KR102562774B1 - health management method that provides a priority action guide that can efficiently improve the risk of disease occurrence, and an electronic device that performs the same - Google Patents

Abstract

Disclosed are a health management method providing a priority action guide capable of efficiently improving the risk of disease occurrence, and an electronic device performing the same. A health management method executed by the disclosed processor includes the steps of identifying user's body information and health information, using a scoring table modeling a plurality of risk factors for disease occurrence, and determining a user's disease occurrence from body information and health information. determining the risk level of the user, determining the user's pancreatic cancer risk factor (PCRF) type from body information and health information, and selecting the user first among a plurality of risk factors based on at least one of the PCRF type and the scoring table. Determining one to be improved as a priority action guide, and providing the priority action guide together with the user's risk level and PCRF type to the user. PCRF types are classified into multiple risk factors.

Description

A health management method that provides a priority action guide that can efficiently improve the risk of disease occurrence, and an electronic device that performs the same}

The following description relates to a health management method that provides a priority action guide that can efficiently improve the risk of disease occurrence and an electronic device that performs the same.

With a lot of knowledge about cancer, it is well known what habits, behaviors and food intake cause and exacerbate cancer. However, there is a lack of information about which factors have the most influence on cancer occurrence among cancer-causing factors, so there are cases in which factors with little effect are improved first. For example, among the factors that cause pancreatic cancer, improving your weight first rather than drinking habits can lower the incidence of pancreatic cancer the most, but if you improve your drinking habits first, the decrease in the incidence of pancreatic cancer may be weaker than the former.
A conventional profile providing method includes Patent Publication No. 10-2020-0064770 filed on November 29, 2018 and published on June 8, 2020 (Title of Invention: Health Management Device and Operation Method Thereof) .

A health management method, executed by a processor according to an embodiment, includes checking body information and health information of a user; determining a risk level of the user for the occurrence of the disease from the body information and the health information by using a scoring table modeling a plurality of risk factors for occurrence of the disease; determining a Pancreatic Cancer Risk Factor (PCRF) type of the user from the body information and the health information; determining one of the plurality of risk factors to be improved first as a priority action guide based on at least one of the PCRF type and the scoring table; and providing the priority action guide to the user together with the user's risk level and the PCRF type, wherein the PCRF type is classified into the plurality of risk factors.

In the health management method according to an embodiment, the step of determining one of the plurality of risk factors as the priority action guide may include selecting a risk factor having the highest corresponding item score among the plurality of risk factors as the priority action guide. You can decide as a guide.

In the health management method according to an embodiment, the step of determining one of the plurality of risk factors as the priority action guide may include sequentially prioritizing the plurality of risk factors according to the priority assigned in the PCRF type. can be determined by

In the health management method according to an embodiment, the step of determining the user's risk may include determining individual scores corresponding to the body information and health information of the user through the scoring table, and summing the individual scores to determine the user's risk level. A final score may be determined, and a risk level of the user may be determined based on the final score.

The health management method according to an embodiment further includes determining whether at least one of the user's body information and health information has been updated, and determining the user's risk level includes at least one of the user's body information and health information. It is determined whether an update to one is due to an improvement in a risk factor, and if the update is due to an improvement in a risk factor, the weight determined based on the change in the final score determined through the scoring table When the user's risk level is determined to be low by a predetermined ratio, and the update is due to deterioration of risk factors, the user's risk level may be determined to be high by a predetermined ratio through the weight.

In the health management method according to an embodiment, the predetermined ratio may be determined in proportion to the number of times the risk factor is improved or deteriorated.

An electronic device according to an embodiment includes a processor; and a memory including at least one instruction executable by the processor, and when the at least one instruction is executed by the processor, the processor checks the user's body information and health information, and determines a plurality of diseases. Using a scoring table modeling risk factors of, determining the user's risk level for the occurrence of the disease from the body information and the health information, determining the user's PCRF type from the body information and the health information, Based on at least one of the PCRF type and the scoring table, one of the plurality of risk factors to be improved first is determined as a priority action guide, and the priority action guide is determined as a risk level of the user. and provided to the user together with the PCRF type.

According to an embodiment, the risk of disease occurrence can be effectively reduced by providing a priority action guide most suitable for the user's health and physical condition. In addition, by providing a priority action guide corresponding to the PCFR type to which the user belongs or by providing a risk factor corresponding to the item with the highest score among the individual item scores determined through the scoring table as a priority action guide, It enables them to recognize and put into action the most effective ways to improve. In addition, the user's will to improve may be reflected in the final score for determining the risk of disease by applying a weight according to whether the user has improved the risk of disease occurrence according to the priority action guide.

1 is a diagram illustrating an electronic device according to an exemplary embodiment.
2 is a diagram illustrating a health management method according to an embodiment.
3 and 4 are views for explaining a scoring table according to an exemplary embodiment.
5 and 6 are diagrams for explaining a process of determining a user's risk level for disease occurrence using a scoring table according to an embodiment.
7 and 8 are views for explaining PCRF types according to an embodiment.
9 and 10 are diagrams for explaining a process of determining a user's risk level and a PCRF type according to an embodiment.
11 is a diagram for explaining a process of determining a user's priority action guide according to an embodiment.
12 is a diagram for explaining a user's risk level change based on a priority action guide according to an embodiment.

Specific structural or functional descriptions of the embodiments are disclosed for illustrative purposes only, and may be changed and implemented in various forms. Therefore, the form actually implemented is not limited only to the specific embodiments disclosed, and the scope of the present specification includes changes, equivalents, or substitutes included in the technical idea described in the embodiments.

Although terms such as first or second may be used to describe various components, such terms should only be construed for the purpose of distinguishing one component from another. For example, a first element may be termed a second element, and similarly, a second element may be termed a first element.

It should be understood that when an element is referred to as being “connected” to another element, it may be directly connected or connected to the other element, but other elements may exist in the middle.

Singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, terms such as "comprise" or "have" are intended to designate that the described feature, number, step, operation, component, part, or combination thereof exists, but one or more other features or numbers, It should be understood that the presence or addition of steps, operations, components, parts, or combinations thereof is not precluded.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related art, and unless explicitly defined in this specification, it should not be interpreted in an ideal or excessively formal meaning. don't

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. In the description with reference to the accompanying drawings, the same reference numerals are given to the same components regardless of reference numerals, and overlapping descriptions thereof will be omitted.

1 is a diagram illustrating an electronic device according to an exemplary embodiment.

Referring to FIG. 1 , an electronic device 100 includes a memory 110 and a processor 120 . The electronic device 100 is a device that presents an action guide capable of effectively suppressing the occurrence of diseases of the user in consideration of the user's health and physical condition, and is, for example, a mobile phone, smart phone, tablet, laptop, personal computer, electronic device Various computing devices such as book devices, various wearable devices such as smart watches, smart glasses, HMD (Head-Mounted Display), and smart clothing, various home appliances such as smart speakers, smart TVs, and smart refrigerators, smart cars, smart kiosks, and IoT (Internet of Things) devices, WADs (Walking Assist Devices), drones, robots, and the like. In addition, the electronic device 100 may be implemented as various devices carried or controlled by the user.

The memory 110 may include computer readable instructions. The processor 120 may perform operations described below as instructions stored in the memory 110 are executed by the processor 120 . Memory 110 may be volatile memory or non-volatile memory.

The processor 120 may be a module that executes instructions or programs or controls the electronic device 100 . The processor 120 identifies the user's body information and health information, determines the user's risk level for disease occurrence from the body information and health information using a scoring table in which a plurality of risk factors for disease occurrence are modeled, and The user's PCRF type is determined from information and health information, and based on at least one of the PCRF type and the scoring table, one of the plurality of risk factors to be improved first is determined as a priority action guide, and A ranking action guide is provided to the user along with the user's risk level and PCRF type.

It will be described in detail with reference to the drawings below.

2 is a diagram illustrating a health management method according to an embodiment.

Referring to FIG. 2 , a health management method performed by a processor included in an electronic device is illustrated.

In step 210, the electronic device checks the user's body information and health information.

Body information includes height, weight, waist circumference, left and right vision, blood pressure, heart rate, etc., and can be measured through a wearable device that can be attached to or detached from the user's body and/or a separate measuring device. Health information includes smoking status, smoking amount, age at which smoking started, alcohol consumption, alcohol consumption, eating habits, number of meat and vegetable intake, number of exercises, personal history and family history, etc. ) can be entered through.

Also, the electronic device may determine at least one of the user's body information and health information based on the user's examination information (eg, health examination results) received from an external server (eg, the National Health Insurance Service). To this end, the electronic device may perform an authentication procedure of the external server using the user's public certificate or biometric information (eg, fingerprint, iris, facial information, etc.).

In step 220, the electronic device determines the user's risk of disease occurrence from body information and health information by using a scoring table modeling a plurality of disease risk factors. The electronic device may calculate the user's final score through the scoring table and determine the user's risk level based on the final score. Risk can be expressed as a grade or rank indicating the degree of risk of disease occurrence.

In step 230, the electronic device determines the user's PCRF type from body information and health information. The PCRF type may be a type composed of factors that pose a risk to pancreatic cancer, which is difficult to diagnose early and has a poor prognosis if detected in a fairly advanced state even if diagnosed.

In step 240, the electronic device determines one of the plurality of risk factors to be improved first as a priority action guide based on at least one of the PCRF type and the scoring table. The electronic device may determine, as a priority action guide, a risk factor having the greatest influence on a user's risk of developing a disease among a plurality of risk factors. And, the electronic device provides the user with a priority action guide together with the user's risk level and PCRF type. The electronic device may provide the current status of the user and a priority action guide personalized to the user.

In step 250, the electronic device may determine whether at least one of the user's body information and health information has been updated. If at least one of the body information and health information is updated (eg, no smoking, no drinking, weight loss, etc.), step 210 may be performed again. Conversely, if the body information and health information are not updated, the electronic device may end its operation.

According to an embodiment, a weighting algorithm may be additionally applied to inspire the user's will to improve by applying a weight to the final score based on whether the user has improved the disease risk according to the priority action guide. For example, when at least one of the body information and health information is updated in step 250, a final score determined by applying a weighting algorithm to at least one of the updated body information and health information may be applied in step 220. This weighting algorithm may be used to evaluate the risk of disease occurrence lower due to the active attitude of the user if the user has improved according to the priority action guide. Conversely, if the user does not improve according to the priority action guide and rather deteriorates, the risk of disease occurrence may be evaluated higher due to the user's passive attitude. Here, improvement or deterioration may be determined based on whether a score of an individual item based on a scoring table to be described with reference to FIGS. 3 and 4 increases or decreases. For example, if the frequency of smoking decreases from 0.7 packs per day to 0.3 packs per day, the corresponding item score also decreases from 5 points to 3 points, which may correspond to improvement. Conversely, if the frequency of smoking increases from 0.7 to 1.1 packs per day, the corresponding item score also increases from 5 to 7, which may correspond to an aggravation.

For example, in the case of improvement, a score by a predetermined ratio (eg, 0.1) to the extent to which the final score is lowered due to improvement may be additionally determined to be lower. If the final score is lowered from 30 points to 20 points due to improvement, the final score may be determined to be 19 points by additionally lowering 1 point, which is the result of multiplying the reduced degree of 10 points by a predetermined ratio of 0.1. Furthermore, the size of a predetermined ratio may be increased according to the number of improvements. For example, in the case of two improvements, a score by twice a predetermined ratio (eg, 0.2) may be additionally determined to be lower than the degree to which the final score is lowered by the improvement. If the final score is lowered from 19 points to 9 points due to the second improvement, 2 points, which is the result of multiplying the lowered 10 points by 0.2, which is twice the predetermined ratio of 0.1, can be further lowered to determine the final score as 7 points. If it is the n-th improvement, a score by n times a predetermined ratio may be additionally determined to be lower. In the case of deterioration, as opposed to the case of improvement described above, the final score may be determined higher, so a detailed description thereof will be omitted.

3 and 4 are views for explaining a scoring table according to an exemplary embodiment.

Referring to FIG. 3 , a scoring table for men according to an embodiment is shown. The scoring table models a plurality of risk factors for disease occurrence and may include scores corresponding to beta values of each risk factor developed as a statistical model.

For example, age is divided into 10 categories, and scores may be distributed from -24 to 24 points. Height is divided into 4 categories, and the score can be distributed from 0 to 1 point. BMI (body mass index) is divided into 4 categories, and the score can be distributed from 0 to 1 point. Urine glucose is divided into two categories, and the score can be distributed from 0 to 2 points. Smoking is divided into 5 categories, and the score can be distributed from 0 to 7 points. Smoking initiation age is divided into two categories, and the score can be distributed from 0 to 1 point. Blood glucose is divided into two categories, and the score can be distributed from 0 to 2 points. A final score of the user may be determined by adding all the scores corresponding to the user. A lower final score may mean a lower risk of disease occurrence.

The scoring table according to an embodiment may be determined based on a risk management index for each disease generated through raw data of 2 million cases of user examination data observed for 8 years.

Here, the risk management index for each disease predicts the risk of developing five diseases, including digestive disease, heart disease, circulatory disease, liver function disease, and lung function disease, by analyzing the user's body information, health information, and examination information. could be an indicator. By analyzing the data tracked for 8 years in the disease risk prediction repository, the value of the data can be indexed to predict the probability of occurrence for each disease, and the risk management index for each disease can be determined by expressing the degree of risk for each disease step by step. For example, if smoking is selected according to the user's health information, the amount of cigarettes smoked per day is 20, and the first starting age is 18 years old, the stage of occurrence of lung function disease may be determined as the highest stage. A technique for deriving the corresponding result may be based on a deep learning algorithm or the like.

The predicted risk of occurrence for each disease may be the risk of the user developing five diseases including digestive disease, cardiac disease, circulatory disease, liver function disease, and pulmonary function disease. The electronic device may determine the prediction risk of occurrence of each disease by comparing and analyzing the risk management index for each disease in the disease occurrence risk prediction storage and the risk management index for each disease analyzed from user information in the user's gender and age group.

The disease information database may store cumulative data continuously received from the user for a predetermined period of time, such as body information, health information, and checkup information of the user, and the electronic device identifies a data pattern from the accumulated data to provide risk management indicators for each disease of the user. can decide Here, the physical information may include age, gender, height, weight, etc., and the health information may include whether or not to smoke, how much smoking, whether or not to drink, how much alcohol to drink, eating habits, whether or not to exercise, the number of times of exercise, personal history, family history, etc. In addition, the examination information may include blood test results such as waist circumference, left and right visual acuity, blood pressure, hemoglobin, fasting blood sugar, and total cholesterol.

Referring to FIG. 4 , a scoring table for women according to an exemplary embodiment is shown. The scoring table models a plurality of risk factors for disease occurrence and may include scores corresponding to beta values of each risk factor developed as a statistical model.

For example, age is divided into 10 categories, and scores may be distributed from -22 to 20 points. Height is divided into 4 categories, and the score can be distributed from 0 to 2 points. BMI is divided into 4 categories, and the score can be distributed from 0 to 3 points. Urine glucose is divided into two categories, and the score can be distributed from 0 to 4 points. Smoking is divided into 3 categories, and the score can be distributed from 0 to 6 points. Drinking is divided into two categories, and the score can be distributed from 0 to 1 point. Blood glucose is divided into two categories, and the score can be distributed from 0 to 1 point.

5 and 6 are diagrams for explaining a process of determining a user's risk level for disease occurrence using a scoring table according to an embodiment.

Referring to FIG. 5 , an example of determining a male user's risk level using a scoring table is shown. The male user's final score determined using the male scoring table of FIG. 3 may have a score between the lowest point of -24 points and the highest point of 38 points, which is divided into 5 equal parts and classified into 5 grades: excellent, good, normal, caution, and warning. can do. In addition, the rank of the corresponding user may be determined by dividing the final score into 1 to 100.

The electronic device calculates the risk management index for each disease in the disease risk prediction storage and the risk management index for each disease of the user calculated through the disease information database in the user's gender and age group to calculate the predicted risk for each disease. can The user's disease occurrence risk ranking may be generated by referring to an occurrence risk ranking indicator for each disease obtained by dividing the calculated disease occurrence prediction risk through the data of the disease occurrence prediction repository by dividing it into 100 equal parts. The ranking is calculated as follows.

The cutoff range of the ranks can be determined by dividing the difference between P _worst of the lowest score and P _best of the highest score by 100 equal parts.

However, if the rank is calculated using the cutoff determined through the above method, a result may be obtained that the cases classified from 1st to 100th do not come out evenly and are excessively biased to 1st to 10th. Therefore, the cutoff may be determined as in the following equation.

Using the data of the disease outbreak prediction storage described above, it is possible to check the unique value and number of probabilities that can actually occur. A total of 8,403 unique probabilities for women can be identified, and a total of 15,201 unique probabilities for men can be identified. The cutoff range of the ranks can be determined by dividing the probability value unique to both genders into 100 equal parts.

If the Wilcoxon Rank Sum Test (W = 3.6895e + 10, p-value < 2.2e-16) is applied to the method using Equations 1 and 2 described above, in the method of Equation 1, the rank is excessive in the 1st to 10th places. On the other hand, in the method of Equation 2, it can be confirmed that the ranks are relatively evenly distributed.

From 1st to 100th, it can be divided into 5 levels: excellent, good, average, caution, and warning. Grade 1, Excellent, is the lowest level of risk for each disease and is included in the rankings from 1st to 20th, and Good is the second lowest level among the rankings of risk for each disease, from 21st to 40th. It is a grade included in the ranking, and is usually a grade included in the ranking from 41st to 60th as the middle level of the ranking of risk of occurrence by disease, and caution is a grade included in the ranking from 61st to 80th as the second highest level among the ranking of risk of occurrence by disease. A warning is a grade included in the rankings from 81st to 100th as the highest level among risk rankings for each disease.

The user can intuitively grasp the degree of risk of occurrence of each disease by referring to the rank and grade of the risk of occurrence of each disease assigned to the user. The electronic device may determine the rank and grade of risk of occurrence of each disease in the same sex for the user, or the rank and rank of risk of occurrence of each disease in the same gender and age for the user. In addition, information on how serious the risk level of each disease received by the user may be additionally provided compared to other grades. For example, when the user's rating is determined to be good, information about how many times higher the risk of each disease occurs compared to the good rating may be additionally provided to the user.

According to another embodiment, the electronic device determines the risk rate of disease occurrence (eg, t2-t1) based on the amount of change in the amount of time data is entered through the user's accumulated data in the disease information database (eg, t2-t1) and the amount of change in the predicted risk of occurrence for each disease. v) can be judged. Here, the increase in the predicted risk of occurrence for each disease may be the rate of change in the degree of progression of the risk of developing a disease or the degree of occurrence of a biological disease based on the time each data is input in the accumulated data, for example, the amount of change in the final score , the amount of change in rank, the amount of change in grade, and the like. In addition, through the tracked data in the disease occurrence risk prediction storage, whether the user's disease occurrence risk rate is fast or slow may be evaluated and provided to the user.

In addition, the electronic device may determine the order of occurrence of the disease at the current age of the user based on the user's pre-calculated risk rate of disease occurrence. Methods of predicting future disease risk rankings may be based on techniques such as deep learning algorithms. In addition, the average disease risk rate can be calculated by analyzing the accumulated data in the disease risk prediction repository, and future disease risk rankings can also be calculated.

Referring to FIG. 6 , an example of determining the risk level of a female user using a scoring table is shown. The female user's final score determined using the female scoring table of FIG. 4 may have a score between the lowest point -23 points and the highest point 37 points, which is divided into 5 equal parts and classified into 5 grades: excellent, good, normal, caution, and warning. can do. In addition, the rank of the corresponding user may be determined by dividing the final score into 1 to 100.

7 and 8 are views for explaining PCRF types according to an embodiment.

Referring to FIG. 7, a male PCRF type is shown, and referring to FIG. 8, a female PCRF type is shown. Some of the plurality of risk factors modeled in the scoring table may correspond to unchanging items that are difficult to change through user effort, such as age and height, and others may correspond to variable items that can be improved through user effort. The PCRF type is a type set based on variable items, and enables the user to easily grasp items that can be improved through effort.

The PCRF type is difficult to diagnose early, and even if diagnosed, it can be created based on risk factors for pancreatic cancer, which is found in a fairly advanced state and has a poor prognosis. Factors of type are (1) Lean (low weight), Normal (normal weight), Overweight (overweight), Obese (obesity) of BMI (degree of obesity), (2) Normal (normal blood sugar), High ( High urine sugar), (3) never smoker of SMOKING, past smoker, Smoking-1, 2, 3 (smoker), (4) not- of SMOKING INITIATION Fast (25 years of age or older), fast (less than 25 years of age), (5) Normal (normal blood sugar), Hight (high blood sugar) of BLOOD GLUCOSE (blood sugar) may be included. All factors may be determined based on information input from a user or measured by a sensor. For non-normal factors, related risk disease information may be provided to the user.

For example, if the PCRF type is 'ObNS1FH', 'Ob' means BMI is obese, 'N' means Urine GLUCOSE is normoglycemic, 'S1' means SMOKING is a smoker, and 'F' means SMOKING INITIATION is the first Smoking is under the age of 24, and 'N' may indicate that BLOOD GLUCOSE is normoglycemic. In this type, for 'obesity' as a user, risk information of 'high blood pressure, dyslipidemia, coronary artery disease, risk of cardiovascular diseases such as stroke, and digestive diseases, musculoskeletal disorders, diabetes and cancer.' Regarding smoking, 'Carcinogens in tobacco can cause various cancers such as lung cancer, stomach cancer, cervical cancer, larynx cancer, and pancreatic cancer, and there is a risk of getting diseases of the cardiovascular system, respiratory system, and reproductive system.' It can be. Users classified as the corresponding type can set a target value to change the risk factor to normal in the goal setting unit with awareness of the variables that have emerged as risk. When the user achieves the target value, the user can receive the PCRF result value, check the risk variable again, perform health improvement activities in the target setting unit, and receive the result accordingly. PCRF can be typed with different variables depending on the disease.

9 and 10 are diagrams for explaining a process of determining a user's risk level and a PCRF type according to an embodiment.

Referring to FIG. 9 , the electronic device may check user's body information and health information. The electronic device may check the user's body information and health information based on information input from the user, detected through a sensor, or information received from an external server. For example, the user's gender is male, age is 43 years old, height is 167 cm, BMI is 23.2, urine glucose level is negative, blood sugar level is 120, smoking 0.4 pack a day, smoking 23 years old , the user's final score is determined as -1, the pancreatic cancer risk rank is determined as 41st, the pancreatic cancer risk rank is determined as good, the PCRF type is ONS1F, and it can be medium to high in the corresponding type. 10 illustrates the process of determining the score for each risk factor described above and calculating the total score, grade, and rank. The electronic device may guide risk factors to be improved in the first place in order to reduce the incidence of pancreatic cancer of the user.

11 is a diagram for explaining a process of determining a user's priority action guide according to an embodiment.

Referring to FIG. 11, a table for determining a priority action guide according to a PCRF type is shown as an example. In that table, the grid marks indicate the risk factors that need to be improved first in that PCRF type, the hatch marks indicate the risk factors that need improvement in second place in that PCRF type, and the dots indicate the risk factors that need improvement in that PCRF type. Thirdly, it can indicate risk factors that need to be improved. For example, users corresponding to the PCRF type of OHS3 can confirm that smoking needs to be improved first, then sugar needs to be lowered, and then BMI needs to be improved. . The electronic device may enable the user to effectively reduce the risk of disease occurrence by providing a priority action guide corresponding to the type of PCRF to which the user belongs.

12 is a diagram for explaining a user's risk level change based on a priority action guide according to an embodiment.

Referring to FIG. 12 , an example of a score change of users A and B is shown when only user B among users A and B under the same condition improves their constitution based on the priority action guide. As described above, since a higher score means a higher risk of disease, it may be important to lower the score as quickly as possible through constitutional improvement.

The genders of users A and B are male, they are 41 years old, and their height is 178 cm. The sum of scores of constant items among a plurality of risk factors may be -6 points. In addition, the PCRF types of users A and B may be OHS2H, and the final score considering variable items may be 6 points. The following table may indicate the details of the PCRF type of OHS2H.

The electronic device may sequentially provide prioritized action guides to the user to improve risk factors corresponding to the highest scores among the scores of detailed items included in the PCRF type of OHS2H. Referring to Table 1, it can be seen that the score due to smoking has the highest score of 7 points, followed by urine glucose and blood sugar of 2 points, and finally BMI of 1 point. The electronic device may provide the user with a priority to improve smoking as a priority action guide. If the user improves his smoking, the electronic device may then guide him to improve either one of urine sugar and blood sugar (eg, urine sugar). Next, the electronic device may guide improvement of the other one of urine sugar and blood sugar (eg, blood sugar). Through this, the user B can reduce the risk of disease occurrence effectively by improving the risk factors in the order of high risk of disease occurrence.

On the other hand, if user A indiscriminately improves without a prioritized action guide, he may progress from overweight to underweight rather than normal, and the risk of developing a disease may rather increase. As a result, it may be inefficient for user A to lower the risk of developing a disease compared to user B.

The embodiments described above may be implemented as hardware components, software components, and/or a combination of hardware components and software components. For example, the devices, methods and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate (FPGA). array), programmable logic units (PLUs), microprocessors, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and software applications running on the operating system. A processing device may also access, store, manipulate, process, and generate data in response to execution of software. For convenience of understanding, there are cases in which one processing device is used, but those skilled in the art will understand that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it can include. For example, a processing device may include a plurality of processors or a processor and a controller. Other processing configurations are also possible, such as parallel processors.

Software may include a computer program, code, instructions, or a combination of one or more of the foregoing, which configures a processing device to operate as desired or processes independently or collectively. The device can be commanded. Software and/or data may be any tangible machine, component, physical device, virtual equipment, computer storage medium or device, intended to be interpreted by or provide instructions or data to a processing device. , or may be permanently or temporarily embodied in a transmitted signal wave. Software may be distributed on networked computer systems and stored or executed in a distributed manner. Software and data may be stored on computer readable media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer readable medium. A computer readable medium may store program instructions, data files, data structures, etc. alone or in combination, and program instructions recorded on the medium may be specially designed and configured for the embodiment or may be known and usable to those skilled in the art of computer software. there is. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. - includes hardware devices specially configured to store and execute program instructions, such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of program instructions include high-level language codes that can be executed by a computer using an interpreter, as well as machine language codes such as those produced by a compiler.

The hardware device described above may be configured to operate as one or a plurality of software modules to perform the operations of the embodiments, and vice versa.

As described above, although the embodiments have been described with limited drawings, those skilled in the art can apply various technical modifications and variations based on this. For example, the described techniques may be performed in an order different from the method described, and/or components of the described system, structure, device, circuit, etc. may be combined or combined in a different form than the method described, or other components may be used. Or even if it is replaced or substituted by equivalents, appropriate results can be achieved.

Therefore, other implementations, other embodiments, and equivalents of the claims are within the scope of the following claims.

Claims (7)

In the health management method, executed by a processor,
checking user's body information and health information;
determining a risk level of the user for the occurrence of the disease from the body information and the health information by using a scoring table modeling a plurality of risk factors for occurrence of the disease;
determining a pancreatic cancer risk factor (PCRF) type of the user from the body information and the health information;
determining one of the plurality of risk factors to be improved first as a priority action guide based on at least one of the PCRF type and the scoring table; and
Providing the priority action guide to the user together with the risk level of the user and the PCRF type
including,
The PCRF type is classified into the plurality of risk factors,
The step of determining the user's risk is
determining whether an update of at least one of the body information and health information is due to an improvement in a risk factor;
When the update is due to improvement in the risk factor, determining the risk of the user as low as a predetermined ratio through a weight determined based on a change in the final score determined through the scoring table;
If the update is due to deterioration of the risk factor, determining the risk of the user as high as a predetermined ratio through the weight,
health care method.
According to claim 1,
The step of determining any one of the plurality of risk factors as the priority action guide
Determining a risk factor having the highest corresponding item score among the plurality of risk factors as the priority action guide,
health care method.
According to claim 1,
The step of determining any one of the plurality of risk factors as the priority action guide
Determining the plurality of risk factors sequentially as a priority action guide according to the priority specified in the PCRF type,
health care method.
According to claim 1,
The step of determining the user's risk is
Individual scores corresponding to the body information and health information of the user are determined through the scoring table, a final score of the user is determined by summing the individual scores, and a risk level of the user is determined based on the final score. doing,
health care method.
delete According to claim 1,
The predetermined ratio is determined to be proportional to the number of times the risk factor is improved or deteriorated,
health care method.
processor; and
memory containing at least one instruction executable by the processor
including,
When the at least one instruction is executed in the processor, the processor
Check the user's physical and health information;
determining the risk of the user for the occurrence of the disease from the body information and the health information using a scoring table modeling a plurality of risk factors for the occurrence of the disease;
determining the user's PCRF type from the body information and the health information;
Based on at least one of the PCRF type and the scoring table, one of the plurality of risk factors to be improved first is determined as a priority action guide;
Providing the priority action guide to the user together with the risk level of the user and the PCRF type;
The processor
determining whether the update of at least one of the body information and health information is due to an improvement in a risk factor;
When the update is due to improvement in the risk factor, determining the risk of the user as low as a predetermined ratio through a weight determined based on a change in the final score determined through the scoring table;
If the update is due to deterioration of the risk factor, determining the risk of the user as high as a predetermined ratio through the weight,
electronic device.

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