Method, system, device and program for determining possibility degree of secondary hypertension

Abstract

To provide a system for determining a degree of possibility of secondary hypertension in a patient for whom reduction in blood pressure cannot be observed despite that behavioral modification for reducing blood pressure has been implemented.SOLUTION: In a method for determining a degree of possibility of secondary hypertension, one or more computers are caused to execute: a step for acquiring user-related data containing blood pressure value data indicating measured blood pressure values and behavior recording data indicating actions taken by a user; and a step for determining a degree of possibility of essential hypertension and secondary hypertension, on the basis of the acquired user-related data, an essential hypertension prediction model based on a behavior score indicating actions of the user, and a secondary hypertension prediction model.SELECTED DRAWING: Figure 6

Description

[Technical field]

[0001] The present invention relates to a method, system, device, and program for determining the likelihood of secondary hypertension. [Background technology]

[0002] There are two types of hypertension: essential hypertension and secondary hypertension. Essential hypertension is hypertension without any underlying disease that could cause hypertension and the cause is unclear. It is known that the causes of essential hypertension involve a variety of factors, such as diet and lifestyle habits. In contrast, secondary hypertension is hypertension caused by diseases of the thyroid gland, adrenal glands, etc.

[0003] Regarding essential hypertension, there is a treatment app that can be installed on a smartphone to assist in the treatment of essential hypertension. Patients can use this treatment app to input the results of their blood pressure measurements at home (home blood pressure values) and to implement behavioral changes and continue to improve their diet and lifestyle habits, thereby achieving a blood pressure lowering effect (Non-Patent Document 1). [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] CureApp HT Hypertension Treatment Support App Product Overview Site (for Patients) (URL: https: / / cureapp.co.jp / productsite / ht / forpatient / ) Summary of the Invention [Problem to be solved by the invention]

[0005] When using the above-mentioned therapeutic app, there was a risk that patients with secondary hypertension would continue to use the therapeutic app without considering the possibility of secondary hypertension, even though the app would not be effective enough to lower their blood pressure.Until now, no system has been developed to determine the degree of possibility of secondary hypertension for patients who do not see a drop in blood pressure despite taking actions to lower their blood pressure. [Means for solving the problem]

[0006] 1. The present invention has been made in view of the above problems, and has the following features: That is, a method in one embodiment of the present invention is a method for determining the degree of possibility of secondary hypertension, which executes the steps of acquiring user-related data including blood pressure value data indicating measured blood pressure values ​​and behavior record data indicating behavior performed by a user, and determining the degree of possibility of essential hypertension and secondary hypertension based on the acquired user-related data, an essential hypertension prediction model based on a behavior score indicating the user's behavior, and a secondary hypertension prediction model.

[0007] 2. In the method described in section 1, the step of determining the degree of possibility of essential hypertension and secondary hypertension may include the steps of calculating an error term in an essential hypertension prediction model and an error term in a secondary hypertension prediction model based on the acquired user-related data, calculating the likelihood of assuming essential hypertension based on the calculated error term in the essential hypertension prediction model, calculating the likelihood of assuming secondary hypertension based on the calculated error term in the secondary hypertension prediction model, and determining the degree of possibility of essential hypertension and secondary hypertension based on the calculated likelihood of assuming essential hypertension and the likelihood of assuming secondary hypertension.

[0008] 3. The method according to item 1 or 2 may further comprise a step of determining the degree of likelihood of secondary hypertension compared with the determined likelihood of essential hypertension.

[0009] 4. The method described in section 3 may further include a step of presenting information on the terminal in response to a request operation input by a user or medical professional to the terminal, the information indicating the degree of possibility of secondary hypertension compared with the determined possibility of essential hypertension.

[0010] 5. In the method according to any one of items 1 to 4, the essential hypertension prediction model can include a term related to the behavior score, and the secondary hypertension prediction model can not include a term related to the behavior score.

[0011] 6. In the method described in any one of items 1 to 5, the behavior record data may include at least one of the amount of exercise, the weight, and the salt intake of the user.

[0012] 7. In the method described in any one of items 1 to 6, the user-related data is acquired during a predetermined period of time, and may include two or more blood pressure value data and two or more behavior record data.

[0013] In the method described in any one of 8.1 to 7, the user-related data includes a blood pressure value input by the user to the user's terminal and information indicating an action performed by the user, and can be transmitted by the user's terminal.

[0014] 9. A computer program in one embodiment of the present invention causes one or more computers to execute the method according to any one of items 1 to 8.

[0015] 10. A system in one embodiment of the present invention is a system for determining the degree of possibility of secondary hypertension, which acquires user-related data including blood pressure value data indicating measured blood pressure values ​​and behavioral record data indicating behavior performed by the user, and determines the degree of possibility of essential hypertension and secondary hypertension based on the acquired user-related data, an essential hypertension prediction model based on a behavioral score indicating the user's behavior, and a secondary hypertension prediction model.

[0016] 11. An apparatus in one embodiment of the present invention is an apparatus for determining the degree of possibility of secondary hypertension, which acquires user-related data including blood pressure value data indicating measured blood pressure values ​​and behavioral record data indicating behavior performed by the user, and determines the degree of possibility of essential hypertension and secondary hypertension based on the acquired user-related data, an essential hypertension prediction model based on a behavioral score indicating the user's behavior, and a secondary hypertension prediction model. Effect of the Invention

[0017] By using the present invention, it is possible to determine the degree of possibility of secondary hypertension in patients who have not seen a decrease in blood pressure despite implementing behavioral changes to lower their blood pressure. [Brief description of the drawings]

[0018] [Figure 1] 1 is a configuration diagram of a system according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a hardware configuration diagram of a client device and a server according to an embodiment of the present invention. [Diagram 3] FIG. 2 is a functional block diagram of a decision server according to an embodiment of the present invention. [Figure 4] FIG. 2 is a functional block diagram of a patient terminal according to an embodiment of the present invention. [Diagram 5] FIG. 2 is a functional block diagram of a doctor terminal according to an embodiment of the present invention. [Figure 6] 2 is a flowchart according to an embodiment of the present invention. [Figure 7] 4 is an example of user-related data according to an embodiment of the present invention. [Figure 8A] 1 is an example of values ​​of each variable in an essential hypertension and secondary hypertension prediction model according to one embodiment of the present invention. [Figure 8B] 7 and 8A according to one embodiment of the present invention. [Figure 9] 4 is a sequence chart according to an embodiment of the present invention. [Figure 10] 4 is a sequence chart according to an embodiment of the present invention. [Figure 11] 13 is an example of a patient terminal screen according to an embodiment of the present invention. [Figure 12] 1 is an example of a patient terminal screen according to an embodiment of the present invention. [Figure 13] 1 is an example of a patient terminal screen according to an embodiment of the present invention. [Figure 14] 1 is an example of a patient terminal screen according to an embodiment of the present invention. [Figure 15] 1 is an example of a doctor terminal screen according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0019] FIG. 1 shows an example of a system configuration diagram of the present invention. The system 100 is used to determine the degree of possibility of secondary hypertension, and includes a network 101, a determination server 102, a patient terminal 103, and a doctor terminal 104. In this embodiment, the system further includes a blood pressure measuring device 105 related to blood pressure measurement, which is connected to the patient terminal 103. For example, the blood pressure measuring device 105 can measure blood pressure according to instructions from a treatment application. Also, for example, the blood pressure measuring device 105 can operate in cooperation with the treatment application by being wirelessly connected to the patient terminal 103.

[0020] In the present embodiment, the therapeutic app (therapeutic application) refers to an application that improves the user's cognition or behavior related to hypertension or is used to treat hypertension, but any application may be used to improve the user's hypertension. In this specification, the therapeutic app and the therapeutic app are used interchangeably. In this specification, a user who uses the therapeutic app to improve hypertension is referred to as a user or a patient. In this specification, hypertension includes not only medical hypertension but also a state in which blood pressure is high to a degree that is desirable to improve from a health perspective, and treatment is not limited to treatment under the instructions of a medical professional, and may include treatment for improving health. In the embodiment of this specification, a doctor is described as an example of a medical professional, but the present invention can be implemented by a medical professional other than a doctor.

[0021] FIG. 2 is a block diagram showing the hardware configuration of a client device 200 that can be used as the patient terminal 103 and the doctor terminal 104 according to an embodiment of the present invention, and a server 250 that can be used as the decision server 102. The client device 200 includes a processing device 201, a display device 202, an input device 203, a storage device 204, and a communication device 205. These components are connected by a bus 210. Note that an interface is interposed between the bus 210 and each component device as necessary. In this embodiment, the client device 200 as the patient terminal is a smartphone, the doctor terminal is a terminal used by a doctor, and the client device 200 as the doctor terminal is a personal computer. However, the client device 200 can be other information processing devices such as a tablet computer and a touchpad as long as it has the above configuration.

[0022] Similarly, the server 250 also includes a processing device 251, a display device 252, an input device 253, a storage device 254, and a communication device 255. Each of these components is connected by a bus 260. Note that an interface is interposed between the bus 260 and each of the components as necessary. In this embodiment, the server 250 is realized by a computer that operates as a server or other information processing device.

[0023] The processing devices 201, 251 control the overall operation of the client device 200 and the server 250, and are, for example, CPUs. The processing devices 201, 251 execute various processes by reading and executing programs and data stored in the storage devices 204, 254. In one example, the processing devices 201, 251 are composed of multiple processors.

[0024] The display devices 202, 252 display application screens and the like to a user (patient or doctor) of the client device 200 and a user (administrator) of the server 250 under the control of the processing devices 201, 251. The display devices can be liquid crystal displays, displays using organic electroluminescence, plasma displays, or the like.

[0025] The input devices 203, 253 are user interfaces that accept input from a user to the client device 200 and the server 250, and are, for example, a touch panel, a touch pad, a keyboard, or a mouse. In this embodiment, the client device 200 as a patient terminal is a smartphone, so the client device 200 has a touch panel as the input device 203, which also functions as the display device 202, and the display device 202 and the input device 203 are integrated. The display device 202 and the input device 203 may be separate devices arranged in different positions. Here, the client device 200 as a doctor terminal and the server 250 are computers, so they are equipped with a keyboard and a mouse as input devices and a liquid crystal display as a display device.

[0026] The storage devices 204 and 254 are storage devices that are included in general smartphones and computers, including storage devices using volatile memory such as RAM and non-volatile memory such as eMMC, UFS, and SSD, and magnetic storage devices, etc. The storage devices 204 and 254 may also include external memory.

[0027] For example, when the client device 200 is used as a patient terminal, the program of the therapeutic application is stored in the storage device 204 and installed, so that the client device 200 can function as a patient terminal capable of executing the therapeutic application. When the client device 200 is used as a doctor terminal, the program for the doctor terminal is stored in the storage device 204 and installed. Similarly, in the storage device of the server 250, a server program for decision processing for the decision server is stored. The server program for the therapeutic application includes functions and various data for performing information processing so that the therapeutic application executed in each of the patient terminals and doctor terminals, which are clients, operates appropriately. In addition, a database physically separated from the client device 200 and the server 250 can be used as the storage device.

[0028] The communication devices 205 and 255 transmit and receive data to and from other devices via the network 101 (omitted in FIG. 2). For example, the communication device 205 performs wireless communication such as mobile communication or wireless LAN, and connects to the network 101. The client device 200 uses the communication device 205 to communicate with the server 250 via the network 101. The communication devices 205 and 255 may perform wired communication using an Ethernet (registered trademark) cable or the like.

[0029] 3 to 5 show examples of functional block diagrams of the decision server 102, the patient terminal 103, and the doctor terminal 104 according to an embodiment of the present invention, respectively. The decision server 102 includes a control unit 301, a display unit 302, an input unit 303, an acquisition unit 304, a storage unit 305, a decision unit 306, a comparison unit 307, and a communication unit 308. The patient terminal 103 includes a control unit 401, a display unit 402, an input unit 403, a storage unit 404, a communication unit 405, and a position determination unit 406. The doctor terminal 104 includes a control unit 501, a display unit 502, an input unit 503, a storage unit 504, and a communication unit 505.

[0030] In this embodiment, the processing devices 201, 251 execute the decision server program, the patient terminal treatment app, and the doctor terminal treatment app stored in the storage devices 204, 254 to realize these functions. Since various functions are realized by loading programs, a part or all of one part (function) may be possessed by another part. These functions may be realized by hardware by configuring electronic circuits or the like for realizing a part or all of each function. Furthermore, these functional units do not need to be realized on one physical information processing device. They may be realized on multiple information processing devices, or all or a part of each storage unit may be realized as a database. Any of the functional units may be realized on another device.

[0031] The control units 301, 401, and 501 perform control processing for performing information processing for treatment by the treatment app of this embodiment, a process of determining the degree of possibility of essential hypertension and secondary hypertension in the determination unit 306, and a process of comparing the degree of possibility of essential hypertension and secondary hypertension in the comparison unit 307. The storage units 305, 404, and 504 store data required for these processes and data acquired by these processes. The display units 302, 402, and 502 are configured using the display devices 202 and 252, and present information to the user of each device. In addition to or instead of the display unit, a presentation unit that presents information by voice, tactile sensation, or the like may be provided. The input units 303, 403, and 503 are configured using the input devices 203 and 253, and accept input from the user of each device. In this embodiment, the patient terminal 103 can use a touch detection function that is generally provided in a smartphone equipped with a touch panel. The communication units 308, 405, and 505 are configured using the communication devices 205, 255, and transmit and receive information to and from other servers, patient terminals, doctor terminals, and the like.

[0032] The acquisition unit 304 of the decision server 102 is realized by using the communication device 255, and acquires user-related data of a patient who is treated using a treatment app. The storage unit 404 stores the acquired user-related data in association with information (user identification information) that can identify the patient who acquired the user-related data. For example, the acquired user-related data is stored in association with application identification information of the treatment app used by the patient who acquired the user-related data.

[0033] Here, the user-related data will be described. The user-related data includes blood pressure value data and behavior record data. The blood pressure value data is data indicating a measured blood pressure value, and may include at least one of a systolic blood pressure value and a diastolic blood pressure value. The blood pressure value data can be measured using a blood pressure measuring device 105 or the like. The behavior record data includes data indicating behavior performed by a patient. The behavior record data can include, for example, the amount of exercise, weight, and salt intake of a user. The user-related data includes blood pressure value data and behavior record data for multiple days, and each blood pressure value data and each behavior record data is stored in association with the date to which each data is associated. The blood pressure value data can be associated with the date on which the blood pressure was measured, and the behavior record data can be associated with the date on which the patient performed the behavior indicated by the behavior record data.

[0034] The behavior record data may include subjective data and objective data. The subjective data in the behavior record data may include, for example, data that the patient himself / herself subjectively evaluates the patient's efforts to exercise or reduce salt intake. The objective data in the behavior record data may include, for example, temperature data and weight data. The temperature data may be temperature data at a representative time in a representative location that can be acquired on a date associated with the user-related data. The temperature of the area where the patient was active may be input, or may be acquired from a weather information server that manages temperature data for each area. The area where the patient was active may be acquired as location information from the patient terminal 103, may be input by the patient, or may be an area including the address of the patient included in the patient's profile information, etc. In addition to or instead of the data that the patient himself / herself subjectively evaluates the patient's efforts to exercise or reduce salt intake, objective data may be used. For example, the number of steps the patient takes in a day measured by a pedometer or the calorie consumption of the patient in a day estimated by using a wearable device may be data indicating the patient's efforts to exercise. The amount of salt intake by the patient in a day measured may be data indicating the efforts to reduce salt.

[0035] The patient terminal 103 can accept user-related data input by the patient via the installed treatment app. The patient can input user-related data for a single day, or can input user-related data for multiple days daily or all at once. The patient terminal 103 transmits the user-related data input by the patient to the decision server 102. The user-related data can be transmitted from the patient terminal 103 to the decision server 102 at a predetermined timing. For example, the user-related data may be transmitted at a predetermined time every day, once a week, or at the timing when a process for determining the degree of possibility of secondary hypertension is executed.

[0036] With regard to user-related data, an example of user-related data in one embodiment of the present invention is shown in Fig. 7. In the example shown in Fig. 7, the systolic blood pressure value BP i , temperature T i , and behavior score BS i are shown, and these values ​​are based on user-related data input by the patient over a period of 30 days (i=1 to 30). For example, on the first day (i=1), the systolic blood pressure value is 146, the temperature is 13, and the behavior score is 2. Here, the behavior score is based on the behavior record data included in the user-related data. For example, the behavior score can be determined based on the scores of "efforts to reduce salt" and "efforts to exercise" that are subjectively scored by the patient on a screen 1200 in FIG. 12, which will be described later.

[0037] Returning to the description of the functional block diagram of the determination server 102 in Fig. 3, the determination unit 306 determines the degree of possibility of essential hypertension and secondary hypertension based on the user-related data acquired by the acquisition unit 304. As one embodiment, the degree of possibility of essential hypertension and secondary hypertension is determined based on the user-related data acquired by the acquisition unit 304, the essential hypertension prediction model, and the secondary hypertension prediction model. Determining the degree of possibility of essential hypertension and secondary hypertension may be determined based on the likelihood of assuming essential hypertension and the likelihood of assuming secondary hypertension, which are calculated based on the user-related data acquired by the acquisition unit 304, the essential hypertension prediction model, and the secondary hypertension prediction model.

[0038] The comparing unit 307 compares the degree of possibility of essential hypertension and secondary hypertension determined by the determining unit 306, and determines the degree of possibility of secondary hypertension compared with the possibility of essential hypertension. In this embodiment, the comparing unit 307 is included, but in one modified example, the comparing unit 307 may not be included.

[0039] Information indicating the degree of possibility of secondary hypertension determined by comparison with the possibility of essential hypertension is displayed on the display unit 402 of the patient terminal 103 or the display unit 502 of the doctor terminal 104. When the comparison unit 307 is not included, information indicating the degree of possibility of essential hypertension and secondary hypertension determined based on the likelihood when it is assumed that there is essential hypertension and the likelihood when it is assumed that there is secondary hypertension may be displayed.

[0040] 6 shows a flow chart executed by the system 100 for determining the degree of possibility of essential hypertension and secondary hypertension in one embodiment. A hypertensive patient starts treatment using a treatment app, measures his / her blood pressure, and inputs user-related data into a patient terminal via the treatment app. In step S602, the user-related data thus input by the patient is acquired.

[0041] In step S604, the error of the essential hypertension prediction model and the secondary hypertension prediction model with respect to the change in the acquired blood pressure value is calculated based on the user-related data acquired in step S602. Here, the change in the blood pressure value may be the change from the blood pressure value on one day to the blood pressure value on another day, for example, the change from the systolic blood pressure value on the 1st day to the systolic blood pressure value on the 30th day, or the change from the average systolic blood pressure value on multiple days to the average systolic blood pressure value on multiple other days, for example, the change from the average systolic blood pressure value on the 1st to 4th days to the average systolic blood pressure value on the 27th to 30th days. The blood pressure value here is not limited to the average blood pressure value, and may be the median, or a value calculated by other statistical methods from blood pressure values ​​for a predetermined period may be used.

[0042] The essential hypertension prediction model and the secondary hypertension prediction model will be explained. If a patient has essential hypertension, the error of the essential hypertension prediction model for the amount of change in the acquired blood pressure value will be small, and the error of the secondary hypertension prediction model for the amount of change in the acquired blood pressure value will be large. On the other hand, if a patient has secondary hypertension, the error of the essential hypertension prediction model for the amount of change in the acquired blood pressure value will be large, and the error of the secondary hypertension prediction model for the amount of change in the acquired blood pressure value will be small.

[0043] Since essential hypertension is considered to be influenced by the patient's behavior such as diet and lifestyle, in this embodiment, the essential hypertension prediction model is a model based on the behavior score, more specifically, a model including a term related to the behavior score. Since secondary hypertension is considered to be based on a disease and is less influenced by the patient's behavior, in this embodiment, the secondary hypertension prediction model is a model not based on the behavior score, more specifically, a model not including a term related to the behavior score. If it is considered that even secondary hypertension is influenced by certain patient behavior, it may be based on the behavior score. In that case, the coefficient given to the behavior score can be smaller than that of the essential hypertension prediction model. It may also be limited to the behavior score related to the behavior that is considered to have an effect on secondary hypertension.

[0044] The essential hypertension prediction model in one embodiment is shown in the following formula 1. Each term in formula 1 will be explained below. ΔBP e indicates the amount of change in the acquired blood pressure value, and in one embodiment, it is the amount of change in the mean systolic blood pressure value from the mean systolic blood pressure value for four days from days 1 to 4 shown in FIG. 7 to the mean systolic blood pressure value for four days from days 27 to 30, which is 2.0 as shown in FIG. 8B. e represents the regression coefficient of the mean behavior score in the essential hypertension prediction model, which is −1.8 in one embodiment as shown in FIG. 8B. ave indicates the average behavioral score from the 1st day to the nth day, and in one embodiment, is calculated by the following formula 2, and is the average behavioral score from the 1st day to the 30th day in FIG. 7, which is 3.9 as shown in FIG. 8B. e denotes the regression coefficient of the temperature difference in the essential hypertension prediction model, which is 0.5 in one embodiment as shown in FIG. 8B.

[0045] ΔT indicates the amount of change in the acquired temperature. Here, the amount of change in temperature may be the amount of change from the temperature on one day to the temperature on another day, for example, when the temperature is acquired for 30 days, or it may be the amount of change from the temperature on the 1st day to the temperature on the 30th day. Also, the amount of change in temperature may be the amount of change from the xth day to (x+n 0 -1) Day n 0 Starting from the date of (yn 1 +1)th to yth day 1 When the end period is defined as 1 day, the beginning period is n days. 0 End of period n from daily average temperature 1 Alternatively, ΔT may be calculated as the amount of change in the average temperature from the average temperature over the four days from the 1st to the 4th day in the beginning period to the average temperature over the four days from the 27th to the 30th day in the end period. In this case, ΔT can be calculated using Equation 3. For example, when the four days from the 1st to the 4th day are defined as the beginning period and the four days from the 27th to the 30th day are defined as the end period, ΔT may be calculated as the amount of change in the average temperature from the average temperature over the four days from the 1st to the 4th day in the beginning period to the average temperature over the four days from the 27th to the 30th day in the end period. In the example of FIG. 7, ΔT is -0.8 as shown in FIG. 8B. ε e is the change in the acquired blood pressure value ΔBP e α for e B.S. ave and β e The error in the sum of ΔT is shown, which in one embodiment is 13.02.

number

[0046] Here, we will explain Equation 2 and Equation 3. The average behavior score from the first day to the nth day can be calculated by Equation 2 below. BS k indicates the behavioral score on day k. In one embodiment, n is 30.

number

[0047] The change in temperature from the first day to the nth day can be calculated using the following formula: 0 is the period from the beginning of the xth day to (x+n 0 -1) Day n0 The number of samples used to calculate the daily average temperature is shown in FIG. 8A. In one embodiment, the average temperature at the beginning of the day is calculated from the temperatures on the first to fourth days in FIG. 7. 0 is 4. n 1 is the telophase (yn 1 +1)th to yth day 1 The number of samples used to calculate the daily average temperature is shown in FIG. 8A. In one embodiment, the average temperature at the end of the period is calculated from the temperatures on the 27th to 30th days in FIG. 7. 1 is 4. Therefore, in one embodiment, ΔT is calculated by the following equation 4, and is −8.0 as shown in FIG. 8B.

number

[0048] Next, the secondary hypertension prediction model in one embodiment is shown in the following formula 4. Each term in formula 4 will be explained below. ΔBP s indicates the amount of change in the acquired blood pressure value, and in one embodiment, it is the amount of change in the mean systolic blood pressure value from the mean systolic blood pressure value for four days from days 1 to 4 shown in FIG. 7 to the mean systolic blood pressure value for four days from days 27 to 30, which is 2.0 as shown in FIG. 8B. s indicates the regression coefficient of the temperature difference in the secondary hypertension prediction model, which is 0.2 in one embodiment as shown in FIG. 8B. ΔT indicates the amount of change in average temperature from the average temperature at the beginning to the average temperature at the end, which is calculated by the above formula 3 in one embodiment and is −0.8 as shown in FIG. 8B. ε s is the change in the acquired blood pressure value ΔBP s β for s The error in ΔT is shown and is 3.6 in one embodiment.

number

[0049] In step S606, the likelihood of assuming essential hypertension is calculated based on the error in the essential hypertension prediction model for the amount of change in the acquired blood pressure value calculated in step S604. In one embodiment, the likelihood of essential hypertension is calculated based on the error ε e In one embodiment, the error ε e is the normal distribution N(0, σ e 2 ) with a standard deviation of σ e = 6, the likelihood of assuming essential hypertension is calculated using the following equation 5, which is 0.0178.

number

[0050] In step S608, the likelihood of assuming secondary hypertension is calculated based on the error in the secondary hypertension prediction model for the amount of change in the acquired blood pressure value calculated in step S604. In one embodiment, the degree of possibility of secondary hypertension is, for example, calculated error ε s In one embodiment, the error ε s is the normal distribution N(0, σ s 2 ) with a standard deviation of σ s = 6, the likelihood of assuming secondary hypertension is calculated using the following equation 6, which is 0.2742.

number

[0051] In step S610, the degree of possibility of essential hypertension and secondary hypertension is determined based on the likelihood of assuming essential hypertension and the likelihood of assuming secondary hypertension calculated in steps S606 and S608. Here, the degree of possibility of essential hypertension and secondary hypertension is the calculated likelihood of assuming essential hypertension and the likelihood of assuming secondary hypertension themselves.

[0052] In step S612, the degree of possibility of secondary hypertension compared to the possibility of essential hypertension determined in step S610 is determined. Here, the degree of possibility of secondary hypertension compared to the possibility of essential hypertension is determined by calculating the likelihood ratio of the likelihood of assuming secondary hypertension to the likelihood of assuming essential hypertension determined in step S610 (hereinafter, the likelihood ratio of secondary hypertension).

[0053] The likelihood ratio of secondary hypertension is calculated by dividing the likelihood of assuming secondary hypertension calculated in step S608 by the likelihood of assuming essential hypertension calculated in step S606. For example, in one embodiment, the likelihood ratio of secondary hypertension is about 15.4, which is calculated by dividing the likelihood of assuming secondary hypertension (0.2742) by the likelihood of assuming essential hypertension (0.0178). From this likelihood ratio, the degree of possibility of secondary hypertension can be determined to be 15.4 times the possibility of essential hypertension. The degree of possibility of essential hypertension compared to the possibility of secondary hypertension may be represented by the likelihood ratio of the likelihood of assuming essential hypertension to the likelihood of assuming secondary hypertension (hereinafter, the likelihood ratio of essential hypertension). The likelihood ratio of essential hypertension may be calculated by dividing the likelihood of assuming essential hypertension by the likelihood of assuming secondary hypertension.

[0054] The above-described determination of the degree of likelihood of essential hypertension and secondary hypertension may be determined using an essential hypertension and secondary hypertension prediction model based on a machine learning model.

[0055] The decision server 102 may present information indicating the degree of possibility of secondary hypertension determined as described above to the doctor via the doctor terminal 104 or to the patient via the patient terminal 103.

[0056] In this embodiment, the comparison process of step S612 is included, but in other embodiments, the comparison process may not be included. In this case, the patient terminal 103 and the doctor terminal 104 can present the determined degree of possibility of essential hypertension and secondary hypertension. The degree of possibility of essential hypertension and secondary hypertension may be the likelihood when assuming essential hypertension and the likelihood when assuming secondary hypertension calculated by Equation 5 and Equation 6, or may be a numerical value obtained by performing a predetermined calculation process on the likelihood when assuming essential hypertension and the likelihood when assuming secondary hypertension. The likelihood and the obtained numerical value may be a value without a range, or may be a value with a range. In addition, it may be an index determined based on the likelihood when assuming essential hypertension and the likelihood when assuming secondary hypertension. For example, when the calculated likelihood when assuming essential hypertension and the likelihood when assuming secondary hypertension are equal to or greater than a preset threshold, an index indicating a high degree of possibility can be presented, and when the calculated likelihood when assuming essential hypertension and the likelihood when assuming secondary hypertension are less than the threshold, an index indicating a low degree of possibility can be presented.

[0057] FIG. 9 shows a sequence chart for presenting information indicating the degree of possibility of secondary hypertension determined as described above on the doctor terminal 104 in response to a request operation input to the doctor terminal. A patient who has been prescribed a treatment app installs the treatment app on the patient terminal 103 and inputs user information such as the patient's profile to the patient terminal 103 via the treatment app. The decision server 102 acquires the user identification and stores it in association with the user identification information. The user identification information may be the patient's user name or may be the application identification information of the treatment app used by the patient. Next, the patient inputs user-related data indicating blood pressure value data and behavior record data to the patient terminal 103 via the treatment app. The patient may input the user-related data every day, or may input user-related data for multiple days all at once.

[0058] The patient terminal 103 transmits the user-related data input by the patient to the decision server 102, and the decision server 102 receives and acquires the user-related data of the patient who is treated using the treatment app from the patient terminal 103. The decision server 102 stores the acquired user-related data in association with the user identification information of the patient who acquired the data. The acquired user-related data may be stored in association with the profile information of the acquired patient, or in association with the application identification information of the treatment app used by the acquired patient.

[0059] Next, the patient visits a medical institution, for example, 30 days after being prescribed the treatment app. In response to the doctor's input, the doctor terminal 104 transmits a request to present patient information including the user-related data stored in the decision server 102. The decision server 102 determines the degree of possibility of secondary hypertension based on the user-related data associated with the stored user identification information for the visited patient. The decision server 102 transmits the patient information including information indicating the determined degree of possibility of secondary hypertension to the doctor terminal 104. The doctor terminal 104 presents the patient information including information indicating the determined degree of possibility of secondary hypertension acquired from the decision server 102 to the doctor.

[0060] FIG. 10 shows a sequence chart for presenting information indicating the degree of possibility of secondary hypertension determined as described above on the patient terminal 103 in response to a request operation input to the patient terminal. A patient who has been prescribed a treatment app inputs user information such as a patient profile to the patient terminal 103 via the treatment app installed in the patient terminal 103. The decision server 102 acquires the user information and stores it in association with user identification information. The user identification information may be application identification information of the treatment app used by the patient. Next, the patient inputs user-related data indicating blood pressure value data and behavior record data to the patient terminal 103 via the treatment app. The patient may input user-related data every day, or may input user-related data for multiple days all at once. The decision server 102 acquires user-related data of a patient who is treated using the treatment app from the patient terminal 103. The decision server 102 stores the acquired user-related data in association with user identification information. The acquired user-related data may be stored in association with the acquired patient profile, etc., or in association with application identification information of the treatment app used by the patient.

[0061] Next, when the patient is worried about not being able to lower his / her blood pressure, for example, the patient executes a consultation function of the treatment app on the patient terminal 103. The patient terminal 103 transmits information indicating the execution of the consultation function to the server, and the decision server 102 presents available consultation options for the patient to the patient via the patient terminal 103. The patient selects "blood pressure does not decrease" from the available consultation options presented. The decision server 102 determines the degree of possibility of secondary hypertension based on user-related data associated with the stored user identification information for the patient. The patient terminal 103 presents the data indicating the determined degree of possibility of secondary hypertension, acquired from the decision server 102, and information indicating a suggestion to consult a doctor to the patient.

[0062] 11 to 14 each show an example of a screen displayed on a patient terminal on which a therapeutic application according to one embodiment of the present invention is installed.

[0063] FIG. 11 is an example of a screen displayed when a patient uses a consultation function in a treatment app. Screen 1100 displays the question, "What would you like to consult about?" Screen 1100 also displays items that the patient may want to consult about, for example, "I'm trying my best but my blood pressure won't go down." There may be one or more items that the patient may want to consult about.

[0064] FIG. 12 is an example of a screen displayed when a patient records user-related data in a treatment application. The screen 1200 displays the items of "Date" and the performed actions, "Salt Reduction Effort", "Exercise Effort", and "Blood Pressure". In the "Salt Reduction Effort" item of the screen 1200, the patient can subjectively score his / her salt reduction efforts, for example, the patient selects from the options of "0 points, nothing to be careful about", "1 point, a little bit of care", and "2 points, a lot of care". In the "Exercise Effort" item of the screen 1200, the patient can subjectively score his / her exercise efforts, for example, the patient selects from the options of "0 points, nothing to be careful about", "1 point, a little bit of care", and "2 points, a lot of care". In the "Blood Pressure" item of the screen 1200, the systolic blood pressure value and the diastolic blood pressure value are input. The systolic blood pressure value and the diastolic blood pressure value may be input by the patient using blood pressure values ​​measured by a blood pressure measuring device, or may be input by a patient terminal connected to the blood pressure measuring device.

[0065] FIG. 13 is an example of a screen that is displayed when a patient selects the item "My blood pressure won't go down despite my efforts" on the screen 1100 of FIG. 11. The screen 1300 displays "I have looked at the records of Mr. / Ms. XX (e.g., the patient's name), and the following is thought to be the cause." The screen 1300 also displays possible consultation options based on the user-related data entered on the screen 1100 of FIG. 11, for example, displaying items such as "1. Possibility of not appropriately reducing salt intake and exercising" and "2. Possibility of high blood pressure being caused by another illness." Also, an option for checking each item in detail is displayed, for example, displaying "Check this in detail."

[0066] FIG. 14 is an example of a screen displayed when the patient selects to check in detail in the item "2. Possibility of hypertension caused by another disease" in FIG. 13. The screen 1400 displays information indicating the degree of possibility of secondary hypertension determined as described above. In one embodiment, the screen 1400 displays information indicating the degree of possibility of secondary hypertension determined in comparison with the possibility of essential hypertension. The degree of possibility of secondary hypertension determined in comparison with the possibility of essential hypertension is displayed, for example, as "The possibility that Mr. XX (for example, the patient's name) has high blood pressure due to another disease, taking into account his / her actions in the past month, the season, his / her age, and his / her gender, is 15.4 times higher than the possibility that he / she does not have high blood pressure." Also, a message urging the patient to see a doctor is displayed. For example, the message displayed is "Please consult a doctor at least once."

[0067] FIG. 15 shows an example of a screen displayed on the doctor terminal 104 in which the doctor terminal program according to an embodiment of the present invention is installed. The screen 1500 displays information about the patient. For example, the information about the patient may include identification information assigned to the patient, and a line graph in which the horizontal axis indicates the date and the vertical axis indicates the blood pressure value, and the systolic and diastolic blood pressure values ​​are displayed. The screen 1500 also displays a message that prompts the doctor to be careful, for example, the message may be "The blood pressure drop is small compared to the action". The message may be displayed, for example, when the degree of possibility of secondary hypertension determined as described above is greater than a preset threshold. When the message is selected, the screen 1501 is displayed. The degree of possibility of secondary hypertension determined as described above is displayed on the screen 1501. In one embodiment, the degree of possibility of secondary hypertension determined in comparison with the possibility of essential hypertension is displayed on the screen 1501. The degree of possibility of secondary hypertension determined in comparison with the possibility of essential hypertension is displayed, for example, as follows: "...2. Secondary hypertension. The probability of secondary hypertension estimated from daily records is calculated to be 15.4 times (95% CI (CI: confidence interval)) the estimated probability of essential hypertension. Further examination is recommended."

[0068] A message urging the patient to see a doctor, as in Figure 14, or a message urging the doctor to be warned, as in Figure 15, can encourage the patient to undergo a detailed examination. If the patient is indeed diagnosed with secondary hypertension, this can lead to the identification of the disease causing the secondary hypertension.

[0069] In the above-described processes or operations, the processes or operations can be freely changed as long as no inconsistencies in the processes or operations occur, such as the use of data that should not yet be available in a certain step. Furthermore, the above-described embodiments are merely examples for explaining the present invention, and the present invention is not limited to these embodiments. The present invention can be embodied in various forms without departing from the gist of the present invention. [Explanation of symbols]

[0070] 100: system, 101: network, 102: decision server, 103: patient terminal, 104: doctor terminal, 105: blood pressure measuring device, 200: client device, 201: processing device, 202: display device, 203: input device, 204: storage device, 205: communication device, 210: bus, 250: server, 251: processing device, 252: display device, 253: input device, 254: storage device, 255: communication device, 260: bus, 301: control unit, 302: display unit, 303: input unit, 304: acquisition unit, 305: storage unit, 306: determination unit, 307: comparison unit, 308: communication unit, 401: control unit, 402: display unit, 403: input unit, 404: storage unit, 405: communication unit 501: control unit, 502: display unit, 503: input unit, 504: storage unit, 505: communication unit 1100: Screen, 1200: Screen, 1300: Screen, 1400: Screen, 1500: Screen, 1501: Screen

Claims

1. 1. A method for determining a likelihood of secondary hypertension, comprising: acquiring user-related data including blood pressure value data indicative of measured blood pressure values ​​and action record data indicative of actions taken by the user; determining a degree of likelihood of essential hypertension and secondary hypertension based on the acquired user-related data, an essential hypertension prediction model based on a behavior score indicating the user's behavior, and a secondary hypertension prediction model; A method comprising:

2. The step of determining the degree of likelihood of essential hypertension and secondary hypertension comprises: Calculating an error term in an essential hypertension prediction model and an error term in a secondary hypertension prediction model based on the acquired user-related data; Calculating the likelihood of assuming essential hypertension based on the error term in the calculated essential hypertension prediction model; Calculating the likelihood of assuming secondary hypertension based on the error term in the calculated secondary hypertension prediction model; determining the degree of possibility of essential hypertension and secondary hypertension based on the calculated likelihood of essential hypertension and the calculated likelihood of secondary hypertension; The method of claim 1 , comprising:

3. 2. The method of claim 1, further comprising the step of: determining a degree of likelihood of secondary hypertension compared to the determined likelihood of essential hypertension.

4. The method according to claim 3, further comprising a step of presenting information on the terminal indicating the degree of possibility of secondary hypertension compared with the determined possibility of essential hypertension in response to a request operation input to the terminal by the user or medical professional.

5. The method of claim 1 , wherein the essential hypertension prediction model includes a term related to a behavioral score, and the secondary hypertension prediction model does not include a term related to a behavioral score.

6. The method according to claim 1 , wherein the behavior record data includes at least one of the user's amount of exercise, weight, and salt intake.

7. The method of claim 1 , wherein the user-related data is acquired over a predetermined period of time and includes at least two blood pressure value data and at least two behavior record data.

8. The method of claim 1 , wherein the user-related data includes blood pressure values ​​entered by the user into the user's terminal and information indicative of actions taken by the user, and is transmitted by the user's terminal.

9. A computer program for causing one or more computers to carry out the method according to any one of claims 1 to 8.

10. 1. A system for determining a degree of likelihood of secondary hypertension, comprising: Acquire user-related data including blood pressure value data indicating a measured blood pressure value and action record data indicating an action performed by the user; determining a degree of likelihood of essential hypertension and secondary hypertension based on the acquired user-related data, an essential hypertension prediction model based on a behavior score indicating the user's behavior, and a secondary hypertension prediction model; A system characterized by executing the following.

11. 1. An apparatus for determining a degree of likelihood of secondary hypertension, comprising: Acquire user-related data including blood pressure value data indicating a measured blood pressure value and action record data indicating an action performed by the user; determining a degree of likelihood of essential hypertension and secondary hypertension based on the acquired user-related data, an essential hypertension prediction model based on a behavior score indicating the user's behavior, and a secondary hypertension prediction model; An apparatus for performing the above-mentioned steps.

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