Method and device for presenting treatment plan for nocturia diseases in non-face-to-face manner

Abstract

The present disclosure relates to a method and device for presenting a treatment plan for nocturia disease in a non-face-to-face manner. Provided according to one embodiment of the present disclosure is a method for presenting a treatment plan for a nocturia disease in a non-face-to-face manner, the method comprising the steps of: obtaining diagnostic results for at least one condition among lower urinary tract symptoms (LUTS), overactive bladder (OAB), nocturnal polyuria (NP), obstructive sleep apnea (OSA), and sleep disorders for a user; determining a treatment group for the user's nocturia disease on the basis of the diagnostic results; generating a treatment plan for the determined treatment group; and presenting the treatment plan to the user through a user terminal to address the user's nocturia disease, wherein the treatment plan may include at least one of a first treatment plan for exercise, a second treatment plan for dietary control, a third treatment plan for sleep, and a fourth treatment plan for medication.

Description

Method and device for suggesting a treatment plan for nocturia without face-to-face contact

[0001] The present disclosure relates to a method and device for providing a treatment plan for nocturia in a non-face-to-face manner.

[0002] Nocturia refers to urination during sleep, usually after waking up. Nocturia must be accompanied by sleep before or after urination, and is distinct from enuresis, which involves involuntary urination during sleep.

[0003] Nocturia is a common urinary disorder, affecting approximately 70% of people over 60 years of age. The number of patients with nocturia is increasing as the population ages.

[0004] According to the research results, nocturia reduces work productivity by 24% and leisure time activity by 34% compared to other chronic diseases, which shows that nocturia causes inconvenience in daily life and has a high incidence rate.

[0005] The main causes of nocturnal enuresis include nocturnal polyuria (NP), excessive urine output at night; sleep disorders that prevent deep sleep; and bladder dysfunction due to overactive bladder (OAB). Of these, nocturnal polyuria is the most common, affecting approximately 83% of nocturnal enuresis patients.

[0006] Treatment options for nocturia are broadly divided into behavioral therapy and pharmacological therapy. Behavioral therapy includes encouraging urination before bed and refraining from drinking water after dinner. Pharmacological therapy includes desmopressin, a synthetic analog of vasopressin.

[0007] Referring to the above, nocturia is a chronic disease that causes great difficulties in daily life, and the number of people with nocturia is also increasing.

[0008] Recently, research on digital therapeutics (DTx) has been actively conducted, and there is a growing need to develop a method to provide non-face-to-face treatment for nocturia.

[0009] The present disclosure provides a method and device for providing a non-face-to-face treatment for nocturia. The problems addressed by the present invention are not limited to those mentioned above. Other problems and advantages of the present invention, not mentioned above, can be understood through the following description and will be more clearly understood through the embodiments of the present invention. Furthermore, it will be appreciated that the problems and advantages of the present invention can be realized by the means and combinations thereof set forth in the claims.

[0010] As a technical means for achieving the above-described technical problem, a first aspect of the present disclosure is a method for suggesting a treatment plan for nocturia disorder in a non-face-to-face manner, comprising: obtaining a diagnosis result for at least one of lower urinary tract symptoms (LUTS), overactive bladder (OAB), nocturnal polyuria (NP), obstructive sleep apnea (OSA), and sleep disorder; determining a treatment group for treating nocturia disorder based on the diagnosis result; generating a treatment plan for the treatment group; and suggesting the treatment plan to the user belonging to the treatment group through a user terminal to treat the nocturia disorder of the user; wherein the treatment plan may include at least one of a first treatment plan for exercise performance, a second treatment plan for dietary control, a third treatment plan for sleep, and a fourth treatment plan for medication administration.

[0011] A second aspect of the present disclosure is a device for suggesting a treatment plan for nocturia in a non-face-to-face manner, comprising: at least one memory; and at least one processor; wherein the at least one processor obtains a diagnosis result for at least one of lower urinary tract symptoms (LUTS), overactive bladder (OAB), nocturnal polyuria (NP), obstructive sleep apnea (OSA), and sleep disorder for a user, determines a treatment group for treating nocturia to which the user belongs based on the diagnosis result, generates a treatment plan for the treatment group, and suggests the treatment plan to the user belonging to the treatment group through a user terminal, thereby treating the nocturia of the user, and the treatment plan may include at least one of a first treatment plan for exercise performance, a second treatment plan for dietary control, a third treatment plan for sleep, and a fourth treatment plan for medication administration.

[0012] A third aspect of the present disclosure can provide a computer-readable recording medium having recorded thereon a program for executing the method according to the first aspect on a computer.

[0013] In addition, other methods for implementing the present invention, other systems, and computer-readable recording media storing a computer program for executing the method may be further provided.

[0014] Other aspects, features and advantages other than those described above will become apparent from the following drawings, claims and detailed description of the invention.

[0015] According to the problem solving means of the present disclosure described above, the present disclosure provides a treatment method for nocturia in a non-face-to-face manner, thereby reducing the time required for a user to visit a hospital in person and receive treatment for nocturia.

[0016] In addition, the present disclosure provides classification results into various treatment groups as diagnostic results for nocturia disease, thereby providing accurate diagnostic results.

[0017] In addition, the present disclosure can efficiently provide various treatment methods, such as exercise therapy, drug therapy, and sleep therapy, to users diagnosed with nocturia in a non-face-to-face manner.

[0018] Additionally, the present disclosure can monitor the user's treatment plan implementation process to provide immediate feedback.

[0019] Figure 1 is a conceptual diagram illustrating a method for suggesting a treatment plan for nocturia according to one embodiment.

[0020] Figure 2 is a block diagram of a user terminal according to one embodiment.

[0021] FIG. 3 is an exemplary configuration diagram of a system including a user terminal and an external device according to one embodiment.

[0022] FIG. 4a is a flowchart illustrating a method for providing a treatment plan for nocturia in a non-face-to-face manner according to one embodiment.

[0023] FIG. 4b is a flowchart illustrating a method for monitoring the performance of a treatment plan for nocturia in a non-face-to-face manner according to one embodiment.

[0024] FIG. 5A and FIG. 5B are exemplary drawings illustrating a method for determining a treatment group according to one embodiment.

[0025] FIGS. 6A and 6B are exemplary drawings illustrating a method for generating a treatment plan for a treatment group according to one embodiment.

[0026] FIG. 7 is an exemplary drawing for explaining a method for monitoring the execution process of a first treatment plan according to one embodiment.

[0027] FIG. 8 is an exemplary drawing for explaining a method for monitoring the execution process of a second treatment plan according to one embodiment.

[0028] FIG. 9 is an exemplary drawing for explaining a method for monitoring the execution process of a third treatment plan according to one embodiment.

[0029] One aspect of the present invention is a method for presenting a treatment plan for nocturia through a user terminal in a non-face-to-face manner, the method comprising: obtaining a diagnosis result for at least one of lower urinary tract symptoms (LUTS), overactive bladder (OAB), nocturnal polyuria (NP), obstructive sleep apnea (OSA), and sleep disorder for the user; determining a treatment group for treating the nocturia to which the user belongs based on the diagnosis result; generating a treatment plan for the treatment group; and presenting the treatment plan to the user belonging to the treatment group through the user terminal to treat the nocturia to which the user belongs.

[0030] Additionally, the treatment plan may include at least one of a first treatment plan for exercise performance, a second treatment plan for dietary control, a third treatment plan for sleep, and a fourth treatment plan for medication.

[0031] In addition, the step of acquiring may additionally acquire information about the drug being taken, and the step of determining the treatment group may determine the treatment group based on at least one of the diagnosis result and the information about the drug being taken.

[0032] In addition, the method further includes a step of obtaining at least one of a first indicator of renal function and a second indicator of body electrolytes at predetermined time intervals; and the step of generating the treatment plan may generate the fourth treatment plan based on a change in at least one of the first indicator and the second indicator according to taking the drug.

[0033] In addition, the step of acquiring additionally acquires basic health information, and the step of generating the treatment plan generates the treatment plan based on at least one of the diagnosis result and the basic health information, and the basic health information may include at least one of information regarding gender, weight, whether diabetes exists, whether heart disease exists, and whether obesity exists.

[0034] In addition, the step of generating the treatment plan may include a step of generating a schedule for performing the exercise based on the severity of the nocturia disorder; and a step of generating a first treatment plan for performing the exercise according to the schedule.

[0035] Additionally, the step of generating the treatment plan may include a step of calculating an appropriate amount of water intake using basic health information; and a step of generating a second treatment plan that causes the patient to consume water greater than the appropriate amount of water intake.

[0036] Additionally, the step of generating the above treatment plan may generate a third treatment plan that allows sleep by maintaining an appropriate sleeping posture.

[0037] In addition, the method further includes a step of generating an interface that displays information for performing the treatment plan; and the information for performing the treatment plan may include at least one of information on a schedule for performing the treatment plan, image information including an appropriate exercise posture, image information including an appropriate sleep posture, and appropriate dietary information.

[0038] In addition, the step of generating the treatment plan for the treatment group may generate the treatment plan by inputting information about the treatment group into a neural network model. In addition, the method further includes a step of training the neural network model using learning data; and the learning data may include information about each treatment group, information about each treatment plan performed for each treatment group, and information about the degree of improvement in nocturia according to the performance of the treatment plan for each treatment group.

[0039] Another aspect of the present invention is a method for monitoring the progress of a treatment plan for nocturia through a user terminal, the method comprising: generating a treatment plan for nocturia based on a diagnosis result for at least one of lower urinary tract symptoms, overactive bladder, nocturia, sleep apnea, and sleep disorder for the user; presenting the treatment plan to the user through the user terminal to treat the nocturia; and monitoring the progress of the user performing the presented treatment plan at predetermined intervals.

[0040] Additionally, the monitoring step may monitor at least one of a first treatment plan for exercise performance, a second treatment plan for dietary control, a third treatment plan for sleep, and a fourth treatment plan for medication.

[0041] In addition, the monitoring step may include a step of calculating information on exercise posture based on an exercise performance video; and a step of monitoring the performance process of the first treatment plan based on the information on the exercise posture.

[0042] In addition, the monitoring step may include a step of obtaining the results of a questionnaire on eating habits; and a step of monitoring the progress of the second treatment plan using the results of the questionnaire.

[0043] In addition, the monitoring step may include a step of calculating information on a sleeping posture based on a sleep image; and a step of monitoring the performance of the third treatment plan based on the information on the sleeping posture.

[0044] In addition, the monitoring step may include a step of generating information about sleep apnea based on sleep sounds; and a step of monitoring the performance of the third treatment plan based on the information about sleep apnea.

[0045] In addition, the monitoring step monitors the progress of the fourth treatment method by using changes in physical indicators according to taking a drug for treating nocturia, and the physical indicators may include at least one of a first indicator regarding renal function and a second indicator regarding body electrolytes.

[0046] In addition, the method may further include a step of calculating the degree of improvement of nocturia based on the monitoring results; a step of generating a management schedule for the nocturia using the degree of improvement; and a step of presenting the generated management schedule to the user through the user terminal.

[0047] In addition, the method may further include a step of updating the treatment plan based on the monitoring results; and a step of presenting the updated treatment plan to the user through a user terminal to treat nocturia.

[0048] Another aspect of the present invention is a device for suggesting a treatment plan for nocturia through a user terminal in a non-face-to-face manner, wherein the device obtains a diagnosis result for at least one of lower urinary tract symptoms, overactive bladder, nocturia, sleep apnea, and sleep disorder for the user, determines a treatment group for treating the nocturia to which the user belongs based on the diagnosis result, generates a treatment plan for the treatment group, and suggests the treatment plan to the user belonging to the treatment group through the user terminal, thereby treating the nocturia to which the user belongs.

[0049] Another aspect of the present invention is a device for monitoring the progress of a treatment plan for nocturia through a user terminal, wherein the device generates a treatment plan for nocturia based on a diagnosis result for at least one of lower urinary tract symptoms, overactive bladder, nocturia, sleep apnea, and sleep disorder for the user, presents the treatment plan to the user through the user terminal to treat the nocturia, and monitors the progress of the user performing the presented treatment plan at predetermined intervals.

[0050] The advantages and features of the present invention, and the methods for achieving them, will become clearer with reference to the embodiments described in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments presented below, but may be implemented in various different forms, and it should be understood that it includes all transformations, equivalents, and substitutes included in the spirit and technical scope of the present invention. The embodiments presented below are provided to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the scope of the invention. In describing the present invention, if a detailed description of a related known technology is judged to obscure the gist of the present invention, the detailed description thereof will be omitted.

[0051] The terminology used in this application is only used to describe specific embodiments and is not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, it should be understood that the terms "comprise" or "have" indicate the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, but do not preclude the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0052] Some embodiments of the present disclosure may be represented by functional block configurations and various processing steps. Some or all of these functional blocks may be implemented by various hardware and / or software configurations that perform specific functions. For example, the functional blocks of the present disclosure may be implemented by one or more microprocessors or by circuit configurations for a given function. Furthermore, for example, the functional blocks of the present disclosure may be implemented in various programming or scripting languages. The functional blocks may be implemented by algorithms that execute on one or more processors. Furthermore, the present disclosure may employ conventional techniques for electronic configuration, signal processing, and / or data processing. Terms such as "mechanism," "element," "means," and "configuration" may be used broadly and are not limited to mechanical and physical configurations.

[0053] Additionally, the connecting lines or connecting members between components depicted in the drawings are merely exemplary representations of functional connections and / or physical or circuit connections. In an actual device, connections between components may be represented by various functional connections, physical connections, or circuit connections that may be replaced or added.

[0054] The present disclosure will be described in detail with reference to the attached drawings below.

[0055] Figure 1 is a conceptual diagram illustrating a method for suggesting a treatment plan for nocturia according to one embodiment.

[0056] Referring to FIG. 1, the user terminal (20) can obtain a diagnosis result (30) of nocturia for the user (10).

[0057] For example, the user terminal (20) can obtain a diagnosis result (30) for at least one of nocturnal polyuria (NP), lower urinary tract symptoms (LUTS), overactive bladder (OAB), obstructive sleep apnea (OSA), and sleep disorder for the user (10).

[0058] In one embodiment, the user terminal (20) can obtain a diagnosis result (30) for the user (10) from a server or database in which the diagnosis result (30) for the user (10) is stored. In addition, the user terminal (20) can obtain a diagnosis result (30) for nocturia for the user (10) from a medical staff terminal that provides the diagnosis result (30) for nocturia.

[0059] In another embodiment, the user terminal (20) may obtain a diagnosis result (30) for at least one of nocturnal polyuria, lower urinary tract symptoms, overactive bladder, sleep apnea, and sleep disorder by using at least one of the user's (10) basic health information and the results of a questionnaire for diagnosing nocturia.

[0060] Here, the basic health information may include at least one of the following information: the user's (10) age, gender, height, weight, whether he or she has high blood pressure, whether he or she has diabetes, whether he or she has hyperlipidemia, whether he or she has unstable angina, whether he or she has myocardial infarction, whether he or she has heart failure, whether he or she smokes, whether he or she drinks alcohol, whether he or she is taking internal medicine medication, whether he or she is taking urology medication, and whether he or she is depressed, but is not limited thereto.

[0061] Additionally, the questionnaire test for diagnosing nocturia disorder may include, but is not limited to, a first test to determine the risk of developing nocturia-related disorder and a second test to diagnose nocturia disorder.

[0062] In addition, the user terminal (20) may suggest to a user (10) who is determined to have a high risk of developing a nocturia-related disease to receive in-patient treatment using the results of a questionnaire test for diagnosing nocturia-related disease. Here, the nocturia-related disease refers to a disease that is likely to accompany nocturia-related disease, considering the characteristics of nocturia-related disease having a high incidence rate in the elderly, and may include, but is not limited to, diabetes, obesity, heart disease, and mental illness.

[0063] In addition, the user terminal (20) can classify a treatment group according to the degree of symptoms of nocturia based on the diagnosis result (30) for at least one of nocturnal polyuria, lower urinary tract symptoms, overactive bladder, sleep apnea, and sleep disorder.

[0064] Nocturnal polyuria, lower urinary tract symptoms, overactive bladder, sleep apnea, and sleep disorders are the main causes of nocturia. The user terminal (20) provides a final diagnosis result (30) for nocturia based on the diagnosis result (30) for the main cause of nocturia, and can determine a treatment group for nocturia based on at least one of the diagnosis result (30) for the main cause or the final diagnosis result (30) for nocturia.

[0065] For example, the user terminal (20) can determine the final treatment group for nocturia based on the first classification result classified based on the basic health information of the user (10) and the second classification result classified based on the diagnosis result (30) for nocturia, lower urinary tract symptoms, overactive bladder, sleep apnea, and sleep disorder of the user (10).

[0066] Additionally, the user terminal (20) can generate a treatment plan (40) for the user (10) based on the treatment group results determined for the user (10).

[0067] For example, the treatment plan (40) that the user terminal (20) suggests to the user (10) may include at least one treatment plan (40) among a first treatment plan for exercise performance, a second treatment plan for dietary control, a third treatment plan for sleep, and a fourth treatment plan for drug administration.

[0068] Additionally, the user terminal (20) can monitor (50) the execution process of the treatment plan (40) of the user (10) and provide feedback to the user (10).

[0069] Figure 2 is a block diagram of a user terminal according to one embodiment.

[0070] Referring to FIG. 2, the user terminal (100) includes a processor (110), a memory (120), and a communication module (140). For convenience of explanation, only components related to the present invention are illustrated in FIG. 2. Therefore, in addition to the components illustrated in FIG. 2, other general-purpose components may be further included in the user terminal (100). Furthermore, it will be apparent to those skilled in the art that the processor (110), memory (120), input / output interface (130), and communication module (140) illustrated in FIG. 2 may be implemented as independent devices.

[0071] Here, the user terminal (100) may be the same device as the user terminal (20) of FIG. 1.

[0072] The processor (110) can process computer program commands by performing basic arithmetic, logic, and input / output operations. Here, the commands can be provided from memory (120) or an external device. In addition, the processor (110) can generally control the operations of other components included in the user terminal (100).

[0073] For example, the processor (110) can obtain a diagnostic result for at least one of lower urinary tract symptoms, overactive bladder, nocturia, sleep apnea, and sleep disorders. In addition, the processor (110) can obtain a final diagnostic result for nocturia using the diagnostic result for at least one of the above-described lower urinary tract symptoms, overactive bladder, nocturia, sleep apnea, and sleep disorders.

[0074] For example, the processor (110) may perform a first test to determine the risk of developing a nocturia-related disease based on the user's basic health information. If the result of the first test is abnormal, the processor (110) may suggest an in-patient treatment for the nocturia-related disease. Furthermore, if the result of the first test is normal, the processor (110) may perform a second test to diagnose the nocturia-related disease. For example, the second test may include at least one of a first questionnaire regarding past medical history, a second questionnaire regarding the number of times nocturia occurs, a third questionnaire regarding sleep quality, a fourth questionnaire regarding overactive bladder, and a fifth questionnaire regarding prostate symptoms. For example, the processor (110) may determine the questionnaire to be included in the second test based on the user's basic health information.

[0075] Additionally, the processor (110) may provide a diagnostic result for at least one of nocturnal polyuria, lower urinary tract symptoms, overactive bladder, sleep apnea, and sleep disorder based on the results of the second examination.

[0076] Additionally, the processor (110) can determine the user's treatment group based on the final diagnosis result for the nocturia disease described above.

[0077] For example, the processor (110) can classify a treatment group according to the degree of symptoms of nocturia based on at least one of the diagnosis results for at least one of nocturia, lower urinary tract symptoms, overactive bladder, sleep apnea, and sleep disorder and at least one of the user's basic health information.

[0078] In one embodiment, the processor (110) may first classify a user based on a diagnosis of at least one of nocturnal polyuria, lower urinary tract symptoms, overactive bladder, sleep apnea, and sleep disorders. Additionally, the processor (110) may secondarily classify the user based on the user's basic health information.

[0079] Additionally, the processor (110) can generate a treatment plan for the determined treatment group and provide it to the user.

[0080] Additionally, the processor (110) can monitor the process in which the user performs the treatment plan and generate feedback on the process in which the treatment plan is performed to provide the feedback to the user.

[0081] In one embodiment, the processor (110) may monitor the performance of the first treatment plan for exercise performance based on a video of the user's exercise performance. In another embodiment, the processor (110) may monitor the performance of the second treatment plan for dietary control based on the results of a questionnaire on eating habits. In yet another embodiment, the processor (110) may monitor the performance of the third treatment plan for sleep based on a sleep image or sleep sound of the user's sleep.

[0082] Through this, the processor (110) can effectively monitor the user's treatment plan execution process performed non-face-to-face and generate feedback to obtain high treatment performance results.

[0083] The processor (110) may be implemented as an array of a plurality of logic gates, or may be implemented as a combination of a general-purpose microprocessor and a memory storing a program that can be executed on the microprocessor. For example, the processor (110) may include a general-purpose processor, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a controller, a microcontroller, a state machine, etc. In some environments, the processor (110) may include an application-specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), etc. For example, the processor (110) may refer to a combination of processing devices, such as a combination of a digital signal processor (DSP) and a microprocessor, a combination of a plurality of microprocessors, a combination of one or more microprocessors coupled with a digital signal processor (DSP) core, or any other such combination of configurations.

[0084] The memory (120) may include any non-transitory computer-readable recording medium. As an example, the memory (120) may include a non-permanent mass storage device such as a random access memory (RAM), a read only memory (ROM), a disk drive, a solid state drive (SSD), a flash memory, etc. As another example, the non-permanent mass storage device such as a ROM, an SSD, a flash memory, a disk drive, etc. may be a separate permanent storage device distinct from the memory. In addition, the memory (210) may store an operating system (OS) and at least one program code (e.g., code for the processor (110) to perform an operation to be described later with reference to FIGS. 3 to 9).

[0085] These software components may be loaded from a computer-readable recording medium separate from the memory (120). This separate computer-readable recording medium may be a recording medium that can be directly connected to the user terminal (100), and may include, for example, an input / output computer-readable recording medium such as a floppy drive, a disk, a tape, a DVD / CD-ROM drive, a memory card, etc. Alternatively, the software components may be loaded into the memory (120) through a communication module (140) that is not a computer-readable recording medium. For example, at least one program may be loaded into the memory (120) based on a computer program (e.g., a computer program for the processor (110) to perform the operations described below with reference to FIGS. 3 to 9) that is installed by files provided by developers or a file distribution system that distributes installation files of applications through the communication module (140).

[0086] The input / output interface (130) may be a means for interfacing with a device (e.g., a keyboard, a mouse, etc.) for input and / or output that may be connected to or included in the user terminal (100). In FIG. 2, the input / output interface (130) is illustrated as an element configured separately from the processor (110), but is not limited thereto, and the input / output interface (130) may be configured to be included in the processor (110). For example, the processor (110) may provide the user with a diagnosis result for nocturia based on the user's basic health information input from the input / output interface (130).

[0087] The communication module (140) may provide a configuration or function for the user terminal (100) to communicate with an external device (not shown) via a network. In addition, the communication module (140) may provide a configuration or function for the user terminal (100) to communicate with another external device. For example, control signals, commands, data, etc. provided under the control of the processor (110) may be transmitted to an external device via the communication module (140) and the network.

[0088] FIG. 3 is an exemplary configuration diagram of a system including a user terminal and an external device according to one embodiment.

[0089] Referring to FIG. 3, the user terminal (310) may include any type of server that manages a web and / or app capable of providing artificial intelligence services. Furthermore, the user terminal (310) of FIG. 3 may be the same device as the user terminal (20) of FIG. 1 and / or the user terminal (100) of FIG. 2.

[0090] The external device (320) may include, but is not limited to, a server that manages a web service API capable of providing information. For example, the external device (320) may correspond to a medical staff terminal that transmits the user's visit treatment results to the user terminal (310).

[0091] For example, the user terminal (310) may determine whether to perform additional tests for diagnosing nocturia based on the user's in-patient treatment results received from the external device (320). Through this, the user terminal (310) may efficiently provide diagnostic results for nocturia by re-entering the user who has been suggested for in-patient treatment into the nocturia diagnosis process.

[0092] The user terminal (310) and the external device (320) can communicate with each other and / or with other devices through a network. The network is a comprehensive data communication network that allows different entities to communicate smoothly with each other, and may include wired Internet, wireless Internet, and mobile wireless communication networks. For example, the network may include a local area network (LAN), a wide area network (WAN), a value-added network (VAN), a mobile radio communication network, a satellite communication network, and combinations thereof. In addition, wireless communication may include, but is not limited to, wireless LAN (Wi-Fi), Bluetooth, Bluetooth Low Energy, ZigBee, Wi-Fi Direct (WFD), ultrawideband (UWB), infrared communication (IrDA; Infrared Data Association), NFC (Near Field Communication), etc.

[0093] The user terminal (310) can communicate with an external device (320) via a network. By communicating via the network, the user terminal (310) can receive the user's medical examination results from the external device (320) and determine whether to perform additional tests for diagnosing nocturia.

[0094] FIG. 4a is a flowchart illustrating a method for providing a treatment plan for nocturia in a non-face-to-face manner according to one embodiment.

[0095] Referring to FIG. 4a, the method for suggesting a treatment plan for nocturia in a non-face-to-face manner is comprised of steps that are processed in a time-series manner in the user terminal (100) and / or processor (110) illustrated in FIG. 2. Therefore, even if omitted below, the content described above regarding the user terminal (100) or processor (110) illustrated in FIG. 2 can also be applied to the method for suggesting a treatment plan for nocturia in a non-face-to-face manner in FIG. 4a.

[0096] In step 410, the user terminal can obtain a diagnosis result for at least one of lower urinary tract symptoms, overactive bladder, nocturia, sleep apnea, and sleep disorder.

[0097] In one embodiment, the user terminal may obtain a diagnosis result for at least one of the user's lower urinary tract symptoms, overactive bladder, nocturia, sleep apnea, and sleep disorder from an external device or an external server.

[0098] In another embodiment, the user terminal may obtain a diagnosis result for at least one of lower urinary tract symptoms, overactive bladder, nocturia, sleep apnea, and sleep disorder by using at least one of the user's basic health information and the results of at least one questionnaire test for diagnosing nocturia.

[0099] For example, a questionnaire for diagnosing nocturia may include at least one of the following: a first questionnaire about past medical history, a second questionnaire about the frequency of nocturia, a third questionnaire about sleep quality, a fourth questionnaire about overactive bladder, and a fifth questionnaire about prostate symptoms. Additionally, a questionnaire for diagnosing nocturia may include a voiding diary, which includes information about daily urine volume, frequency of urination, and frequency of nocturia.

[0100] Additionally, the user terminal may obtain a final diagnosis result for nocturia using the diagnosis results for at least one of lower urinary tract symptoms, overactive bladder, nocturia, sleep apnea, and sleep disorders. Here, the final diagnosis result for nocturia may include at least one of whether the user has nocturia and the severity of the nocturia.

[0101] Additionally, the user terminal can obtain additional information about the medications the user is taking. For example, the user terminal can obtain additional information about the medication the user is taking to treat a urological condition. Here, the medication for treating a urological condition may include, but is not limited to, desmopressin.

[0102] In step 420, the user terminal can determine a treatment group for treating nocturia based on the diagnosis results.

[0103] For example, the user terminal can determine a treatment group for treating nocturia based on the severity of the symptoms.

[0104] For example, the user terminal may first classify the user into a treatment group included in the first treatment group based on the diagnosis of at least one of lower urinary tract symptoms, overactive bladder, nocturia, sleep apnea, and sleep disorders. Furthermore, the user terminal may secondarily classify the user into a treatment group included in the second treatment group based on the user's basic health information.

[0105] In one embodiment, the user terminal may first classify the user into a treatment group included in the first treatment group using at least one of a diagnosis result for at least one of lower urinary tract symptoms, overactive bladder, nocturia, sleep apnea, and sleep disorder, and information about a medication being taken for the treatment of a urological disease.

[0106] Additionally, the user terminal can determine the user's final treatment group based on the first classification result and the second classification result.

[0107] At step 430, the user terminal can generate a treatment plan for the treatment group.

[0108] For example, the treatment plan may include at least one treatment plan (40) among a first treatment plan for exercise performance, a second treatment plan for dietary control, a third treatment plan for sleep, and a fourth treatment plan for medication.

[0109] In one embodiment, the user terminal can generate a treatment plan using information about the treatment group determined by the user using a database in which preset treatment plans for each treatment group are stored.

[0110] In another embodiment, the user terminal may obtain at least one of a first indicator of kidney function and a second indicator of body electrolytes to generate a treatment plan regarding drug administration.

[0111] Here, the first indicator may refer to the level of creatine in the body, and the second indicator may refer to the level of sodium in the body.

[0112] For example, the user terminal may generate a treatment plan to not take a medication for treating nocturia if the user's body creatinine level is 1.5 ng / ml or higher. Furthermore, the user terminal may generate a treatment plan to not take a medication for treating nocturia if the user's body sodium level is 132 ng / ml or lower. Furthermore, the user terminal may generate a treatment plan to stop taking the medication for treating nocturia if the body sodium level is 132 ng / ml or lower after prescribing the medication for treating nocturia. Furthermore, the user terminal may generate a treatment plan to stop taking the medication for treating nocturia if facial edema occurs after prescribing the medication for treating nocturia.

[0113] In another embodiment, the user terminal may obtain at least one of a first indicator of kidney function and a second indicator of body electrolytes at predetermined intervals, and generate a treatment plan based on a change in at least one of the first indicator and the second indicator according to the user's intake of a drug for the treatment of a urological disease.

[0114] For example, the user terminal can compare the creatine level in the body before prescribing the drug and the creatine level in the body after prescribing the drug, and if the change in the creatine level in the body exceeds a predetermined threshold, a treatment plan can be generated to stop taking the drug being taken.

[0115] For example, a user terminal can generate a treatment plan using a machine learning model, such as a neural network model.

[0116] In machine learning technology and cognitive science, a neural network model refers to a statistical learning algorithm implemented based on the structure of a biological neural network, or a structure that executes the algorithm.

[0117] For example, a neural network model can represent a model with problem-solving capabilities. Nodes, artificial neurons that form a network by combining synapses, similar to biological neural networks, repeatedly adjust the weights of synapses to learn to reduce the error between the correct output corresponding to a specific input and the inferred output. For example, a neural network model can include any probabilistic model used in artificial intelligence learning methods such as machine learning and deep learning, as well as neural network models.

[0118] For example, a neural network model can be implemented as a multilayer perceptron (MLP) composed of multiple layers of nodes and connections between them. The neural network model according to the present embodiment can be implemented using one of various artificial neural network model structures including MLP. For example, the neural network model can be composed of an input layer that receives an input signal or data from the outside, an output layer that outputs an output signal or data corresponding to the input data, and at least one hidden layer located between the input layer and the output layer that receives a signal from the input layer, extracts characteristics, and transmits the same to the output layer. The output layer receives a signal or data from the hidden layer and outputs it to the outside.

[0119] For example, a user terminal can train a neural network model using treatment group information as input data and treatment plans for the corresponding treatment group as output data.

[0120] Here, the data for training the neural network model, i.e., input and output data, may include information on treatment groups for existing patients, treatment combinations performed, and the degree of treatment effectiveness for nocturia based on the treatment. The degree of treatment effectiveness may include the complete cure of symptoms such as lower extremity urinary incontinence, overactive bladder, nocturia, sleep apnea, and sleep disorders, and / or the degree of improvement in nocturia.

[0121] For example, referring to Figure 6a, data for training a neural network model may include patients belonging to treatment group 1-1, combinations of treatment methods performed on the patients, and the degree of improvement in nocturia according to the performance of the combinations of treatment methods. The neural network model may use this training data to generate a final treatment method that exhibits the optimal treatment effect for treatment group 1-1. In addition, the user terminal may train the neural network model using training data that includes information on combinations of treatment methods previously performed on patients belonging to other treatment groups and the degree of improvement in nocturia.

[0122] Specifically, the user terminal can train a neural network model by using information about the treatment group as input data and the treatment method that showed the highest degree of improvement in nocturia as output data.

[0123] In addition to data collected on existing patients, data for training a neural network model can also include data on patients receiving remote treatment via user devices. For example, if a specific treatment plan is suggested for a user identified as belonging to a specific treatment group, the user can input information on the degree of improvement in nocturia after performing the plan via the user device. This information regarding the treatment effectiveness received from the user, along with the treatment group and the treatment plan performed, can be used as training data for the neural network model. As the data for training the neural network model increases, the optimal combination of treatment plans may change.

[0124] Additionally, information about a user's treatment period can be included in the training data for a neural network model. For example, information about the treatment period (e.g., weeks 1, 2, and 3) for nocturia can be used as training data for a neural network model, along with the user's treatment group, the combination of treatment options provided to the user, and the degree of improvement in nocturia following the treatment. When training a neural network model using this training data, the optimal treatment combination for a user in a specific treatment group for week 1 may differ from the optimal treatment combination for week 2.

[0125] Additionally, information about existing patients, such as gender, age, and weight, may also be included in the training data. In this case, the optimal treatment combination may vary by age group, gender, and weight. The user terminal can input the treatment group information determined for the user into a trained neural network model to generate a treatment plan for the user.

[0126] In addition, although not shown in FIG. 4, the user terminal can treat the user's nocturia by presenting at least one generated treatment plan to a user belonging to the treatment group through the user terminal.

[0127] FIG. 4b is a flowchart illustrating a method for monitoring the performance of a treatment plan for nocturia in a non-face-to-face manner according to one embodiment.

[0128] Referring to FIG. 4b, a method for monitoring the execution process of a treatment plan for nocturia in a non-face-to-face manner is comprised of steps that are processed in a time-series manner in the user terminal (100) and / or processor (110) illustrated in FIG. 2. Therefore, even if omitted below, the content described above regarding the user terminal (100) or processor (110) illustrated in FIG. 2 can also be applied to the method for monitoring the execution process of a treatment plan for nocturia in a non-face-to-face manner in FIG. 4b.

[0129] In step 440, the user terminal can generate a treatment plan based on the diagnosis results for at least one of lower urinary tract symptoms, overactive bladder, nocturia, sleep apnea, and sleep disorders.

[0130] Here, the process of generating a treatment plan may be the same as the process of generating a treatment plan in step 430 of FIG. 4a described above.

[0131] In step 450, the user terminal can monitor the progress of the treatment plan at predetermined intervals.

[0132] For example, the user terminal may monitor the user's performance of the first treatment plan based on a video of the user performing an exercise. Furthermore, the user terminal may monitor the user's performance of the second treatment plan based on the results of a questionnaire regarding the user's eating habits. Furthermore, the user terminal may monitor the user's performance of the third treatment plan based on at least one of a sleep video or a sleep sound. Furthermore, the user terminal may monitor the user's performance of the fourth treatment plan using at least one of a first indicator of the user's renal function and a second indicator of the user's body electrolytes.

[0133] Additionally, the user terminal can calculate the degree of improvement in nocturia based on the monitoring results of the treatment plan. Furthermore, after the treatment plan is completed, the user terminal can conduct a questionnaire to determine the degree of improvement in nocturia.

[0134] Additionally, the user terminal can generate a management schedule for nocturia using the degree of improvement in nocturia. For example, the management schedule may include, but is not limited to, the treatment plan implementation cycle and the need for in-patient treatment. Specifically, if the user's degree of improvement exceeds a predetermined threshold, the user terminal may determine that the nocturia symptoms have sufficiently improved and suggest that the treatment plan be re-implemented after a predetermined period of time. Furthermore, if the user's degree of improvement does not exceed a predetermined threshold, the user terminal may determine that the nocturia symptoms have not sufficiently improved and suggest that the user undergo in-patient treatment.

[0135] Additionally, the user terminal can update the user's previously implemented treatment plan based on the monitoring results of the treatment plan. For example, if the degree of improvement in the user's nocturia exceeds a predetermined threshold, the user terminal can determine that the symptoms of the nocturia have sufficiently improved and update the treatment plan by lowering the intensity of the treatment plan. Furthermore, if the degree of improvement in the user's nocturia does not exceed the predetermined threshold, the user terminal can determine that the symptoms of the nocturia have not sufficiently improved and update the treatment plan by increasing the intensity of the treatment plan.

[0136] FIG. 5A and FIG. 5B are exemplary drawings illustrating a method for determining a treatment group according to one embodiment.

[0137] The user terminal can determine the final treatment group of the user by using the results of the first classification, which classifies the user into one of the treatment groups included in the first treatment group based on at least one of the diagnosis results for nocturia, lower urinary tract symptoms, overactive bladder, sleep apnea, and sleep disorder, and whether or not the user is taking medication for the treatment of a urological disease, and the results of the second classification, which classifies the user into one of the treatment groups included in the second treatment group based on basic health information.

[0138] Referring to FIG. 5a, table (510) corresponds to a table showing the treatment groups included in the first treatment group.

[0139] For example, a user terminal can divide a patient into treatment groups based on whether or not they experience nocturnal polyuria. Furthermore, for users diagnosed with nocturnal polyuria, the user terminal can divide the patient into treatment groups based on whether or not they are prescribed medications related to nocturnal polyuria. For example, for users diagnosed with nocturnal polyuria, the user terminal can further divide the patient into treatment groups based on whether or not they are prescribed demopressin, a medication for treating urological disorders.

[0140] Additionally, the user terminal can divide the treatment group based on the presence of lower urinary tract symptoms or overactive bladder. For example, if the user is male, the user terminal can divide the treatment group based on the presence of lower urinary tract symptoms. If the user is female, the user terminal can divide the treatment group based on the presence of overactive bladder.

[0141] Additionally, the user terminal can divide the treatment group based on the presence of a sleep disorder or sleep apnea. For example, the user terminal can divide the treatment group based on the presence of a sleep disorder. Furthermore, for users with sleep disorders, the user terminal can divide the treatment group based on the presence of sleep apnea.

[0142] For example, the user terminal may include treatment groups 1-1 to 1-18 in the first treatment group based on the presence or absence of nocturnal polyuria, taking medication for the treatment of urinary diseases, having lower urinary tract symptoms or overactive bladder, having sleep disorders, and having sleep apnea.

[0143] Referring to FIG. 5b, table (520) corresponds to a table showing the treatment groups included in the second treatment group.

[0144] For example, a user terminal can be divided into treatment groups based on whether the user has diabetes. Furthermore, the user terminal can be divided into treatment groups based on whether the user has heart disease. Furthermore, the user terminal can be divided into treatment groups based on whether the user is obese.

[0145] For example, the user terminal may include treatment groups 2-1 to 2-8 in the second treatment group based on the presence or absence of diabetes, heart disease, and obesity described above.

[0146] For example, the user terminal can determine the user's final treatment group based on the first classification result into the first treatment group and the second classification result into the second treatment group.

[0147] For example, the user terminal can generate a total of 144 final treatment groups, including 18 treatment groups in the first treatment group and 8 treatment groups in the second treatment group. Furthermore, the user terminal can suggest different treatment options for each of the 144 final treatment groups.

[0148] FIGS. 6A and 6B are exemplary drawings illustrating a method for generating a treatment plan for a treatment group according to one embodiment.

[0149] The user terminal can generate a treatment plan based on information about the determined treatment group.

[0150] For example, the user terminal can generate a final treatment plan by synthesizing a treatment plan generated based on information about the first treatment group and a treatment plan generated based on information about the second treatment group.

[0151] Referring to FIG. 6a, table (610) corresponds to a table showing a treatment plan generated based on information on the first treatment group.

[0152] The treatment plan may include at least one of the following: a first treatment plan for exercise performance, a second treatment plan for dietary control, a third treatment plan for sleep, and a fourth treatment plan for medication.

[0153] For example, the user terminal may generate a final treatment plan by varying the intensity or duration of each treatment plan for each treatment group. For example, the user terminal may generate a final treatment plan by varying the intensity or duration of the first treatment plan generated for the first treatment group 1-1, which includes users diagnosed with at least one of lower urinary tract symptoms or overactive bladder, and the intensity or duration of the first treatment plan generated for the second treatment group 2-1, which includes users diagnosed with obesity.

[0154] The user terminal can generate a final treatment plan including a first treatment plan for exercise performance for a treatment group including users diagnosed with at least one of lower urinary tract symptoms or overactive bladder. Here, the user terminal can generate a first treatment plan for a performance schedule including the intensity and duration of the exercise.

[0155] The user terminal may generate a final treatment plan including a second treatment plan for dietary control for a treatment group including users diagnosed with nocturnal polyuria. Here, the second treatment plan may include at least one of a treatment plan for allowing daily fluid intake exceeding the appropriate amount of water intake, a treatment plan for allowing daily salt intake below the appropriate amount of salt intake, a treatment plan for reducing the intake of processed foods, and a treatment plan for allowing daily caffeine intake below the appropriate amount of caffeine intake, but is not limited thereto.

[0156] For example, a user terminal can calculate an appropriate amount of water intake for each user based on their body weight. Specifically, the user terminal can calculate 3% of the user's body weight as the appropriate amount of water intake for the user.

[0157] The user terminal may generate a final treatment plan including a sleep treatment plan 3-1 for a treatment group including users diagnosed with a sleep disorder. Here, the 3-1 treatment plan may include at least one of, but is not limited to, a treatment plan for maintaining a constant sleep time, a treatment plan for avoiding daytime sleep, a treatment plan for reducing food intake before sleep, a treatment plan for reducing physical movement before sleep, and a treatment plan for maintaining a constant sleep environment.

[0158] The user terminal can generate a final treatment plan, including a third-second treatment plan for sleep, for a treatment group including users diagnosed with sleep apnea. It is understood that the third-second treatment plan may include, but is not limited to, at least one of a treatment plan for correcting sleeping posture and a treatment plan for reducing the intake of sleeping pills.

[0159] In another embodiment, the user terminal may generate a final treatment plan that includes a second treatment plan for sleep for a treatment group including users diagnosed with sleep apnea. The second treatment plan may include, but is not limited to, a treatment plan for reducing alcohol consumption.

[0160] The user terminal may generate a final treatment plan, including a fourth treatment plan for drug administration, for a treatment group including users taking medication for the treatment of urological diseases. The fourth treatment plan may include a treatment plan for discontinuing the administration of a drug if the change in at least one of a first indicator of renal function and a second indicator of body electrolytes exceeds a predetermined threshold value due to drug administration.

[0161] Referring to FIG. 6b, table (620) corresponds to a table showing a treatment plan generated based on information on the second treatment group.

[0162] The user terminal can generate a final treatment plan, including a first treatment plan for exercise performance, for a treatment group including users diagnosed with obesity or users diagnosed with both diabetes and heart disease. Here, the user terminal can generate a first treatment plan for a performance schedule, including the intensity and duration of the exercise.

[0163] For example, the exercises in the first treatment plan may include a variety of exercises such as walking, push-ups, squats, lunges, running kicks, and crutches.

[0164] The user terminal may generate a final treatment plan including a second treatment plan for dietary control for a treatment group including users diagnosed with at least one of obesity, diabetes, and heart disease. Here, the second treatment plan may include, but is not limited to, a treatment plan for ensuring that each user consumes calories below the set appropriate calorie intake, a treatment plan for maintaining consistent meal times, a treatment plan for ensuring that meals with low fat content are consumed, and a treatment plan for reducing snack intake.

[0165] Additionally, the user terminal may provide educational materials on the implementation of the treatment plan to enhance its effectiveness. Furthermore, the user terminal may periodically generate notifications regarding the availability of educational materials, allowing the user to access them. For example, the educational materials may include educational materials on dietary control, which is included in the second treatment plan.

[0166] Referring to FIGS. 7 to 9 below, a method for monitoring the execution process of a treatment plan is described in detail.

[0167] FIG. 7 is an exemplary drawing for explaining a method for monitoring the execution process of a first treatment plan according to one embodiment.

[0168] Referring to FIG. 7, the user terminal (710) can monitor the execution process of the first treatment plan for improving symptoms of nocturia diagnosed by the user (720).

[0169] For example, the user terminal (710) can generate a schedule for performing exercise of the first treatment plan based on information about the severity of at least one of lower urinary tract symptoms, overactive bladder, obesity, heart disease, and diabetes diagnosed by the user (720).

[0170] For example, the exercises in the first treatment plan may include a variety of exercises such as walking, push-ups, squats, lunges, running kicks, and crutches.

[0171] For example, the user terminal (710) can generate a schedule for performing exercise for a preset period of time. In addition, the user terminal (710) can receive a period for generating an exercise schedule from the user (720) and generate a schedule accordingly.

[0172] Additionally, the user terminal (710) can capture the user's (720) exercise performance according to the execution schedule of the first treatment plan. Furthermore, the user terminal (710) can capture the user's (720) exercise performance from multiple angles. For example, the user terminal (710) may include an imaging device such as a camera.

[0173] In another embodiment, the user terminal (710) can obtain an image of the user's (720) first treatment plan execution process from an external device.

[0174] The user terminal (710) can monitor the performance progress of the first treatment plan of the user (720) and calculate a performance score (730). For example, the user terminal (710) can calculate at least one of detailed information and time information regarding the exercise performance progress of the user (720) based on the captured exercise performance video.

[0175] As an example, the user terminal (710) can calculate skeletal information of the user (720) based on an exercise performance video. In addition, the user terminal (710) can calculate posture information during the exercise performance process of the user (720) based on the calculated skeletal information.

[0176] Specifically, the user terminal (710) can generate a three-dimensional (3D) mesh of the user's (720) body based on an exercise performance video. The user terminal (710) can distinguish body parts of the user (720) based on the three-dimensional (3D) mesh of the user's (720) body and calculate skeletal information based on the distinguished body parts. Here, the distinguished body parts may include, but are not limited to, the waist, thighs, calves, arms, and head. In addition, the user terminal (710) can generate posture information including information on the angle of the skeleton during the exercise performance based on the skeletal information.

[0177] The user terminal (710) can calculate a performance score (730) for the first treatment plan based on at least one of the posture information and time information during the exercise performance process of the user (720).

[0178] Additionally, the user terminal (710) can generate feedback (740) based on the performance score (730). For example, the feedback (740) may include a notification recommending correction of exercise posture. Through feedback (740), the user can improve the nocturia treatment effect of the first treatment method by correcting exercise posture.

[0179] Additionally, feedback (740) may be output as voice through the user terminal (710). For example, while the user (720) is exercising, a third party may use the user terminal (710) to capture the user's (720) exercise. The user terminal (710) may analyze the captured image or video in real time to calculate a performance score (730), and output feedback (740) based on the performance score in real time as voice.

[0180] According to one embodiment, if the performance score (730) remains below a predetermined value for a certain period of time, the user terminal (710) may suggest that the user perform another exercise that is easier to perform, thereby enhancing the treatment effect of nocturia. For example, if the user performs a push-up exercise with a score below 50 for a certain period of time, the user terminal (710) may suggest another exercise as a first treatment method using physical information such as the user's age, gender, body size, etc. The newly suggested treatment method may be an exercise that is easy for the user to perform, and the user may enhance the treatment effect of nocturia by performing the treatment method with a high performance score.

[0181] Through this, the user terminal (710) can enhance the treatment effect by performing the first treatment method for improving symptoms of urological diseases without a visit to the hospital.

[0182] According to another embodiment, the treatment group to which the user belongs, the combination of specific treatment methods performed by the user (720), and the degree of improvement in nocturia may be included in the learning data, and a neural network model may be trained using this learning data. The trained neural network model may be used to identify a combination of specific exercises that is most effective in treating nocturia for a user belonging to a specific treatment group. For example, if the user belongs to a specific treatment group (e.g., treatment group 1-1, treatment group 1-2, etc.), the user terminal may use the neural network model to generate a final treatment method in which the combination of exercises that most effectively treats nocturia for the user is performed, for example, in a ratio of 5:3:2, of push-ups, squats, and lunges.

[0183] FIG. 8 is an exemplary drawing for explaining a method for monitoring the execution process of a second treatment plan according to one embodiment.

[0184] Referring to FIG. 8, the user terminal (810) can monitor the execution process of the second treatment plan for improving symptoms of nocturia diagnosed by the user.

[0185] For example, the user terminal (810) may perform a questionnaire test (820) on eating habits to monitor the progress of the second treatment plan for dietary control.

[0186] For example, the questionnaire (820) may include, but is not limited to, questions about at least one of meal times, calorie intake, water intake, caffeine intake, and salt intake.

[0187] Additionally, the user terminal (810) can synthesize the values ​​for each questionnaire to calculate the result value for the questionnaire test (820). For example, the user terminal (810) can calculate the value for each questionnaire using preset criteria. Specifically, the user terminal (810) can calculate the value for the questionnaire asking about the amount of water intake by comparing the appropriate amount of water intake with the user's water intake.

[0188] Additionally, the user terminal (810) may generate an alarm for improving eating habits based on the results of the questionnaire (820). For example, if the user terminal (810) determines that the second treatment plan is not being properly implemented by comparing the results of the questionnaire (820) with a predetermined threshold value, the user terminal (810) may generate an alarm to suggest to the user to improve eating habits.

[0189] According to one embodiment, the treatment group to which the user belongs, the threshold value compared with the results of the questionnaire test, and the degree of improvement in nocturia may be included in the data for training the neural network model, and the neural network model may be trained using such training data.

[0190] The trained neural network model can be used to determine the threshold that maximizes the treatment effect for nocturia for users in a specific treatment group. For example, if a user falls into a specific treatment group (e.g., Group 1-1, Group 1-2, etc.), the user terminal can use the trained neural network model to set the threshold for the most effective treatment of the user's nocturia.

[0191] In addition, the user terminal (810) can perform a questionnaire test (820) at a predetermined time interval. According to one embodiment, the treatment group to which the user belongs, the frequency at which the questionnaire test is performed, and the degree of improvement in nocturia may be included in the learning data, and the neural network model may be trained using this learning data. The trained neural network model may be used to determine the frequency at which the questionnaire test is performed to have the highest treatment effect on nocturia for a user belonging to a specific treatment group. For example, if the user belongs to a specific treatment group (e.g., treatment group 1-1, treatment group 1-2, etc.), the user terminal may determine, using the trained neural network model, that the questionnaire test should be performed once every three days in order to most effectively treat the user's nocturia.

[0192] FIG. 9 is an exemplary drawing for explaining a method for monitoring the execution process of a third treatment plan according to one embodiment.

[0193] Referring to FIG. 9, the user terminal (910) can monitor the execution process of the third treatment plan for improving the symptoms of nocturia diagnosed by the user (920).

[0194] For example, in order to monitor the performance of the third treatment plan regarding sleep of the user terminal (910), a sleep image or sleep sound of the user (920) may be acquired. Here, the sleep image may refer to an image capturing the sleeping posture of the user (920), and the sleep sound may refer to a recording of sounds generated by the user (920) while sleeping.

[0195] The user terminal (910) can monitor the user's (920) third treatment plan performance process using at least one of a sleep image and a sleep sound.

[0196] In one embodiment, the user terminal (910) may use the sleep image to derive information about the user's (920) sleeping posture. Furthermore, the user terminal (910) may derive a performance score (930) for the third treatment plan based on the information about the sleeping posture. Furthermore, the user terminal (910) may generate feedback (940) based on the performance score (930).

[0197] Feedback (940) may be output as voice through the user terminal (910). For example, while the user (940) is in a specific sleeping position, a third party may use the user terminal (910) to capture the sleeping position of the user (920). The user terminal (910) may analyze the captured image or video in real time to calculate a performance score (930) and output feedback (940) as voice in real time.

[0198] For example, the user terminal (910) can derive skeletal information of the user (920) using a sleep image. In addition, the user terminal (910) can derive information about the sleeping posture of the user (920) using the derived skeletal information.

[0199] For example, the user terminal (910) can calculate the angle formed by the spine and neck of the user (920) as information about the sleeping posture. The user terminal (910) can calculate a performance score (930) by comparing the calculated angle with a preset threshold value.

[0200] According to one embodiment, the treatment group to which the user belongs, the angle formed by the user's spine and neck, and the degree of improvement in nocturia may be included in the learning data, and a neural network model may be trained using this learning data. The trained neural network model may be used to identify the angle formed by the spine and neck that has the highest treatment effect for nocturia for a user belonging to a specific treatment group. For example, if the user belongs to a specific treatment group (e.g., treatment group 1-1, treatment group 1-2, etc.), the user terminal may use the trained neural network model to calculate the optimal angle formed by the spine and neck for the most effective treatment of the user's nocturia, and may set this as a threshold for calculating a performance score.

[0201] In another embodiment, the user terminal (910) may use sleep sounds to derive information about the user's (920) sleep apnea. Furthermore, the user terminal (910) may derive a performance score (930) for the third treatment plan based on the information about sleep apnea. Furthermore, the user terminal (910) may generate feedback (940) based on the performance score (930).

[0202] For example, the user terminal (910) can use the sleep sound to produce at least one of information on the number of apnea episodes during sleep, time information, and decibel information as information on apnea during sleep of the user (920).

[0203] Additionally, the user terminal (910) can compare information about sleep apnea of ​​the user (920) with a preset threshold value to calculate a performance score (930).

[0204] Additionally, the user terminal (910) may generate feedback (940) based on the performance score (930). For example, if the performance score (930) does not exceed a predetermined threshold, the user terminal (910) may determine that the third treatment plan is not being properly performed and may generate a notification recommending modification of the sleeping position or sleeping environment.

[0205] Through this, the user terminal (910) can enhance the treatment effect by performing a third treatment method for improving symptoms of urological diseases that is performed non-face-to-face without a visit to the hospital.

[0206] Embodiments of the present invention may be implemented in the form of a computer program that can be executed through various components on a computer, and such a computer program may be recorded on a computer-readable medium. In this case, the medium may include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specifically configured to store and execute program instructions, such as ROMs, RAMs, and flash memories.

[0207] Meanwhile, the computer program may be specifically designed and constructed for the present invention, or may be one known and available to those skilled in the computer software field. Examples of computer programs may include not only machine language code, such as that generated by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like.

[0208] According to one embodiment, the method according to various embodiments of the present disclosure may be provided as included in a computer program product. The computer program product may be traded as a product between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or may be distributed online (e.g., downloaded or uploaded) through an application store (e.g., Play Store™) or directly between two user devices. In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily generated in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

[0209] Unless the steps constituting the method according to the present invention are explicitly described in a specific order or are not described to the contrary, the steps may be performed in any appropriate order. The present invention is not necessarily limited to the order in which the steps are described. The use of all examples or exemplary terms (e.g., "for example," etc.) in the present invention is merely intended to illustrate the present invention in detail, and the scope of the present invention is not limited by the examples or exemplary terms unless otherwise defined by the claims. Furthermore, those skilled in the art will appreciate that various modifications, combinations, and variations can be configured according to design conditions and factors within the scope of the appended claims or their equivalents.

[0210] Therefore, the idea of ​​the present invention should not be limited to the embodiments described above, and all scopes equivalent to or equivalently modified from the following claims as well as the claims are considered to fall within the scope of the idea of ​​the present invention.

Claims

1. A step of obtaining a diagnosis result for at least one of lower urinary tract symptoms (LUTS), overactive bladder (OAB), nocturnal polyuria (NP), obstructive sleep apnea (OSA), and sleep disorder for the user; A step of determining a treatment group for treating nocturia to which the user belongs based on the above diagnosis results; A step of generating a treatment plan for the above treatment group; and A step of treating the nocturia of the user by presenting the above treatment plan to the user belonging to the treatment group through a user terminal; The above treatment plan is, A method for presenting a treatment plan for nocturia through a user terminal, the treatment plan including at least one of a first treatment plan for exercise performance, a second treatment plan for dietary control, a third treatment plan for sleep, and a fourth treatment plan for medication, in a non-face-to-face manner.

2. In paragraph 1, The above acquisition steps are: Get additional information about the medications you are taking, The step of determining the above treatment group is: A method for determining the treatment group based on at least one of the above diagnostic results and information about the medication being taken.

3. In paragraph 2, The above method, Further comprising a step of obtaining at least one of a first indicator of kidney function and a second indicator of body electrolytes at regular intervals; The steps for generating the above treatment plan are: A method for generating the fourth treatment plan based on a change in at least one of the first indicator and the second indicator according to taking the drug.

4. In paragraph 1, The above acquisition steps are: Obtain additional basic health information, The steps for generating the above treatment plan are: Generate the treatment plan based on at least one of the above diagnostic results and the above basic health information, The above basic health information is: A method comprising at least one of information regarding gender, weight, diabetes, heart disease, and obesity.

5. In paragraph 1, The steps for generating the above treatment plan are: A step of creating a schedule for performing the exercise based on the severity of the nocturia disorder; and A step of generating a first treatment plan that causes exercise to be performed according to the above performance schedule; A method comprising:

6. In paragraph 1, The steps for generating the above treatment plan are: A step for calculating the appropriate amount of water intake using basic health information; and A step of generating a second treatment plan that causes the patient to consume more water than the above-mentioned appropriate water intake; A method comprising:

7. In paragraph 1, The steps for generating the above treatment plan are: A method for generating a third treatment plan that allows sleep by maintaining an appropriate sleeping posture.

8. In paragraph 1, The above method, Further comprising a step of generating an interface displaying information for performing the above treatment plan; Information for carrying out the above treatment plan is: A method comprising at least one of information on the performance schedule of the above treatment plan, image information including an appropriate exercise posture, image information including an appropriate sleep posture, and appropriate diet information.

9. In paragraph 1, The step of generating the above treatment plan for the above treatment group is: A method for generating the treatment plan by inputting information about the above treatment group into a neural network model.

10. In paragraph 9, The above method, further comprising a step of training the neural network model using learning data; The above learning data is, A method comprising information on each treatment group, information on each treatment plan performed for each treatment group, and information on the degree of improvement in nocturia according to the performance of the treatment plan for each treatment group.

11. At least one memory; and comprising at least one processor; At least one processor, Obtaining a diagnosis result for at least one of lower urinary tract symptoms (LUTS), overactive bladder (OAB), nocturnal polyuria (NP), obstructive sleep apnea (OSA), and sleep disorder for a user, determining a treatment group for treating the nocturia disorder to which the user belongs based on the diagnosis result, generating a treatment plan for the treatment group, and presenting the treatment plan to the user belonging to the treatment group through a user terminal to treat the nocturia disorder of the user, The above treatment plan is, A computing device comprising at least one treatment plan among a first treatment plan for exercise performance, a second treatment plan for dietary control, a third treatment plan for sleep, and a fourth treatment plan for medication.

12. A computer-readable recording medium recording a program for executing the method according to paragraph 1 on a computer.

Images