information stored in the storage unit 28 as necessary information every time a search operation is executed. The search unit 27 may be controlled by artificial intelligence. The artificial intelligence can be artificial intelligence based on any of the well-known techniques of artificial intelligence.

The display unit 23 is constituted by a graphic controller that generates a display image under the control of the control unit 24. The display unit 23 is implemented by, for example, a Liquid Crystal Display (LCD).

When the storage unit 28 is configured by hardware, predetermined information is written in each address under the control of the control unit 24, and the information is read as necessary. In addition, a program for executing the present invention is stored in the storage unit 28. The control unit 24 reads and executes the program.

Next, the operation of the prescription search system 1 configured as described above will be described.

First, the symptoms, intake, lifestyle, attributes, and characters of the patient who actually performed the prescription are input via the operation unit 25. The input information is sent to the search unit 27 and the database 3.

The search unit 27 searches for a prescription most suitable for the patient based on the information transmitted from the operation unit 25. In the process of the search by the search unit 27, the information stored in the database 3 is referred to.

In the database 3, 3 or more stages of 1 st association degrees between each symptom, each intake, each living habit, each attribute information, and each character of the patient and the prescription for the patient are stored in advance. The 1 st degree of association is only required to be associated at least between each symptom and the prescription for the patient, and it is not necessary to specifically associate each intake, each lifestyle habit, each attribute information, and each character of the patient with the 1 st degree of association.

Fig. 3 shows an example in which 1 st association degrees of 3 stages or more are predefined between each symptom and a prescription for a patient. The symptoms are arranged on the left side with the 1 st degree of association, and the parts are arranged on the right side with the 1 st degree of association. The 1 st degree of association shows to which prescription the symptom ranked on the left side is highly associated and to what extent. In other words, the degree of correlation is an index showing a high possibility of which prescription each symptom is likely to be related to, and shows reliability in selecting the most suitable prescription according to the symptom.

The symptoms are items showing the actual symptoms of pollinosis in a patient. Examples of items of the symptom include the following: the number of nose blowing (21 times or more, 10 to 20 times or 9 times or less), "eye symptoms" (eyes are not open, itching is felt slightly, and itching is not felt), "influence on attention" (large, medium, and small), "nasal obstruction" (breathing is not possible, breathing is noticed by mouth, breathing is basically done by nose, and breathing is sometimes done by mouth), and the like.

The intake includes all the intake of the patient, and is food, beverage, health product, medicament, etc.

The attribute information of the patient includes information such as age, sex, occupation, presence or absence of a person living in the same house, presence or absence of a person suffering from pollinosis including parents, and a result of physical examination in a medical institution.

In table 1, the arrangement shows examples of these symptoms. However, the symptoms are not limited to the examples in table 1, and other items may be included as long as they are similar to the symptoms.

[ Table 1]

The prescription consists of items relating to diet and lifestyle habits for ameliorating pollinosis, which are: the method comprises the steps of reducing the amount of oil, using sunflower oil, using fatty acid health care products at 2 g/day, bathing for more than ten minutes, sleeping for more than 6 hours, using toxin expelling health care products at 1 g/day, and keeping the number of times of eating outside to be less than 25% of the number of times of eating outside.

In table 2, examples of these prescriptions are shown in arrangement. However, the recipe is not limited to the example in table 2, and other items may be included as long as they are similar to the above.

[ Table 2]

The prescription is generally classified into diet, health care product, water supplement, exercise, bath, sleep, and the like, but is not limited thereto, and any items may be included as long as they are factors contributing to improvement of pollinosis.

For example, when the "number of times of blowing a nose" was 21 or more, the 1 st correlation with the "oil reduction amount" was 60%, the 1 st correlation with the "fatty acid-based health product 2 g/day" was 40%, and the 1 st correlation with the "number of times of eating outside was 25% or less of the total number" was 80%. When the number of times of blowing the nose was 10 to 20, the 1 st correlation with the number of times of using sunflower oil was 60%. When the "number of times of blowing a nose" was 9 or less, the degree of 1 st correlation with "ten minutes or more of bathing" was 40%.

In addition, if the "influence on attention" is large, the 1 st degree of association with "decrease in amount of oil" is 50%, and the 1 st degree of association with "2 g/day of fatty acid-based health product" is 70%. If the "influence on attention" is middle, the 1 st degree of association with "sleep for 6 hours or more" is 80%. If the "influence on attention" is small, the 1 st degree of association between "the number of times of eating outside is 25% or less of the total number" is made 60%.

The 1 st degree of association may be constituted by a model that can be updated by so-called machine learning, or may be constituted by a neural network. The 1 st relevance may be a network based on deep learning.

The search unit 27 refers to the 1 st relevance stored in the database 3 in this manner, and determines which symptom of the symptoms arranged on the left side of the 1 st relevance corresponds to the symptom newly input through the operation unit 25. If the newly input symptom through the operation unit 25 is "a little itching" among "symptoms of eyes", it is possible to search for a prescription in which "sleep for 6 hours or more" is the most suitable prescription with a1 st relevance degree of 80%, and "the number of meals out is 25% or less of the total number" is the prescription of the second opinion with a1 st relevance degree of 60%.

The search unit 27 performs an operation of selecting a prescription by referring to these 1 st relevance degrees based on the symptom newly acquired through the operation unit 25. In this case, the search unit 27 may select the recipe with the highest 1 st relevance degree. This is because, as described above, the higher the 1 st association degree is, the higher the reliability of the selection is. However, the search unit 27 is not limited to selecting the recipe with the highest 1 st relevance degree, and may select the recipe with the medium 1 st relevance degree or may select the recipe with the low 1 st relevance degree. It is needless to say that, in addition to this, a prescription having a1 st correlation degree of 0% may be selected, which is not connected by an arrow between the symptom and the prescription. The search unit 27 is not limited to the case of selecting one prescription, and may further select a plurality of prescriptions with reference to the 1 st relevance degree. The prescription searched by the search unit 27 is displayed on the display unit 23.

It is to be understood that the items of the symptoms and the prescriptions shown in fig. 3 are examples, and the processing operation is similarly executed for the other items shown in tables 1 and 2.

That is, according to the prescription search system 1 to which the present invention is applied, the prescription can be determined by referring to the 1 st relevance degree described above based on the symptom newly obtained through the operation unit 25. Further, according to the prescription search system 1 to which the present invention is applied, these determination operations can be automatically performed without manual work. This eliminates labor and time for a worker having expert knowledge to analyze the newly acquired symptoms.

Further, the prescription search system 1 to which the present invention is applied is characterized in that a prescription is searched via the 1 st relevance degree set to 3 stages or more. The 1 st correlation degree can be described by a numerical value of, for example, 0 to 100%, but is not limited thereto, and may be constituted by any number of stages as long as it can be described by a numerical value of 3 stages or more.

By performing the search based on the 1 st relevance indicated by the numerical values of 3 stages or more, it is possible to perform the search and display in descending order of relevance in a situation where a plurality of prescriptions are selected. If the order of the 1 st relevance degree is from high to low, the prescription with higher display possibility can be preferentially selected. On the other hand, even for the prescription with a low 1 st relevance, the prescription specified by the second opinion can be displayed in the meaning of the second opinion, and the usefulness can be exhibited when the symptoms are not improved by the prescription displayed in the first opinion.

In addition, according to the present invention, it is possible to determine a prescription having an extremely low degree of correlation, such as 1% of the 1 st degree of correlation, without omitting the prescription. Even the prescription with the extremely low 1 st association degree has a tiny sign, and the user can be reminded of the following situations: the prescription may function as a beneficial prescription once in tens or hundreds of times.

Furthermore, according to the present invention, there are advantages as follows: by performing the search based on the 1 st association degree in 3 stages or more, the search policy can be determined by a method of setting a threshold value. If the threshold is made low, even the prescription with 1% relevance can be searched without omission, but a large number of prescriptions with 1% relevance that are less likely to be correct may be searched. On the other hand, if the threshold is set high, only a recipe with a high possibility of being correct can be locked, but a recipe that shows an appropriate solution may be missed several tens of times or several hundreds of times. The degree of freedom in selecting where to place emphasis can be increased by determining where to place emphasis based on the manner of consideration on the user side and the system side.

In the present invention, the 1 st relevance may be updated. That is, the symptoms and prescription as shown in fig. 3 are updated at any time. The update may reflect information provided via a public communication network typified by the internet, for example. In addition, the system side or the user side may manually or automatically update the content according to the research data of experts, papers, academic publications, news reports, books, and the like. Artificial intelligence can be utilized in these update processes.

In the update of the 1 st relevance degree, the 1 st relevance degree is raised or lowered each time the information on the relationship between the symptom and the prescription for the symptom is input. For example, when a prescription is newly confirmed to be effective for a symptom by study, academic bulletin, study data of other experiments, or the like, the degree of association between the symptom and the prescription is increased. In addition, when it is newly confirmed that a certain prescription is invalid for a certain symptom by study data of a paper, a scholarly publication, or other experimental verification, the 1 st correlation between the symptom and the prescription is decreased.

By setting the 1 st correlation degree to 3 stages or more as described above, such a case where the 1 st correlation degree is increased or decreased can be freely dealt with. The 1 st relevance level itself may be updated by the machine learning or the deep learning described above.

Further, when a new symptom that has not yet appeared or a new prescription that has not yet appeared is found, a new 1 st degree of association may be set between them. Further, the 1 st association degree between these new symptoms and the prescription can be updated as described above.

The 1 st degree of association described above is not limited to the case where the prescription is associated with a single symptom. For example, as shown in fig. 4, 1 or more prescriptions may be associated with a combination of a plurality of symptoms via the 1 st degree of association.

In the example of fig. 4, a node P, a node Q, and a node R are provided, the node P being a combination of "blowing a nose" 21 times or more and a case where "the symptom of the eyes" is not open, the node Q being a combination of "blowing a nose" 9 times or less and an "influence on attention", and the node R being a combination of a case where "the influence on attention" is large and a case where "the nose plug" is perceived to be breathing in the mouth, respectively. In addition, the 1 st relation degree of "decrease in oil amount" for the node P was 60%, and the 1 st relation degree of "fatty acid health product 2 g/day" was 40%. For the node Q, the 1 st association degree of "using sunflower oil" was 30%, and the 1 st association degree of "sleep for 6 hours or more" was 70%. For the node R, the 1 st association degree of the 'bath for more than ten minutes' is 80%, and the 1 st association degree of the 'toxin expelling health care product 1 g/day' is 50%.

The 1 st relevance degree of such a combination is obtained in advance. Next, the search unit 27 refers to the 1 st relevance degree and determines to which symptom of the symptoms arranged on the left side of the 1 st relevance degree the newly input symptom of 2 or more through the operation unit 25 corresponds. Since the "reduced oil amount" having a1 st correlation of 60% to the node P, the "fatty acid-based health product 2 g/day" having a1 st correlation of 40% to the node P, and the like are selected, assuming that the "number of nose blowing operations" newly input through the operation unit 25 is 21 or more and the "eye symptom" is a degree of eyes wide.

In the example of fig. 5, a case is shown in which 1 or more prescriptions are associated with a combination of a symptom and an intake and a combination of a symptom and attribute information of a patient via the 1 st association degree.

A node S, which is a combination of a case where the user feels a little itchy and a case where the user has a large amount of dietary oil in the item of "ingestion", and a node T, which is a combination of a case where the user has attribute information of "no pollen disease in relatives" and "nose blowing" for 21 times or more, are provided, respectively. In each node S, the 1 st degree of association of "decrease in the amount of oil" is 70%, the 1 st degree of association of "sleep for 6 hours or more" is 50%, and the 1 st degree of association of "use of sunflower oil" is 30%. In addition, for the node T, the 1 st relevance degree of "2 g/day of fatty acid health products" was 60% and the 1 st relevance degree of "the number of meals outside was 25% or less of the total number of meals" was 40% in each recipe.

The 1 st relevance degree of such a combination is obtained in advance. Next, the search unit 27 refers to the 1 st relevance degree to determine which item of the items arranged on the left side of the 1 st relevance degree corresponds to the combination of the symptom and the ingestion newly input through the operation unit 25 and the combination of the symptom and the attribute information. If the "eye symptom" newly input via the operation unit 25 is a slight itching feeling and the "amount of oil in the diet" in the "ingested material" item is large, the node S is assumed to be selected, and each item associated with the node S by the 1 st degree of association is selected. Similarly, if the newly input symptom through the operation unit 25 is "the number of nose blowing times" is 21 or more and the attribute information of the patient is "the relatives do not have pollinosis", each item associated with the node T by the 1 st association degree is selected corresponding to the node T.

Therefore, it is possible to perform a more accurate search by specifying the 1 st relevance in advance based on the object and the attribute information in addition to the symptom.

It is to be understood that the items listed in fig. 5, such as symptoms, ingested objects, attribute information, and prescriptions, are examples, and that the processing operation is executed in the same manner for the other items shown in tables 1 and 2.

In the 1 st association degree of the combination shown in fig. 5 described above, a combination of 1 symptom and 2 or more intakes, a combination of 2 or more symptoms and 1 intake, and a combination of 2 or more symptoms and 2 or more intakes may be associated. Similarly, in the 1 st association degree, a combination of 1 symptom and 2 or more pieces of attribute information, a combination of 2 or more symptoms and 1 piece of attribute information, and a combination of 2 or more symptoms and 2 or more pieces of attribute information may be associated.

From the viewpoint of improving convenience in inputting symptoms, intakes, and attribute information through the operation unit 25, the following method may be adopted: various kinds of inquiries are displayed on the display unit 23, and the user can naturally input these information by operating the operation unit 25 in accordance with the contents of the inquiry and answering the inquiry.

In the example of fig. 6, 3 combinations of symptoms, ingested objects, and attribute information are shown, and 1 or more prescriptions are associated with each other via the 1 st association degree.

A node U is provided, which is a combination of a number of < 21 > snivel blows and a number of < the amount of dietary oil > in the item of 'intake of food', and attribute information of 'family patient without pollinosis'. In each node U, the 1 st degree of association of "oil amount decreased" was 60%, the 1 st degree of association of "fatty acid-based health product 2 g/day" was 80%, the 1 st degree of association of "sleep over 6 hours" was 30%, and the degree of association of "detoxifying health product 1 g/day" was 50%.

The 1 st relevance degree of such a combination is obtained in advance. Next, the search unit 27 refers to the 1 st relevance degree and determines which item of the items arranged on the left side of the 1 st relevance degree corresponds to the combination of the symptom, the ingestion, and the attribute information newly input through the operation unit 25. If the newly input symptom through the operation unit 25 is "the number of times of nose blowing" is 21 or more, "the" amount of dietary oil "in the item of ingestion is large, and the attribute information is" the relatives patient without pollinosis ", the node U is corresponded to, and each item related to the node U by the 1 st degree of relation is selected. Therefore, the 1 st degree of association is predetermined in accordance with the object and the attribute information in addition to the symptom, and a more accurate search can be performed.

It is to be understood that the items listed in fig. 6, such as symptoms, ingested objects, attribute information, and prescriptions, are examples, and that the processing operation is executed in the same manner for the other items illustrated in tables 1 and 2.

In the 1 st association degree of the combination shown in fig. 6, any combination may be used as long as it is a combination of 1 or more symptoms, 1 or more intakes, and 1 or more attribute information. In addition, each 1 st degree of association shown in fig. 5 and 6 may be updated in the same manner.

In the present invention, as items arranged on the left side of the 1 st relevance degree, external information, personal information of a patient, and a character may be added in addition to symptoms, ingested objects, and attribute information.

The external information includes, for example, data of the amount of pollen scattered in the current residential area, and environmental information such as weather and temperature.

In addition, the lifestyle includes exercise, bathing, sleeping, going out, living places, and other information.

Table 3 below shows examples of the lifestyle habits, external information, and the personality of the patient. However, the lifestyle, external information, and patient character are not limited to those in table 3, and may include other arbitrary lifestyle, external information, and patient character.

[ Table 3]

By associating the relationship between the combination of the lifestyle habits, the external information, the personality of the patient, and the symptoms and the prescription with the 1 st association degree, and by referring to the 1 st association degree, it is possible to perform a more accurate search in consideration of the lifestyle habits, the external information, and the personality of the patient. At this time, of course, the 1 st association degree may include a relationship between the combination of the above-described intake and/or attribute information and the prescription, in addition to a combination of any 1 or more of the lifestyle habits, external information, and the personality of the patient and the symptoms.

Embodiment 2

A prescription search system 1 to which embodiment 2 of the present invention is applied will be explained. In embodiment 2, the same components and members as those in embodiment 1 are denoted by the same reference numerals, and the following description is omitted.

In embodiment 2, a database 3 is referred to, and the database 3 stores in advance 3 or more stages of 2 nd correlation degrees between each improvement symptom of a patient who has previously performed a prescription and a new prescription and the new prescription.

The prescription previously filled as referred to herein includes all prescriptions previously filled by a patient regardless of whether or not the prescription is a prescription searched by applying the prescription search system 1 of the present invention. The examples shown in table 2 and the like are considered for the prescription that has been previously carried out, but the prescription is not limited to this, and any other items may be included as long as the prescription is related to diet or lifestyle habits for ameliorating pollinosis. The prescription previously carried out includes, in addition to the above, the name of the prescription drug, the period of desensitization treatment, the name of the health care product to be taken, the frequency of taking, and the like.

The symptoms of improvement of the patient who has been prescribed are the same as the examples of symptoms shown in table 1 as content examples of items, but the symptoms are not limited to these examples, and any other items may be included as long as the symptoms of pollinosis are shown.

The new recipe may be exemplified by those shown in table 2, but is not limited thereto, and may include any other items as long as the recipe is related to diet or lifestyle habits for ameliorating pollinosis.

Next, the operation of the prescription search system 1 according to embodiment 2 will be described. First, a prescription previously performed and each improvement symptom of a patient who performed the prescription are input through the operation unit 25. The inputted information is transmitted to the search unit 27 and the database 3.

The search unit 27 searches for a new prescription for the patient based on the information transmitted from the operation unit 25.

In the process of the search by the search unit 27, the information stored in the database 3 is referred to.

The 2 nd degree of association described above is stored in advance in the database 3. The 2 nd degree of association indicates which new prescription has a high degree of association and to what degree of association the combinations of each prescription and each symptom of improvement that have been previously performed and are arranged on the left side. In other words, the 2 nd degree of correlation is an index showing a high possibility of which new prescription is related to each combination of each prescription and each symptom to be improved that has been performed before, and shows reliability of selecting the most suitable prescription according to the symptom.

Fig. 7 shows an example of the 2 nd degree of association. The previously performed parts and the improved symptoms are arranged on the left side with the 2 nd relevance, and the new parts are arranged on the right side with the 2 nd relevance.

In the example of fig. 7, a node V, which is a combination of "reduce the amount of oil" and "make the number of meals outside 25% or less of the total number of times" and "number of sneezes per unit time" as an improvement symptom, 21 or more, and a node W, which is a combination of "take a bath ten minutes or more" and "make the number of meals outside 25% or less of the total number of times" and "number of sneezes per unit time" as an improvement symptom, 10 to 20 times and "mask necessity" are provided, respectively. The 2 nd degree of association between this node V and "using sunflower oil" as a new prescription is 70%, and the 2 nd degree of association between "sleep for 6 hours or longer" is 40%. In addition, the 2 nd degree of association between the node W and the "fatty acid health product 2 g/day" as the new prescription is 80%, and the 2 nd degree of association between the node W and the "toxin expelling health product 1 g/day" is 30%.

The 2 nd degree of association of such a combination is obtained in advance. Next, the search unit 27 refers to the 2 nd degree of association, and determines which item of items arranged on the left side of the 2 nd degree of association the improvement symptoms of the prescription previously performed and the patient who performed the prescription newly input through the operation unit 25 correspond to. If the previously executed prescription input through the operation unit 25 is "ten minutes or more for bathing" and "the number of times of eating outside is 25% or less of the total number of times", the symptom of improvement is "the number of sneezes" is 10 to 20 times, and the necessity of a mask "is slightly uncomfortable but can go outside, this corresponds to the node W in this case. In this case, "fatty acid health products 2 g/day" and "toxin expelling health products 1 g/day" are selected as a new prescription with reference to the 2 nd degree of association of the node W.

In this way, in embodiment 2, a new prescription can be automatically searched for on the basis of a prescription previously made and each improvement symptom of a patient who has made the prescription. Therefore, by continuously carrying out embodiment 2 after embodiment 1, it is possible to continuously observe the symptoms of pollinosis of the patient and select the most suitable prescription based on the symptoms. Even if the prescription previously administered is a prescription having a high effect, when the symptoms of the patient later improve, the patient can subsequently present an optimum prescription that is convenient, such as a prescription having a lower effect than the previous prescription.

The 2 nd degree of association may be updated in the same manner. That is, the prescription, each symptom to be improved, and the new prescription that have been previously performed as shown in fig. 7 are updated as needed. The update may reflect information provided via a public communication network typified by the internet, for example. In addition, the system side or the user side may be updated manually or automatically according to the contents of research data, papers, academic publications, news reports, books, and the like of experts. Artificial intelligence can be utilized in these update processes.

It is to be understood that the 2 nd degree of association may be associated with the prescription in combination with any 1 or more of the above-described intake, attribute information, lifestyle habits, characters, and external information, in addition to the prescription and the improvement symptoms which have been previously performed.

Embodiment 3

A prescription search system 1 to which embodiment 3 of the present invention is applied will be described. In embodiment 3, the same components and members as those in embodiments 1 and 2 are denoted by the same reference numerals, and the following description is omitted.

Prescription search for disease

Fig. 8 shows an example in which 1 st association degrees of 3 stages or more are previously defined between each symptom of a disease and a prescription for a patient. The symptoms are arranged on the left side with the 1 st degree of association, and the parts are arranged on the right side with the 1 st degree of association. The 1 st relevance shows which prescription is more relevant and to what extent for the symptoms arranged on the left. In other words, the degree of association is an index showing a high possibility of which prescription each symptom is related to, and shows reliability in selecting the most suitable prescription according to the symptom.

In addition, the 1 st association degree associates a combination of each symptom, an ingested object, and attribute information with a prescription of 1 or more.

The symptoms are various symptoms of the disease, and include direct symptoms such as blood glucose level and hemoglobin A1c amount, and indirect symptoms such as various test values and in vivo mineral amount during physical examination. These symptoms include indexes represented by various medical data, and results (including VAS evaluation) as sensory scores felt by doctors, patients, evaluators, and the like.

Lifestyle habits encompass all matters related to the life of a patient. For example, if the lifestyle habits are matters related to dietary life, the lifestyle habits include ingestion, amount of diet, and time of diet. The ingested material as referred to herein includes all articles that can be ingested by a patient, and includes foods, beverages, health products, medicines, and the like. In addition, if the lifestyle is related to sleep, it includes sleep time, getting up time, and sleeping time. In addition, if the lifestyle is related to exercise, the exercise time and the exercise items are included. In addition, the living habits also include sleeping, bathing, working, and the like. The lifestyle is a concept of a so-called living environment including all living internal environments and all living external environments.

The attribute information of the patient includes the following information: age, sex, occupation, presence or absence of a person living in the same house, presence or absence of a person having the same symptom in both parents, or a physical examination result of a medical institution.

The prescription includes all prescriptions for alleviating symptoms, such as which nutrients are taken, what lifestyle habits are taken, and which drugs should be taken. Additionally, the prescription includes a medical procedure. In the prescription, a schedule of the prescription to be implemented and a treatment plan may be presented.

Examples of the diseases include lifestyle-related diseases (hypertension, diabetes, dyslipidemia, and the like) and other diseases. The disease may be a disease not including pollinosis. The disease also includes symptoms of allergies. In the following examples, lifestyle-related diseases will be described as examples of the diseases.

In the example of fig. 8, a node R, a node S, and a node T are provided, the node R being a combination of a blood glucose value of less than 200(mg/dl) after drinking 75g of glucose for 2 hours in the item of the symptom of lifestyle disease, an intake amount of green vegetables of less than 60g in the item of lifestyle habit (intake), and an age of 30 to 40 years in the attribute item, the node S being a combination of 6.5% or more of hemoglobin in the symptom item and 120g or more of an intake amount of green vegetables in the item of lifestyle habit (intake), the node T being a combination of a blood glucose value of less than 126(mg/dl) at fasting state and an age of 50 generations or more in the symptom item. Further, the 1 st association between the node R and "eating at a prescribed time" is 70%, and the 1 st association between "avoiding sweet food and oily diet" is 20%. The 1 st association degree between the node S and "ingesting green vegetables 120g or more" was 70%, the 1 st association degree between "eating at a predetermined time" was 50%, and the 1 st association degree between "avoiding sweet food and oily diet" was 30%. The 1 st association between node T and "ingesting food containing dietary fiber" was 60%, and the 1 st association between "performing exercise for 3 minutes or more per day" was 40%.

The 1 st relevance degree of such a combination is obtained in advance. Next, the search unit 27 refers to the 1 st relevance degree and determines to which of the symptoms, lifestyle habits, and attribute information newly input through the operation unit 25 corresponds to the left side of the 1 st relevance degree. If the newly inputted symptom through the operation unit 25 is that the blood glucose level 2 hours after drinking 75g of glucose is less than 200(mg/dl), the intake item is the intake amount of green vegetables less than 60g, and the attribute item is 30 to 40 years old, in this case, it corresponds to the node R, and therefore, "eating at a predetermined time" with a1 st association degree of 70% and "eating without sweet food and oily food" with a1 st association degree of 20% are selected. Similarly, when the symptom item newly input through the operation unit 25 is hemoglobin of 6.5% or more and the ingestion item is green vegetables of 120g or more, the node S is equivalent, and therefore, "green vegetables of 120g or more are ingested" with a1 st association degree of 70%, "eating for a predetermined time" with a1 st association degree of 50%, and "eating without sweet food and oily food" with a1 st association degree of 30% are selected.

The 1 st degree of association may be constituted by a model that can be updated by so-called machine learning, or may be constituted by a neural network. The 1 st relevance may be a network based on deep learning.

The search unit 27 refers to the 1 st relevance stored in the database 3 in this manner, and determines which of the symptoms, habits, and attribute information newly input via the operation unit 25 corresponds to each of the symptoms, habits, and attribute information arranged on the left side of the 1 st relevance.

The search unit 27 refers to the 1 st relevance degree and performs an operation of selecting a prescription based on the symptom, lifestyle habit, and attribute information newly acquired through the operation unit 25. In this case, the search unit 27 may select the recipe with the highest 1 st relevance degree. As described above, the higher the 1 st association degree is, the higher the reliability of the selection is. However, the search unit 27 is not limited to selecting the recipe with the highest 1 st relevance degree, and may select the recipe with the medium 1 st relevance degree or may select the recipe with the low 1 st relevance degree. Needless to say, other than these, a prescription having a1 st degree of correlation of 0% may be selected, which is not connected by an arrow between the symptom, lifestyle habit, and attribute information and the prescription. The search unit 27 is not limited to selecting one prescription, and may intentionally select a plurality of prescriptions with reference to the 1 st relevance degree. The prescription searched by the search unit 27 is displayed via the display unit 23.

It is to be understood that the items of symptoms, lifestyle habits, attribute information, and prescriptions listed in fig. 8 are only examples, and the above-described processing operation may be executed as long as the items correspond to the symptoms, lifestyle habits, attribute information, and prescriptions.

That is, according to the prescription search system 1 to which the present invention is applied, the prescription can be determined by referring to the 1 st relevance degree described above based on the symptom newly obtained through the operation unit 25. Further, according to the prescription search system 1 to which the present invention is applied, these determination operations can be automatically performed without manual work. This eliminates the need for labor and time for a worker having expert knowledge to analyze the newly acquired symptoms.

The recipe search system 1 to which the present invention is applied is characterized in that the recipe search is performed through the 1 st relevance degree set to 3 steps or more. The 1 st correlation degree can be described by a numerical value of 0 to 100%, for example, but is not limited thereto, and may be constituted by any number of stages as long as it can be described by a numerical value of 3 stages or more.

By performing the search based on the 1 st relevance indicated by the numerical values of 3 stages or more, it is possible to perform the search and display in descending order of relevance in a situation where a plurality of prescriptions are selected. If the prescription with the high display possibility is displayed to the user in the order of the highest relevance degree 1, the prescription with the high display possibility can be selected with higher priority. On the other hand, even for the prescription with a low 1 st relevance, the prescription specified by the second opinion can be displayed in the meaning of the second opinion, and the usefulness can be exhibited when the prescription displayed in the first opinion does not improve symptoms or the like.

In addition, according to the present invention, it is possible to determine without omitting a prescription having an extremely low degree of correlation such that the 1 st degree of correlation is 1%. Even the prescription with the very low 1 st relevance degree has a slight symptom, and the user can be reminded that the prescription can be used as a beneficial prescription for tens of times or hundreds of times.

Furthermore, according to the present invention, there are advantages as follows: by performing the search based on the 1 st association degree in 3 stages or more, the search policy can be determined by a method of setting a threshold value. If the threshold is made low, even the prescription with 1% relevance is searched without omission, but a large number of prescriptions with 1% relevance having low possibility of being correct may be searched. On the other hand, if the threshold is set high, only a recipe with a high possibility of being correct can be locked, but a recipe showing an appropriate solution may be missed several tens or several hundreds of times. The user side and the system side can decide where to place emphasis according to a method of consideration, and the degree of freedom in selecting where to place emphasis can be improved.

In the present invention, the 1 st relevance may be updated. That is, the symptoms and prescription as shown in fig. 8 are updated at any time. The update may reflect information provided via a public communication network typified by the internet, for example. In addition, the system side or the user side may be updated manually or automatically according to the contents of research data, papers, academic publications, news reports, books, and the like of experts. Artificial intelligence can be utilized in these update processes.

In the update of the 1 st relevance degree, the 1 st relevance degree is increased or decreased every time the information on the relationship between the symptom and the prescription for the symptom is input. For example, when a prescription is newly confirmed to be effective for a symptom by study, academic bulletin, study data based on other experimental verification, or the like, the degree of association between the symptom and the prescription is increased. In addition, when it is newly confirmed that a certain prescription is not effective for a certain symptom by study, academic bulletin, study data based on other experimental verification, or the like, the degree of 1 st association between the symptom and the prescription is decreased.

As described above, by setting the 1 st correlation degree to 3 steps or more, it is possible to cope with a case where the 1 st correlation degree is desired to be increased or decreased as described above. The 1 st relevance level itself may be updated by the machine learning or the deep learning described above.

In addition, when a new symptom that has not appeared so far is found, or when a new prescription that has not appeared so far is found, a new 1 st degree of association may be set between them. Further, the 1 st relevance of these new symptoms or prescriptions may also be updated as described above.

The 1 st relevance is not limited to the above embodiment. In the example of fig. 9, the 1 st association degree is defined for the node U corresponding to the combination of a plurality of symptoms. That is, the node U is connected to 2 or more of the blood glucose level in the fasting state, the blood glucose level 2 hours after drinking 75g of glucose, the blood glucose level at any time, and hemoglobin. The node U is also connected to lifestyle habits (intake) and attributes. The new recipes for the node U are associated with the 1 st association degree, respectively.

That is, in addition to the symptoms, a combination of 2 or more lifestyle habits (intake) or a combination of 2 or more attribute information may be associated with the nodes.

In addition, according to the present invention, the database 3 may be referred to, and the database 3 may store 3 or more stages of 2 nd correlation degrees between each change symptom of the patient who has previously performed the prescription and the new prescription after performing the prescription. The 2 nd degree of association is constituted by, for example, a neural network.

The previously administered prescription as referred to herein is independent of whether or not it is a prescription that has been searched by applying the prescription search system 1 of the present invention, and includes all prescriptions that have been previously administered to a patient. The prescription that has been previously carried out may include other items as long as it is a prescription relating to a diet or a living habit for improving symptoms. The prescription that has been previously carried out includes the name of the prescribed drug, the operation, the name of the health product to be taken, the frequency of taking, and the lifestyle after improvement.

The term "symptoms of change" as used herein means symptoms after the change by the prescription. The symptoms of change include, in addition to the symptoms improved as compared to before the prescription, the symptoms of deterioration or the symptoms without any change as compared to before.

The prescription previously performed and the change symptoms of the patient after the prescription is performed are input through the operation unit 25. The input information is transmitted to the search unit 27 and the database 3.

The search unit 27 searches for a new prescription for the patient based on the information transmitted from the operation unit 25. In the process of the search by the search unit 27, the information stored in the database 3 is referred to.

The 2 nd degree of association described above is stored in advance in the database 3. The 2 nd correlation shows which new prescription is highly correlated and up to what degree with respect to each combination of the previously conducted prescriptions and each change symptom arranged on the left side. In other words, the 2 nd degree of correlation is an index showing a high possibility that a combination of each prescription and each change symptom previously performed is related to which new prescription, and shows reliability of selecting the most suitable prescription according to the symptom.

Fig. 10 shows an example of the 2 nd degree of association. The previously performed parts and changed symptoms are arranged on the left side with the 2 nd degree of association, and the new parts are arranged on the right side with the 2 nd degree of association.

In the example of fig. 10, a node V and a node W are provided, the node V being a "diet avoiding sweet food and oil", "exercise for 3 minutes or more per day", and the change symptom being a blood glucose level in the fasting state of 126(mg/dl) or more, the node W being a "chewing 30 times with one bite", "exercise for 3 minutes or more per day", and the change symptom being a blood glucose level in the fasting state of less than 126(mg/dl), and hemoglobin of 6.5% or more, respectively. The 2 nd degree of association between the node V and "ingesting 120g or more of green vegetables" as a new prescription is 70%, and the 2 nd degree of association between "eating at a predetermined time" is 40%. In addition, the 2 nd degree of association between the node W and "ingesting food containing dietary fiber" as a new prescription is 80%, and the 2 nd degree of association between "performing exercise for 3 minutes or more per day" is 30%.

The 2 nd degree of association of such a combination is obtained in advance. Next, the search unit 27 refers to the 2 nd degree of association, and determines which item of items arranged on the left side of the 2 nd degree of association each change symptom of the prescription and the patient who has given the prescription newly input through the operation unit 25 corresponds to. If the previously executed prescription input through the operation unit 25 is "chewing 30 times in one bite" and "exercise is performed for 3 minutes or more per day, and the change symptom is a blood glucose level of less than 126(mg/dl) and hemoglobin of 6.5% or more in the fasting state, this case corresponds to the node W. In this case, the new prescription is selected such as "ingest food containing dietary fiber", "perform exercise for 3 minutes or more per day" and the like, in addition to the 2 nd relevance degree of the reference node W.

In this way, a new prescription can be automatically searched for based on the previously implemented prescription and the respective changing symptoms of the patient who implemented the prescription. Thus, it is possible to continue to observe the patient's symptoms and select the most appropriate prescription accordingly. Even if the prescription that has been previously administered is a prescription having a high effect, when the symptoms of the patient are improved later, the patient can continue to propose an optimum prescription that is suitable for the occasion, such as a prescription having a lower effect than the previous prescription.

The 2 nd degree of association may be updated in the same manner. That is, the prescription, the various symptoms of change, and the new prescription that have been previously implemented as shown in fig. 10 are updated as needed. The update may reflect information provided via a public communication network typified by the internet, for example. In addition, the system side or the user side may be updated manually or automatically according to the contents of research data, papers, academic publications, news reports, books, and the like of experts. Artificial intelligence can be utilized in these update processes.

It is to be understood that the 2 nd degree of association may be associated with the prescription by combining 1 or more of the above-described intake, attribute information, lifestyle habits, characters, and external information, in addition to the prescription and the improvement symptoms which have been previously performed.

The 2 nd relevance processing operation is not limited to the above 1 st relevance processing operation, and the previously implemented prescription and the symptom that changes based on the prescription may be input by searching and inputting the prescription, and an unrelated prescription that has been previously implemented may be input. In this case, the range may be further expanded than the conventional prescription, and the conventional lifestyle may be referred to. The past lifestyle may be formed of history information of the lifestyle up to now, or may be intermittently detected at a previous time. Like the change symptom, the change symptom may be a history of change of the symptom so far, or may be a symptom intermittently detected at a previous time.

Prescription search for hair growth

Fig. 11 shows an example in which 1 st association degrees of 3 stages or more are predefined between each symptom of hair growth and a prescription for a patient. The symptoms are arranged on the left side with the 1 st degree of association, and the parts are arranged on the right side with the 1 st degree of association. The 1 st relevance shows which prescription is highly relevant and to what extent, corresponding to the symptoms arranged on the left side.

The 1 st relevance relates 1 or more prescriptions to a combination of each symptom, lifestyle habit, and attribute information.

The symptoms include various symptoms of baldness, including direct symptoms such as the range of alopecia, hairy root symptoms, and the number of hair lost, and indirect symptoms such as the state of scalp and the amount of minerals in the body. These symptoms include indices shown in various medical data, and also include results (including VAS evaluation) perceived by doctors, patients, evaluators, and the like as sensory scores.

The lifestyle habits include all the relevant matters of the patient in life. For example, if the lifestyle habits are matters related to dietary life, the lifestyle habits include the amount of food intake and diet and the time of diet. The ingesta as used herein includes all the ingested substances that can be ingested by a patient, including foods, beverages, health products, medicines, and the like. The lifestyle may include a sleeping time, a time of getting up, a time of going to bed, and the like, if the lifestyle is a matter related to sleeping. The lifestyle may include exercise time and exercise items if it is a matter related to exercise. In addition, if the lifestyle is hair care, the lifestyle includes measures for hair and scalp such as shampoo and hair tonic, measures for preventing ultraviolet rays, massage, and the like.

The attribute information of the patient also includes information such as age, sex, occupation, presence or absence of a person living in the same house, presence or absence of a person having the same symptom in both parents, and a physical examination result of a medical institution.

The prescription includes all prescriptions for alleviating symptoms, which nutrients are taken, which lifestyle habits are adopted, and which medicaments should be taken. Additionally, the prescription may also include a medical procedure. The prescription may also be presented with a schedule and treatment plan for the prescription that should be implemented.

In the example of fig. 11, a node R, a node S, and a node T are provided, the node R being a combination of a case where the hair root symptom in the alopecia symptom is male pattern baldness, the amount of protein in the lifestyle (intake) item is small, and the alopecia inheritance item in the attribute information is a case where a bald person is present in the relative, the node S being a combination of a case where the hair root symptom in the symptom item is seborrheic baldness and the amount of protein in the lifestyle (intake) item is large, and the node T being a combination of a case where the hair loss range in the symptom item is the hamilton classification yyyy and the bald inheritance item in the attribute information is a case where a bald person is not present in the relative, respectively. Further, the 1 st association between the node R and "increase in intake of red meat" was 70%, and the 1 st association between "selective cleansing shampoo 3 minutes/day scrub" was 20%. In addition, the 1 st association between the node S and "cytokine combination introduction 0.1 mg/time" was 70%, the 1 st association between the node S and "amino acid nutrition health product 2 g/day" was 50%, and the 1 st association between the node S and "selective cleansing shampoo 3 minutes/day scrub" was 30%. The 1 st correlation between the node T and "2 minutes/day ethanol-based hair tonic" was 60%, and the 1 st correlation between "increase in intake of red meat" was 40%.

The 1 st relevance degree of such a combination is obtained in advance. Next, the search unit 27 refers to the 1 st relevance degree and determines to which of the left side of the 1 st relevance degree the symptom, lifestyle habit, and attribute information newly input through the operation unit 25 corresponds. If the symptoms newly input through the operation unit 25 are that the hairy root symptom in the baldness symptom item is male pattern baldness, the amount of protein in the lifestyle (intake) item is small, and baldness in the attribute information item is inherited to have baldness among relatives, the symptom corresponds to the node R in this case, and therefore, "increase the intake of red meat" with a1 st association degree of 70% and "selective washing shampoo 3 minutes/day scrub" with a1 st association degree of 20% are selected between the node R and the like.

It is to be understood that the items of symptoms, lifestyle habits, attribute information, and prescriptions listed in fig. 11 are examples, and the above-described processing operation may be executed as long as the items correspond to the symptoms, lifestyle habits, attribute information, and prescriptions.

The 1 st relevance is not limited to the above embodiment. In the example of fig. 12, the 1 st association degree is defined for the node U corresponding to the combination of the plurality of symptoms. That is, the node U is connected to: the alopecia range is hamilton's classification XXX type, the hairy root symptom is dandruff alopecia, the amount of protein in the life habit (intake) item is large, and the baldness inheritance in the attribute information item is the one in which no baldness exists in the relatives. I.e. more than 2 symptoms are connected to node U.

Further, in addition to the symptoms, a combination of 2 or more lifestyle habits (ingesta) and a combination of 2 or more attribute information may be associated with the node U.

In addition, according to the present invention, it is possible to refer to the database 3, and the database 3 stores in advance 3 or more 2 nd degrees of correlation between each change symptom of the patient who has previously performed the prescription and the patient who has performed the prescription and the new prescription.

In the present embodiment, the present invention is not limited to so-called lifestyle-related diseases, and can be performed in the same manner when searching for prescriptions for all diseases.

Fig. 13 shows an example of the 2 nd degree of association. The previously performed parts and changed symptoms are arranged on the left side with the 2 nd degree of association, and the new parts are arranged on the right side with the 2 nd degree of association.

In the example of fig. 13, a node V and a node W are provided, the node V being connected to "cytokine combination introduction 0.1 mg/time", "increase in intake amount of red meat" as the prescription previously conducted and "hamilton classification XXX type" as the alopecia range of the change symptom, respectively, and the node W being connected to "selective cleansing shampoo 3 minutes/day scrub", "increase in intake amount of red meat" as the prescription previously conducted and "seborrheic alopecia" as the hair root symptom of the change symptom, respectively. Further, the 2 nd correlation between this node V and "cytokine combination introduction 0.1 mg/time" as a new prescription was 70%, and the 2 nd correlation between "amino acid nutritional health product 2 g/day" was 40%. In addition, the 2 nd degree of association between the node W and "selective cleansing shampoo 3 minutes/daily scrub" as a new prescription is 80%, and the 2 nd degree of association between "exercise performed for 3 minutes or more per day" is 30%.

The 2 nd degree of association of such a combination is obtained in advance. Next, the search unit 27 refers to the 2 nd degree of association, and determines which item of items arranged on the left side of the 2 nd degree of association each change symptom of the prescription and the patient who performed the prescription newly input through the operation unit 25 corresponds to. If the previously performed prescription inputted through the operation unit 25 is "selective cleansing shampoo 3 minutes/day", "increase in intake of red meat", and the hair loss range is hamilton classification yyyy as the variation symptom, the hair root symptom is seborrheic alopecia, this case corresponds to the node W. In this case, the new prescription is selected such as "selective cleansing shampoo 3 minutes/day scrub" and "exercise of 3 minutes or more per day" in addition to the 2 nd correlation degree of the reference node W.

In addition, the 2 nd degree of association may be updated in the same manner. That is, the prescription, each change symptom, and the new prescription that have been previously implemented as shown in fig. 10 are updated as needed. The update may reflect, for example, information provided via a public communication network typified by the internet. In addition, the system side or the user side may be updated manually or automatically according to the contents of research data, papers, academic publications, news reports, books, and the like of experts. Artificial intelligence can be utilized in these update processes.

It is to be understood that the 2 nd degree of association may be associated with the prescription by combining 1 or more of the above-described intake, attribute information, lifestyle habits, characters, and external information, in addition to the prescription and various symptoms of change that have been previously performed.

The 2 nd relevance processing operation is not limited to the input of the prescription previously carried out and the symptom changed based on the prescription by searching based on the 1 st relevance processing operation, and the prescription previously carried out may be input regardless of the input. In this case, the range can be further widened compared to the conventional prescription, and the conventional lifestyle can be further referred to. The past lifestyle may be formed of history information of the lifestyle up to now, or may be intermittently detected at a previous time. Similarly, the change symptom may be a history of the change of the symptom so far, or may be a symptom intermittently detected at a previous time.

Prescription search for visuals

Fig. 14 shows an example in which 1 st association degrees of 3 stages or more are previously defined between each symptom related to the appearance and the prescription. The symptoms are arranged on the left side with the 1 st degree of association, and the parts are arranged on the right side with the 1 st degree of association. The 1 st relevance shows which prescription is more relevant and to what extent, for the symptoms arranged on the left.

The 1 st relevance relates 1 or more prescriptions to a combination of each symptom, lifestyle habit, and attribute information.

The symptoms include various symptoms related to the appearance, including direct symptoms such as facial darkness, facial wrinkles, facial spots, and indirect symptoms such as moisture content of the skin and mineral content in the body. These symptoms include indices shown in various medical data, and also include results (including VAS evaluation) perceived by doctors, patients, evaluators, and the like as sensory scores.

Lifestyle contains all relevant matters about the life of the patient. The lifestyle habits include, for example, intake items taken during the diet, the amount of the diet, and the time of the diet if they are matters related to dietary life. The ingested material as referred to herein includes all articles that can be ingested by a patient, and is food, beverages, health products, medicines, and the like. The lifestyle may include a sleep time, a time of getting up, a time of going to bed, and the like if the lifestyle is related to a sleep event. The lifestyle may include exercise time and exercise items if it is a matter related to exercise. In addition, the lifestyle habits, if the care of the skin is performed, include the maintenance of the skin with a cosmetic liquid, measures for preventing ultraviolet rays, cleansing, massage, and the like.

The attribute information of the patient includes information such as age, occupation, presence or absence of a person living in the same house, presence or absence of a person having the same symptom in both parents, and a physical examination result of a medical institution.

The prescription includes all prescriptions for alleviating symptoms, which nutrients are taken, which lifestyle habits are adopted, and which medicaments should be taken. Additionally, the prescription includes a medical procedure. In the prescription, a schedule and treatment plan of the prescription that should be implemented may be prompted.

In the example of fig. 14, a node R, a node S, and a node T are provided, the node R being a combination of a case where the face is dark at level 3 in the symptom item, a case where the amount of protein in the lifestyle habit (intake) item is small, and an age of 30 to 40 years in the attribute item, the node S being a combination of a case where wrinkles of the face are at level 2 in the symptom and a case where the protein in the lifestyle (intake) is large, the node T being a combination of a case where the face is dark at level 2 in the symptom item and an age of 40 years or more in the attribute information item. The 1 st association degree between the node R and "exercise performed for 3 minutes or more per day" was 70%, and the 1 st association degree between the node R and "moisturizing cosmetic liquid 3 mg/day" was 20%. In addition, the 1 st correlation between the node S and "cytokine combination introduction 0.1 mg/time" was 70%, the 1 st correlation between "increase in red meat intake" was 50%, and the 1 st correlation between "moisturizing cosmetic liquid 3 mg/day" was 30%. The 1 st association between node T and "amino acid nutraceutical 2 g/day" was 60%, and the 1 st association between "exercise performed 3 minutes or more per day" was 40%.

The 1 st relevance degree of such a combination is obtained in advance. Next, the search unit 27 refers to the 1 st relevance degree and determines which item on the left side of the 1 st relevance degree the symptom, lifestyle habit, and attribute information newly input through the operation unit 25 corresponds to. If the face darkness among the symptoms newly input by the operation unit 25 is class 3, the amount of protein is small in the lifestyle (intake) items, and the age is 30 to 40 years in the attribute items, the new input symptoms correspond to the node R in this case, and therefore, "exercise performed for 3 minutes or more per day" with a1 st association of 70% and "moisturizing cosmetic liquid 3 mg/day" with a1 st association of 20% are selected.

It is to be understood that the items of symptoms, lifestyle habits, attribute information, and prescriptions listed in fig. 14 are examples, and the above-described processing operation may be executed as long as the items correspond to the symptoms, lifestyle habits, attribute information, and prescriptions.

The 1 st relevance is not limited to the above embodiment. In the example of fig. 15, the 1 st association degree is defined for the node U corresponding to the combination of the plurality of symptoms. That is, the node U is connected to: the darkness of the face is rated 1, the wrinkles of the face are rated 2, the amount of protein in the lifestyle (intake) items is large, and the age in the attribute information is 40 years or more. I.e. more than 2 symptoms are connected to node U.

In addition to the symptoms, a combination of 2 or more lifestyle habits and a combination of 2 or more attribute information may be associated with the node U.

In addition, according to the present invention, the database 3 may be referred to, and the database 3 may store 3 or more stages of 2 nd correlation between each change symptom of the prescription previously performed and the patient who performed the prescription and the new prescription in advance.

Fig. 16 shows an example of the 2 nd degree of association. The previously performed parts and changed symptoms are arranged on the left side with the 2 nd degree of association, and the new parts are arranged on the right side with the 2 nd degree of association.

In the example of fig. 16, a node V and a node W are provided, and the node V is connected to: the node W, which was previously prescribed as "cytokine combination introduction 0.1 mg/time", "increase in red meat intake", and darkness of the face as a change symptom, was ranked 1, and the following were connected to the node W: the "moisturizing cosmetic liquid 3 mg/l", the "increase in the intake of red meat", and the dull complexion level 2 and the wrinkles level 2, which were previously prescribed. Moreover, the 2 nd degree of association between this node V and "cytokine combination introduction 0.1 mg/time" as a new prescription was 70%, and the 2 nd degree of association with "isoflavone type health products 1 g/day" was 40%. The 2 nd degree of association between the node W and "moisturizing cosmetic liquid 3 mg/day" as a new prescription is 80%, and the 2 nd degree of association between "exercise performed for 3 minutes or more per day" is 30%.

The 2 nd degree of association of such a combination is obtained in advance. Next, the search unit 27 refers to the 2 nd degree of association, and determines which item of items arranged on the left side of the 2 nd degree of association each change symptom of the prescription and the patient who has given the prescription newly input through the operation unit 25 corresponds to. If the previously applied recipe inputted via the operation unit 25 is "3 mg/l of moisturizing cosmetic liquid" and "increase the intake of red meat", and the change symptom is that the face is dark at level 2 and the wrinkles on the face are at level 2, this case corresponds to the node W. In this case, the new prescription is selected such as "moisturizing cosmetic liquid 3 mg/day" and "exercise is performed for 3 minutes or more per day" with reference to the 2 nd correlation degree of the node W.

Further, the 2 nd degree of association may be updated in the same manner. That is, the prescription, each change symptom, and the new prescription that have been previously implemented as shown in fig. 10 are updated as needed. The update may reflect information provided via a public communication network typified by the internet, for example. In addition, the system side or the user side may be updated manually or automatically according to the contents of research data, papers, academic publications, news reports, books, and the like of experts. Artificial intelligence can be utilized in these update processes.

It is to be understood that the 2 nd degree of association may be associated with the prescription by combining 1 of the above-described intake, attribute information, lifestyle habits, character and external information, in addition to the prescription and each change symptom which have been previously carried out.

The 2 nd relevance processing operation is not limited to the input of the prescription previously carried out and the symptom changed based on the prescription by searching based on the 1 st relevance processing operation, and the prescription previously carried out may be input regardless of the input. In this case, the range can be further expanded than the conventional prescription, and the conventional lifestyle can be referred to. The past lifestyle may be formed of history information of the lifestyle up to now, or may be intermittently detected at a previous time. Similarly, the change symptom may be a change history of the symptom so far, or may be a symptom intermittently detected at a previous time.

Description of the reference symbols

1: a prescription search system; 3: a database; 21: an internal bus; 23: a display unit; 24: a control unit; 25: an operation section; 26: a communication unit; 27: a search section; 28: a storage section.