[go: up one dir, main page]

WO2023042971A1 - Procédé de prédiction et de prise en charge de trouble menstruel, et agent thérapeutique numérique - Google Patents

Procédé de prédiction et de prise en charge de trouble menstruel, et agent thérapeutique numérique Download PDF

Info

Publication number
WO2023042971A1
WO2023042971A1 PCT/KR2021/019041 KR2021019041W WO2023042971A1 WO 2023042971 A1 WO2023042971 A1 WO 2023042971A1 KR 2021019041 W KR2021019041 W KR 2021019041W WO 2023042971 A1 WO2023042971 A1 WO 2023042971A1
Authority
WO
WIPO (PCT)
Prior art keywords
menstrual
patient
therapy
disorder
predicting
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/KR2021/019041
Other languages
English (en)
Korean (ko)
Inventor
김선현
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Walden Dt Corp
Original Assignee
Walden Dt Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from KR1020210164488A external-priority patent/KR20230041555A/ko
Application filed by Walden Dt Corp filed Critical Walden Dt Corp
Publication of WO2023042971A1 publication Critical patent/WO2023042971A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/021—Measuring pressure in heart or blood vessels
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/024—Measuring pulse rate or heart rate
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y40/00—IoT characterised by the purpose of the information processing
    • G16Y40/10—Detection; Monitoring

Definitions

  • the present invention relates to digital treatment technology, and in particular, menstrual disorders that predict and evaluate menstrual disorders by themselves using clinical information of a target patient acquired through a digital device, and manage menstrual disorders of the target patient according to the results. It relates to methods and digital therapeutics for predicting and managing
  • Premenstrual syndrome generally refers to the appearance of physical, mental and behavioral symptoms that interfere with daily activities related to menstruation, and is caused by various factors such as hormonal changes due to the menstrual cycle and is accompanied by various emotional and physical symptoms ( see reference [1]).
  • a severe form of premenstrual syndrome is called premenstrual dysphoric disorder, which is a disorder that causes severe impairment in daily life.
  • Premenstrual syndrome has a characteristic that it occurs periodically and symptoms mainly occur in the luteal phase after ovulation, and these symptoms may be severe enough to interfere with daily life.
  • Psychological symptoms of premenstrual syndrome include tension, anxiety, sensitivity, etc., nervousness, depression, and sometimes hostility toward people around you for no reason. In general, they avoid social life and want to be alone, and sometimes they do not do their usual work properly. In severe cases, they lose self-control and shout loudly or fight with others. In addition, physical symptoms include fatigue, headache, back pain, breast pain, etc., swelling of the hands and feet, swelling of the stomach, muscle pain, and in some cases, a morbid desire to eat salty or sweet foods.
  • PMS lasts for a very long time and can afflict women. It is known that some women experience recurrences of PMS symptoms before each menstrual cycle and that these patterns persist until menopause, and that women with PMS or PMS are at greater risk of developing depression.
  • premenstrual syndrome gets severe, it can cause serious disorders in daily life, so professional management and treatment are essential.
  • premenstrual syndrome for granted and overlook the seriousness of the disease or seek solutions based on physical symptoms.
  • 1 out of 3 women experience premenstrual syndrome, and 80% of them are affected by it in their daily lives, but most do not receive professional treatment.
  • Korean Patent Laid-open Publication No. 10-2021-0086246 (published on July 8, 2021, title of invention: "Digital device and application for treating mild cognitive impairment and dementia") is for mild cognitive impairment (MCI) and / Or based on the mechanism of action (MOA) and treatment hypothesis for dementia, create a digital therapeutic module for the treatment of the mild cognitive impairment and the dementia, and use the digital therapeutic module Mild cognitive impairment comprising a digital task generating unit that generates a specific digital task based on the digital task and provides the digital task to a first user, and a result collection unit that collects a result of the first user's performance on the digital task; and Disclosed is a digital device for the treatment of dementia, which derives pathogenesis, treatment hypotheses, and digital treatment hypotheses in consideration of amnestic mild cognitive impairment and neurological factors in the progression of Alzheimer's disease. By presenting digital tasks and collecting and analyzing the performance results, it is possible to suppress the progression of amnestic mild cognitive impairment and Alzheimer's disease and provide improved therapeutic effects.
  • An object of the present invention is to analyze the symptoms of menstrual disorders for patients with menstrual disorders through smartphone applications and smart devices, to predict and evaluate menstrual disorders, and to provide a method for alleviating symptoms through behavioral treatment and medication guidance. is to do
  • an object of the present invention is to divide menstrual disorder patients into patients who need to come to the hospital and patients who do not need to visit depending on the severity, to provide self-therapy in the former case, and to provide medical medication through more precise diagnosis in the latter case It is to provide a digital treatment for predicting and managing menstrual disorders, providing prescriptions or surgical prescriptions.
  • the method according to the present invention comprises the steps of receiving state information about the patient's physical state and psychological state related to the patient's menstrual disorder measured and collected by a digital smart device; Collecting and analyzing the received condition information by a menstrual disorder prediction and evaluation unit, and predicting or evaluating whether or not the patient has menstrual disorder and the severity of the menstrual disorder according to the analysis result; And when the patient is predicted or evaluated to have menstrual disorder by the customized therapy generator, menstrual disorder is managed by creating a customized therapy for each patient according to the severity of the menstrual disorder and providing it to the digital smart device.
  • Including the step of generating the therapy and providing it to the digital smart device when the severity of the menstrual disorder is relatively low, providing self-therapy that patients with menstrual disorders can practice without visiting a medical institution thing; If the severity of the menstrual disorder is relatively high, predicting or evaluating additionally the menstrual disorder of a patient visiting a medical institution, and prescribing medical medication guidance or surgical operation in addition to the self-therapy; and modifying the customized therapy for each patient by monitoring the practice rate indicating the extent to which the patient practices self-therapy and medication guidance and the degree of improvement of menstrual disorder recorded by the digital smart device.
  • the condition information includes the patient's body mass index (BMI), vital signs including blood pressure, pulse, and electrocardiogram, menstrual data, menstrual volume, premenstrual syndrome (PMS) symptoms, ovulation date, information on physical condition including at least one of eating behavior and water intake; and information on a psychological state including at least one of a patient's depression level, anxiety level, and stress level.
  • BMI body mass index
  • PMS premenstrual syndrome
  • ovulation date information on physical condition including at least one of eating behavior and water intake
  • information on a psychological state including at least one of a patient's depression level, anxiety level, and stress level.
  • the step of predicting or evaluating whether the patient has menstrual disorder and the severity of menstrual disorder may include storing accumulated data of the patient's condition information; obtaining real-time status information about the patient; and predicting or evaluating the patient's menstrual disorder and its severity based on the accumulated data and real-time status information.
  • the step of predicting or evaluating whether the patient has menstrual disorder and the severity of menstrual disorder is the patient's weight, body mass index (BMI), blood pressure, body temperature, electrocardiogram (ECG/EKG), electromyography (EMG) ), photoplethysmography (PPG), heart rate (HR), heart rate variability (HRV), respiratory rate (RR), and skin perfusion to predict the patient's ovulation period and menstrual cycle.
  • BMI body mass index
  • ECG/EKG electrocardiogram
  • EMG electromyography
  • PPG photoplethysmography
  • HR heart rate variability
  • RR respiratory rate
  • skin perfusion to predict the patient's ovulation period and menstrual cycle.
  • the step of predicting or evaluating whether the patient has menstrual disorder and the severity of menstrual disorder is a Dutch Eating Behavior Questionnaire (DEBQ), a dysmenorrhea questionnaire (MDQ), and a dysmenorrhea scale (Visual Analog Scale; VAS), Menorrhagia Multi-Attribute Quality-of-Life Scale (MMAS), MINI-Plus (MINI International Neuropsychiatric Interview-Plus) 5.0.0, Hamilton Rating Scale for Depression; HAM-D), Spielberger State-Train Anxiety Inventory (STAI), Korean Mental Disorder Inventory (K-MDI), Center for Epidemiologic Studies Depression Scale (CES-D), At least one of the Perceived Stress Scale (PSS) and the Scale for Suicidal Ideation (SSI) are utilized.
  • DEBQ Dutch Eating Behavior Questionnaire
  • MDQ dysmenorrhea questionnaire
  • MMAS Menorrhagia Multi-Attribute Quality-of-Life Scale
  • MINI-Plus
  • the self-therapy includes psychological therapy including at least one of diet therapy, exercise therapy, yoga therapy, release therapy, and breathing therapy; and physical therapy including at least one of music therapy, cognitive-behavioral therapy, and art therapy.
  • the menstrual disorder is premenstrual syndrome, menstrual irregularity, and menstrual dysphoric disorder (PMDD) predicted or evaluated from the condition information measured through the digital smart device and the symptom record input by the patient; dysmenorrhea predicted or evaluated from a symptom record entered by the patient; and at least one of menorrhagia predicted or evaluated from the amount of menstruation input through the IoT weighing scale.
  • PMDD menstrual dysphoric disorder
  • a digital therapeutic agent for predicting and managing menstrual disorders includes a state information receiving unit for receiving state information on the patient's physical state and psychological state related to the patient's menstrual disorder measured and collected by a digital smart device; a menstrual disorder prediction and evaluation unit that collects and analyzes the received condition information, and predicts or evaluates whether or not the patient has menstrual disorder and the severity of the menstrual disorder according to the analysis result; And if the patient is predicted or evaluated to have a menstrual disorder, a menstrual disorder management unit for managing the menstrual disorder by creating a customized therapy for each patient for the menstrual disorder according to the severity of the menstrual disorder and providing it to the digital smart device And, the menstrual disorder prediction and evaluation unit provides self-therapy that patients with menstrual disorders can practice without visiting a medical institution when the severity of menstrual disorders is relatively low, and the severity of menstrual
  • the condition information includes the patient's body mass index (BMI), vital signs including blood pressure, pulse, and electrocardiogram, menstrual data, menstrual volume, premenstrual syndrome (PMS) symptoms, ovulation date, information on physical condition including at least one of eating behavior and water intake; and information on a psychological state including at least one of a patient's depression level, anxiety level, and stress level.
  • BMI body mass index
  • PMS premenstrual syndrome
  • the menstrual disorder prediction and evaluation unit stores accumulated data of the patient's condition information, obtains real-time condition information about the patient, and determines whether or not the menstrual disorder and severity of the patient are based on the accumulated data and real-time condition information. It is further configured to predict or evaluate.
  • the menstrual disorder prediction and evaluation unit patient's weight, body mass index (BMI), blood pressure, body temperature, electrocardiogram (ECG / EKG), electromyography (EMG), photoplethysmography (PPG), heart rate (HR), heart rate It is further configured to predict an ovulation period and menstrual cycle of the patient using at least one of HRV, respiratory rate (RR), and skin perfusion.
  • the menstrual disorder prediction and evaluation unit the Dutch Eating Behavior Questionnaire (DEBQ), the Menstrual Distress Questionnaire (MDQ), the Visual Analog Scale (VAS), and the Menorrhagia Multi-Attribute Quality-of-Life Scale (MMAS), MINI-Plus (MINI International Neuropsychiatric Interview-Plus) 5.0.0, Hamilton Rating Scale for Depression (HAM-D), Spielberger State-Train Anxiety Scale Inventory; STAI), Korean Mental Disorder Inventory (K-MDI), Center for Epidemiologic Studies Depression Scale (CES-D), Perceived Stress Scale (PSS), and suicidal ideation It is configured to utilize at least one of the Scale for Suicidal Ideation (SSI).
  • SSI Scale for Suicidal Ideation
  • the self-therapy includes psychological therapy including at least one of diet therapy, exercise therapy, yoga therapy, relaxation therapy, and breathing therapy; and physical therapy including at least one of music therapy, cognitive-behavioral therapy, and art therapy.
  • the menstrual disorder is premenstrual syndrome, menstrual irregularity and dysmenorrhea disorder (PMDD) predicted or evaluated from condition information measured through the digital smart device and symptom record input by the patient; dysmenorrhea predicted or evaluated from a symptom record entered by the patient; and at least one of menorrhagia predicted or evaluated from the amount of menstruation input through the IoT weighing scale.
  • PMDD menstrual irregularity and dysmenorrhea disorder
  • the present invention it is possible to analyze symptoms of menstrual disorders through smart phone applications and smart devices, predict and evaluate menstrual disorders, and alleviate symptoms through behavioral treatment and medication guidance.
  • menstrual disorder patients are divided into patients who need to come to the hospital and patients who do not need to visit depending on the severity, and in the former case, self-therapy is provided, and in the latter case, more precise diagnosis through medical medication Menstrual disorders can be managed by providing prescriptions or surgical prescriptions.
  • FIG. 1 is a flowchart schematically illustrating a method for predicting and managing menstrual disorders according to an aspect of the present invention.
  • FIG. 2 is a diagram briefly illustrating the operation process of the method for predicting and managing menstrual disorders of FIG. 1 .
  • FIG. 3 is a diagram illustrating differently the operation process of the method for predicting and managing menstrual disorders of FIG. 2 .
  • 4A and 4B are diagrams illustrating a process in which data input by a patient to an application is used for predicting and managing menstrual disorders.
  • FIG. 5 is a diagram illustrating a process in which biometric information and input data of a patient are used to predict and manage menstrual disorders.
  • FIG. 7 is a block diagram schematically illustrating an operating environment of a digital therapeutic agent for predicting and managing menstrual disorders according to another aspect of the present invention.
  • FIG. 1 is a flow diagram schematically illustrating a method 100 for predicting and managing menstrual disorders in accordance with one aspect of the present invention.
  • the method 100 for predicting and managing menstrual disorders utilizes various condition information as will be described later.
  • the method for predicting and managing menstrual disorders predicts and evaluates menstrual disorders by identifying the patient's symptoms and lifestyle, examining past medical history, and referring to medications taken. .
  • a physical examination and a pelvic examination may be performed to remove factors affecting menstrual disorders.
  • hypothyroidism hypothyroidism
  • tumors in the breast, brain, or ovary can be found, and through blood tests, medical conditions such as hypoglycemia, hypothyroidism, or other hormonal problems that cause symptoms It is also necessary to examine whether this is a factor.
  • menstrual disorder refers to premenstrual syndrome, menstrual irregularity, and menstrual dysphoric disorder (PMDD), which are predicted or evaluated from condition information measured through a digital smart device and symptom records input by the patient, input by the patient. It is a broad concept that includes dysmenorrhea predicted or evaluated from a symptom record, and menorrhagia predicted or evaluated from the amount of menstruation input through the IoT weighing scale.
  • PMDD menstrual dysphoric disorder
  • the state information receiving unit of the digital therapeutic agent for predicting and managing menstrual disorders receives the collected state information (S110).
  • the state information to be measured and collected may include information on the patient's physical state and psychological state.
  • information about physical condition may include the patient's body mass index (BMI), vital signs including blood pressure, pulse, and electrocardiogram, menstrual data, menstrual flow, premenstrual syndrome (PMS) symptoms , ovulation day, eating behavior, and water intake
  • information on the psychological state may include the patient's depression, anxiety, and stress levels.
  • the menstrual disorder prediction and evaluation unit included in the digital therapeutic agent for predicting and managing menstrual disorders collects and analyzes the received condition information, and determines whether the patient has menstrual disorders or not and The severity of menstrual disorders is predicted or evaluated (S120).
  • menstrual irregularity can be evaluated based on biosignal information obtained using a digital smart device such as a smart watch and information directly entered by a patient, and dysmenorrhea based on information directly entered by the patient on the digital smart device. can be evaluated.
  • a digital smart device such as a smart watch and information directly entered by a patient
  • dysmenorrhea based on information directly entered by the patient on the digital smart device.
  • by measuring menstrual blood through an IoT weighing scale it is possible to evaluate whether or not there is excessive menstruation, and it is possible to predict and evaluate menstrual disorders based on biosignal information obtained using a digital smart device and information directly entered by the patient.
  • menstrual disorder diagnosis technique will be additionally described in the relevant part of the present specification, and therefore, repetitive descriptions are omitted for simplicity of the specification.
  • the patient is treated differently according to severity. That is, it is determined whether the severity exceeds a predetermined level (S130), and if the severity does not exceed a predetermined level, the menstrual disorder management unit of the digital therapeutic agent for predicting and managing menstrual disorders can practice menstrual disorder patients without visiting a medical institution.
  • a possible self-therapy is created and provided (S140).
  • the menstrual disorder prediction and evaluation unit additionally predicts or evaluates the menstrual disorder of the patient visiting the medical institution, and the menstrual disorder management unit provides medical medication guidance or surgical treatment in addition to self-therapy. Surgery may be prescribed (S150).
  • menstrual disorders cannot be cured with one or two treatments, it is necessary to improve eating habits, supplement vitamins and minerals such as calcium and magnesium, and exercise prescriptions such as regular exercise to manage menstrual disorders. Yes (see reference [3]).
  • exercise prescriptions such as regular exercise to manage menstrual disorders.
  • the symptoms are severe, psychiatric counseling is required. Therefore, as a therapy for menstrual disorders, all techniques known to be able to treat menstrual disorders can be used.
  • cognitive-behavioral therapy can be performed using a smartphone application and a smart watch, exercise prescription and dietary therapy can be guided, and a patient can write a symptom diary using a smartphone application.
  • the patient's medication may be managed by utilizing a medication guidance application installed in the smartphone.
  • the method 100 for predicting and managing menstrual disorders does not perform a one-time diagnosis and prescription, but indicates the degree to which a patient practices self-therapy and medication guidance recorded by a digital smart device.
  • the practice rate and the degree of improvement of menstrual disorders are continuously monitored (S160).
  • the menstrual disorder management unit can effectively control and manage menstrual disorders by modifying and providing customized therapy for each patient (S170).
  • FIG. 2 is a diagram briefly illustrating the operation process of the method for predicting and managing menstrual disorders of FIG. 1 .
  • condition information measured from a patient is transmitted to a digital therapeutic agent for predicting and managing menstrual disorders
  • the digital therapeutic agent determines whether or not menstrual disorders exist and determines menstrual disorders based on the received condition information and the information input by the patient. Assess severity. The evaluated results are used to create patient-specific treatment.
  • digital therapeutics for predicting and managing menstrual disorders can improve the performance of predicting and managing menstrual disorders by consulting professional personnel such as doctors. As mentioned above, the physician can continuously monitor the patient's condition.
  • the customized prescription for each patient created in this way may be referred to as a 'digital therapeutic agent' in this specification.
  • FIG. 3 is a diagram illustrating differently the operation process of the method for predicting and managing menstrual disorders of FIG. 2 .
  • state information measured through a wearable device such as a smart watch worn by a patient and an IoT weighing scale may be transmitted to a mobile application.
  • Mobile applications can store data and keep data safe by linking with cloud services.
  • This status information is used to evaluate and manage menstrual disorders, and in this process, expert medical staff can provide advice (see reference [4]).
  • a prediction and evaluation algorithm through machine learning may be used to evaluate menstrual disorders.
  • Machine learning means optimizing parameters through given data or experience in a mathematical model composed of multiple parameters. To this end, machine learning first classifies a large number of given data into similar ones in the absence of any prior information, and then extracts cluster features using an algorithm. Then, the algorithm structure built in the previous step is piled up to create a more complex hierarchical structure, and by repeating this process thousands of times, it finds the most optimized algorithm system by itself.
  • machine learning techniques are combined with big data to broaden the scope of application, and efforts are continuously made to increase the probability and reduce the error for prediction success.
  • machine learning can be divided into supervised machine learning, unsupervised machine learning, and reinforcement learning. It is a learning method to find an output suitable for a given input using data with values and corresponding output values.
  • unsupervised machine learning is a learning method that finds the regularity of inputs using training data with only input values
  • reinforcement type machine learning is an optimal action rather than an output (correct action) for a given input (a given state). Indicates how to choose
  • 4A and 4B are diagrams illustrating a process in which data input by a patient to an application is used for predicting and managing menstrual disorders.
  • My Page which collectively displays patient information
  • My Page may include a history of information directly input by a patient, such as the date of ovulation or menstruation, and such history may be managed in the form of a diary.
  • health data provided to patients or recommended therapies may be displayed.
  • Patients can record the symptoms of menstrual disorders they experience and record the amount of food or fluid they have consumed on My Page.
  • psychological state information such as depression and nervousness can be recorded.
  • the status information entered by the user can be used for self-diagnosis of menstrual disorders through the self-diagnosis function provided by the application, or can be delivered to a digital treatment for predicting and managing menstrual disorders and used to create more precise evaluations and prescriptions. .
  • the step of predicting or evaluating whether the patient has menstrual disorder and the severity of menstrual disorder stores accumulated data of the patient's condition information and obtains real-time condition information about the patient. and predicting or evaluating the patient's menstrual disorder and its severity based on the accumulated data and real-time status information. That is, the patient's dysmenorrhea is predicted and evaluated in consideration of both the accumulated patient's state history information and the patient's real-time state information.
  • the condition information used to predict whether a patient has menstrual disorders and the severity of menstrual disorders is the patient's weight, body mass index (BMI), blood pressure (Reference [5] ]), body temperature, electrocardiogram (ECG/EKG), electromyogram (EMG), photoplethysmogram (PPG), heart rate (HR), heart rate variability (HRV), respiratory rate (RR), and skin perfusion, etc.
  • BMI body mass index
  • EMG electromyogram
  • PPG photoplethysmogram
  • HR heart rate variability
  • RR respiratory rate
  • skin perfusion etc.
  • this information can be used to predict the patient's ovulation period and menstrual cycle.
  • ovulation For example, through a study conducted on 237 Swiss women who wanted to conceive, they predicted the timing of ovulation by wearing a bio-signal measuring bracelet and measured the bio-signals (body temperature, pulse, respiratory rate, etc.) that change during ovulation through the bracelet. It can be received and analyzed through a measurement algorithm, and the time of ovulation can be identified from the analysis result. To this end, it can be determined that ovulation is closer as the increase in the resting pulse rate, the increase in body temperature, and the increase in pulse rate variability are greater.
  • menstrual cycle it is possible to accurately predict the menstrual cycle by measuring biosignals generated in the body in real time and using an algorithm for changes in biosignals appearing in the menstrual cycle. If the deviation of the predicted menstrual cycle is large, menstrual irregularity may be predicted. For example, as a result of a study targeting healthy women, ovulation can be predicted when there is a difference between the mean pulse rate and the RR interval as a result of measuring changes in the electrocardiogram during the luteal phase for 24 hours.
  • FIG. 5 is a diagram explaining a patient's biometric information and input data, and a process used for predicting and managing menstrual disorders.
  • the following may be used to obtain user's physical state information and psychological state information.
  • MDQ Menstrual Distress Questionnaire
  • VAS Visual Analog Scale
  • MMAS Multi-Attribute Quality-of-Life Scale
  • K-MDI Korean Mental Disorder Inventory
  • the dysmenorrhea questionnaire includes six categories of pain, poor concentration, behavioral change, autonomic nervous system response, water accumulation, and negative emotion, and each symptom ranges from 1 point of 'no symptoms at all' to 'very severe'. It is displayed on a 5-point scale up to 5 points.
  • State anxiety used in the state trait anxiety scale is to measure the temporary emotional state caused by tension, worry, fear, etc. in a special situation, and trait anxiety is a factor that determines individual differences in coping with external threats. Among them, state anxiety can change in degree over time, but trait anxiety shows a constant pattern throughout life.
  • the suicide ideation scale is a test to evaluate various self-destructive thoughts and desires, and the higher the score, the higher the risk of suicide.
  • a customized therapy for each patient is created based on the diagnosis result.
  • FIG. 6 Various treatment methods as introduced in FIG. 6 should be considered in order to provide customized therapy for each patient.
  • Examples of therapies that can be applied at this time include the following (see references [6], [7], [8], [9]).
  • the application, smart watch, and IoT weighing scale are interlocked to collect patient condition information and clinical information, and predictive and evaluation algorithms through machine learning Based on this, it is possible to self-diagnose the patient's menstrual disorders (irregular menstruation, dysmenorrhea, hypermenorrhea, premenstrual syndrome).
  • the method for predicting and managing menstrual disorders according to the present invention can provide cognitive behavioral therapy and medication guidance to patients using a mobile app and a smart watch based on the diagnosed results.
  • the following two therapies may be suggested depending on whether the menstrual cycle is regular.
  • NSAIDs are administered (menstrual pain control) every 8 hours from 2 to 3 days before menstruation begins, and Tranexamic acid is taken every 8 hours at the start of menstruation.
  • menstrual cycle If the menstrual cycle is irregular, predict the start of menstruation through menstrual pattern analysis and administer it, or take it when pain starts according to the menstrual cycle.
  • the treatment effect on the target disease can be confirmed by monitoring whether symptoms are relieved through a patient management web program.
  • Cognitive behavioral therapy for patients with menstrual disorder helps relieve symptoms of premenstrual syndrome, and exercise guidance, symptom diary writing, and dietary therapy through the application are expected to help improve menstrual irregularity, dysmenorrhea, and hypermenorrhea symptoms. .
  • Carbohydrate-rich food intake can increase the secretion of serotonin, thereby alleviating the symptoms of premenstrual syndrome.
  • behavioral therapy may include:
  • Reducing salt intake can reduce abdominal bloating, water retention, breast bloating and pain, and limiting caffeine intake can reduce premenstrual irritability and insomnia. Small, frequent meals help relieve digestive symptoms, and exercise is also effective.
  • FIG. 7 is a block diagram schematically illustrating an operating environment of a digital therapeutic agent 750 for predicting and managing menstrual disorders according to another aspect of the present invention.
  • a digital therapeutic agent 750 for predicting and managing menstrual disorders includes a state information receiver 760, a menstrual disorder prediction and evaluation unit 770, and a menstrual disorder management unit 780.
  • the term 'digital remedy' does not mean a simple chemical drug, but provides information stored in the cloud, evaluation results provided by specialized medical institutions, and customized therapy for each patient through an application installed on a smart device. It refers to a service or system that receives and controls menstrual disorders by practicing customized therapy for each patient according to the received message.
  • the patient can transmit various information used to evaluate menstrual disorders to the server through his or her own smart device, and can easily grasp the customized therapy provided by the server to control menstrual disorders.
  • a service or system that predicts and evaluates menstrual disorders, provides personalized therapy, and continuously monitors the patient's practice rate and severity level can be referred to as a digital therapeutic agent.
  • the digital smart device (710, 720, 730) collects state information on the patient's physical condition and psychological state related to the patient's menstrual disorder, and through the network (790), a digital therapeutic agent (750) for predicting and managing menstrual disorder ) is sent to
  • the digital smart device possessed by the patient may be implemented as a computer capable of accessing the digital therapeutic agent 750 for predicting and managing menstrual disorders through the network 790, for example, a laptop or a laptop equipped with a web browser. etc. may be included.
  • PCS Personal Communication System
  • GSM Global System for Mobile communications
  • PDC Personal Digital Cellular
  • PHS Personal Handyphone System
  • PDA Personal Digital Assistant
  • IMT International Mobile Telecommunication
  • CDMA Code Division Multiple Access
  • W-CDMA Wireless Broadband Internet
  • smartphone smart pad
  • All kinds of handheld-based wireless communication devices such as smartpads, tablet PCs, and the like can be used as digital smart devices.
  • the menstrual disorder prediction and evaluation unit 770 collects and analyzes the received condition information, and predicts or evaluates whether or not the patient has menstrual disorder and the severity of the menstrual disorder according to the analysis result. As described above, various state information of the user can be used in this process. In addition, the menstrual disorder prediction and evaluation unit 770 may improve prediction and diagnosis accuracy by utilizing machine learning techniques as described above.
  • the menstrual disorder management unit 780 When the menstrual disorder prediction and evaluation unit 770 predicts or evaluates that the patient has menstrual disorder, the menstrual disorder management unit 780 generates a customized therapy for each patient according to the severity of the menstrual disorder and digitally Provided to smart devices (710, 720, 730). At this time, the menstrual disorder management unit 780 provides self-therapy that the menstrual disorder patient can practice without visiting a medical institution when the severity of the menstrual disorder is relatively low, and the severity of the menstrual disorder is relatively high. Menstrual disorders of patients visiting a medical institution may be additionally predicted or evaluated, and medical medication guidance or surgical operation may be prescribed in addition to the self-therapy.
  • the menstrual disorder management unit 780 monitors the practice rate and the degree of improvement of menstrual disorder, which are recorded by the digital smart devices 710, 720, and 730, indicating the degree to which the patient practices self-therapy and medication guidance, for each patient. It is configured to modify the custom therapy.
  • the menstrual disorder management unit 780 refers not only to past history data of the patient but also to data after the customized therapy for each patient is provided in order to create a customized therapy for each patient. Accordingly, the degree of suitability of the therapy customized for each patient to the patient can be improved.
  • menstrual disorders it is possible to diagnose menstrual disorders by analyzing through an algorithm using vital signs obtained from women and evaluation measurement tools (survey, menstrual diary, etc.) For this, customized therapies for each patient, such as dietary therapy and behavioral therapy, can be suggested.
  • therapies for each patient such as dietary therapy and behavioral therapy, can be suggested.
  • it is possible to accurately predict menstrual disorders by using apps and digital smart devices.
  • the treatment effect according to the present invention is more expected.
  • a computer-readable recording medium may include all types of recording devices storing data that can be read by a computer system. Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage devices, and also those implemented in the form of carrier waves (for example, transmission through the Internet). include In addition, the computer-readable recording medium may store computer-readable codes that can be executed in a distributed manner by distributed computer systems connected through a network.
  • the present invention can be applied to the field of predicting and evaluating menstrual disorders and controlling menstrual disorders according to the results.

Landscapes

  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Public Health (AREA)
  • Medical Informatics (AREA)
  • Biomedical Technology (AREA)
  • General Health & Medical Sciences (AREA)
  • Cardiology (AREA)
  • Epidemiology (AREA)
  • Pathology (AREA)
  • Primary Health Care (AREA)
  • Physics & Mathematics (AREA)
  • Biophysics (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Molecular Biology (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • Veterinary Medicine (AREA)
  • Physiology (AREA)
  • Computing Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • General Business, Economics & Management (AREA)
  • Business, Economics & Management (AREA)
  • Chemical & Material Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Medicinal Chemistry (AREA)
  • Vascular Medicine (AREA)
  • Medical Treatment And Welfare Office Work (AREA)

Abstract

Un procédé de prédiction et de prise en charge d'un trouble menstruel ainsi qu'un agent thérapeutique numérique sont divulgués. Le procédé divulgué comprend les étapes consistant : à recevoir des informations d'état concernant un état physique et un état psychologique d'une patiente relatives à un trouble menstruel de la patiente, qui sont mesurées et collectées par un dispositif intelligent numérique; à collecter et à analyser, par une unité de prédiction et d'évaluation de trouble menstruel, les informations d'état reçues, et à prédire ou à évaluer, en fonction d'un résultat de l'analyse, si la patiente présente le trouble menstruel et la gravité du trouble menstruel; et lorsqu'il est prédit ou évalué que la patiente présente le trouble menstruel, à générer, par une unité de génération de traitement personnalisé, le traitement personnalisé spécifique à la patiente pour le trouble menstruel en fonction de la gravité du trouble menstruel, et à fournir le traitement personnalisé au dispositif intelligent numérique, de façon à prendre en charge le trouble menstruel. Selon le procédé divulgué, le trouble menstruel peut être pris en charge par l'analyse des symptômes du trouble menstruel, la prédiction et l'évaluation du trouble menstruel, et la fourniture d'un traitement personnalisé à l'aide d'une application de téléphone intelligent et d'un dispositif intelligent.
PCT/KR2021/019041 2021-09-17 2021-12-15 Procédé de prédiction et de prise en charge de trouble menstruel, et agent thérapeutique numérique Ceased WO2023042971A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
KR20210124610 2021-09-17
KR10-2021-0124610 2021-09-17
KR1020210164488A KR20230041555A (ko) 2021-09-17 2021-11-25 월경장애를 예측하고 관리하기 위한 방법 및 디지털 치료제
KR10-2021-0164488 2021-11-25

Publications (1)

Publication Number Publication Date
WO2023042971A1 true WO2023042971A1 (fr) 2023-03-23

Family

ID=85603012

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/KR2021/019041 Ceased WO2023042971A1 (fr) 2021-09-17 2021-12-15 Procédé de prédiction et de prise en charge de trouble menstruel, et agent thérapeutique numérique

Country Status (1)

Country Link
WO (1) WO2023042971A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024213900A1 (fr) * 2023-04-14 2024-10-17 Samphire Neuroscience Ltd Systèmes et procédés de neurostimulation pour des applications dans des états de santé des femmes

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20160063341A (ko) * 2013-10-02 2016-06-03 액세스 비지니스 그룹 인터내셔날 엘엘씨 다이어트 준수 시스템
JP2016218954A (ja) * 2015-05-26 2016-12-22 株式会社野村総合研究所 ヘルスケアシステム
WO2020198065A1 (fr) * 2019-03-22 2020-10-01 Cognoa, Inc. Procédés et dispositifs de thérapie numérique personnalisée
KR20200128555A (ko) * 2018-03-04 2020-11-13 아킬리 인터랙티브 랩스 인크. 면역-매개 및 신경-퇴행성 장애를 표적으로 하는 인지 스크린, 모니터 및 인지 치료

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20160063341A (ko) * 2013-10-02 2016-06-03 액세스 비지니스 그룹 인터내셔날 엘엘씨 다이어트 준수 시스템
JP2016218954A (ja) * 2015-05-26 2016-12-22 株式会社野村総合研究所 ヘルスケアシステム
KR20200128555A (ko) * 2018-03-04 2020-11-13 아킬리 인터랙티브 랩스 인크. 면역-매개 및 신경-퇴행성 장애를 표적으로 하는 인지 스크린, 모니터 및 인지 치료
WO2020198065A1 (fr) * 2019-03-22 2020-10-01 Cognoa, Inc. Procédés et dispositifs de thérapie numérique personnalisée

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
CHAI NING, WU YING, ZHANG MIAO, WU WEN-BIN, ZHANG HUI, KONG FENG-WEI, ZHANG YING: "Remote intervention using smartphone for rural women suffering from premenstrual syndrome : A propensity score matched analysis", MEDICINE, WILLIAMS AND WILKINS, BALTIMORE., US, vol. 97, no. 29, 1 July 2018 (2018-07-01), US , pages e11629, XP093048815, ISSN: 0025-7974, DOI: 10.1097/MD.0000000000011629 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024213900A1 (fr) * 2023-04-14 2024-10-17 Samphire Neuroscience Ltd Systèmes et procédés de neurostimulation pour des applications dans des états de santé des femmes

Similar Documents

Publication Publication Date Title
US11497438B2 (en) Method and apparatus for the measurement of autonomic function for the diagnosis and validation of patient treatments and outcomes
Hong et al. Effectiveness of tele-monitoring by patient severity and intervention type in chronic obstructive pulmonary disease patients: a systematic review and meta-analysis
Gattuso et al. Coping with gastrointestinal endoscopy: self-efficacy enhancement and coping style.
ES2329650T3 (es) Procedimiento para clasificar desequilibrios fisiologicos cerebrales usando eeg cuantitativo.
Tomkins-Lane et al. Predictors of walking performance and walking capacity in people with lumbar spinal stenosis, low back pain, and asymptomatic controls
Auerbach et al. Relation of diabetic patients' health-related control appraisals and physician–patient interpersonal impacts to patients' metabolic control and satisfaction with treatment
Thenganatt et al. Psychogenic (functional) movement disorders
US20060183980A1 (en) Mental and physical health status monitoring, analyze and automatic follow up methods and its application on clothing
JP2005500869A (ja) 広域ネットワークによる生理学的データの実時間モニタリング、判断、解析、検索および記憶を行うためのシステムおよび方法
Prkachin et al. Real-time assessment of pain behavior during clinical assessment of low back pain patients
RU2719954C2 (ru) Способ и система для улучшения физиологической реакции
Kirschen et al. Neuroprognostication after pediatric cardiac arrest
Solomon et al. Quantifying energy and fatigue: classification and assessment of energy and fatigue using subjective, objective, and mixed methods towards health and quality of life
Ramsey et al. Physical activity intensity of patient’s with traumatic brain injury during inpatient rehabilitation
WO2023042971A1 (fr) Procédé de prédiction et de prise en charge de trouble menstruel, et agent thérapeutique numérique
Spehar et al. Prolonged antepartum hospitalization: no time for rest
Farris et al. Behavioral Exposure for Interoceptive Tolerance (BE-FIT): A stage II randomized clinical trial protocol
Kumaravel Smart healthcare with sensors and wireless body area networking
EP4573560A1 (fr) Système numérique pour l'admission et/ou le traitement par ordre de priorité d'un patient
Chappel et al. The accumulation of, and associations between, nurses’ activity levels within their shift in the emergency department
KR20230041555A (ko) 월경장애를 예측하고 관리하기 위한 방법 및 디지털 치료제
Martelli et al. Assessing the veracity of pain complaints and associated disability
US20200161001A1 (en) Personal Data Analytics System
McKinley et al. Long-Term Outcomes: Sleep in Survivors of Critical Illness
Levet-Bourtayre Can a Smart Ring Improve Sleep? Investigating the Effect of a 4-Week Oura Ring Trial on Sleep Duration and Quality

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21957637

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21957637

Country of ref document: EP

Kind code of ref document: A1