KR20210090844A - System and Method for Predicting the Glycated Hemoglobin Using Real Life Data - Google Patents
System and Method for Predicting the Glycated Hemoglobin Using Real Life Data Download PDFInfo
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Abstract
Description
본 발명은 당뇨병 환자가 실제 생활을 하면서 당뇨병 자가관리 장치를 통해 누적되는 혈당측정 값, 식사 및 운동과 관련된 값, 자가 관리 시스템을 통해 발송된 메시지와 교육 자료에 대한 구독 여부 등의 데이터를 활용하여 3개월 후의 당화혈색소 값을 예측하는 시스템 및 방법에 관한 것이다. The present invention utilizes data such as blood glucose measurement values accumulated through a diabetes self-management device, values related to meals and exercise, and whether or not to subscribe to messages and educational materials sent through the self-management system while the diabetic patient lives in real life. The present invention relates to a system and method for predicting the glycated hemoglobin value after 3 months.
당뇨병은 인슐린의 분비량이 부족하거나 정상적인 기능이 이루어지지 않는 등의 대사질환의 일종으로, 완치가불가능한 만성 질환에 해당한다.Diabetes mellitus is a type of metabolic disease in which insulin secretion is insufficient or normal function is not achieved, and corresponds to a chronic disease that cannot be cured.
당뇨병 치료를 위해서는 지속적인 자가 관리가 매우 중요하다. 이를 위해, 당뇨병 환자가 병원 등에서 제공한 수첩에 자신의 혈당 수치를 꾸준히 기록하더라도, 단순히 기록된 자신의 혈당 만이 파악되어, 효과적으로 당뇨병을 관리하기 힘들다.Continuous self-management is very important for diabetes treatment. To this end, even if the diabetic patient continuously records his or her blood sugar level in a notebook provided by a hospital, etc., only the recorded blood sugar is simply grasped, making it difficult to effectively manage diabetes.
또한 당뇨병 환자의 의료 지식 결핍, 올바르지 못한 생활습관, 갑작스런 환경 변화 등 다양한 원인으로 인해 갑자기 몸 안에서 인슐린이 부족하게 되면 급성 합병증이 생길 수 있다.In addition, acute complications can occur when there is a sudden lack of insulin in the body due to various causes such as a lack of medical knowledge in diabetic patients, incorrect lifestyle, and sudden environmental changes.
급성 합병증은 당뇨병성 케톤산증(diabetic ketoacidosis)과 고혈당성 고삼투압 증후군(hyperglycemic hyperosmolar syndrome)으로 즉각적인 치료가 필요하며, 적절히 치료하지 않을 경우 치명적이다. 심한 경우 의식을 잃을 수 있고 사망에 이를 수도 있다.Acute complications are diabetic ketoacidosis and hyperglycemic hyperosmolar syndrome, which require immediate treatment and are fatal if not properly treated. In severe cases, it can cause loss of consciousness and even death.
그러나, 아직 한국에서는 만성질환 교육에 대한 의료보험급여가 지원되지 않고 있는 실정에 비추어, 당뇨병 환자들에게 체계적인 당뇨병 교육 및 개개인 별로 개별화된 당뇨병 관리를 시행하는 것은 매우 어려운 상황이다.However, in view of the fact that medical insurance benefits for chronic disease education are not yet supported in Korea, it is very difficult to implement systematic diabetes education and individualized diabetes management for diabetic patients.
한편, IT기술이 발달함에 따라 생체신호 및 건강정보를 측정할 수 있는 유무선 첨단 디지털 기술을 활용한 건강관리 서비스가 다양해지고, 스마트폰이 보편화되면서 많은 만성질환 환자들이 건강관리 어플리케이션을 활용하여 일상생활에서의 자신의 건강정보를 누적하고 모니터링이 가능하게 되었다. On the other hand, as IT technology develops, health management services using wired and wireless advanced digital technology that can measure biosignals and health information are diversified, and as smartphones become common, many chronic disease patients use health management applications in their daily lives. It became possible to accumulate and monitor one's own health information in
당뇨병 환자들이 많이 사용하는 자가관리 어플리케이션은 혈당, 체중, 식사와 운동 등의 실생활에 대한 모니터링을 위한 자료 누적이 주된 기능이다. 환자들이 본인의 실생활 상태를 누적하는 이유는 적절한 혈당관리를 하는 것에 목적으로 두고 있지만, 현재 주로 활용되는 어플리케이션이나 관련 시스템에서는 환자 개인의 생활이 다음 병원 방문 시 측정하게 되는 당화혈색소에 어떻게 영향을 미칠지 인지할 수 없다. 따라서 환자의 누적된 실생활 데이터를 활용하여 향후 당화혈색소를 예측하는 발명을 통해 환자가 올바른 생활습관을 실천하도록 유지하고, 궁극적으로는 적절한 혈당관리를 통한 합병증 예방을 가능케 하는 것이 요구된다. The main function of self-management applications used by diabetic patients is to accumulate data for monitoring real life such as blood sugar, weight, meal and exercise. The reason patients accumulate their real life status is for the purpose of proper blood sugar management, but in the currently used applications or related systems, how the patient's personal life will affect the glycated hemoglobin measured at the next hospital visit can't perceive Therefore, it is required to maintain the patient to practice the right lifestyle through the invention of predicting the glycated hemoglobin in the future by using the patient's accumulated real life data, and ultimately to enable the prevention of complications through proper blood sugar management.
본 발명은 상기와 같은 종래 문제점을 해결하기 위해 당뇨병 환자가 실제 생활을 하면서 당뇨병 자가관리 어플리케어션이나 장치를 통해 누적되는 혈당 측정 값, 식사 및 운동과 관련된 값, 자가 관리 시스템을 통해 발송된 메시지와 교육 자료에 대한 구독 여부 등의 데이터를 활용하여 3개월 후의 당화혈색소 값을 예측함에 그 목적이 있다.In order to solve the conventional problems as described above, the present invention provides a blood glucose measurement value accumulated through a diabetes self-management application or device while a diabetic patient lives in real life, a value related to meals and exercise, and a message sent through a self-management system. The purpose of this is to predict the glycated hemoglobin value after 3 months by using data such as whether or not to subscribe to educational materials.
또한, 본 발명에서는 당뇨병 환자들 개개인 별 상황을 고려하여, 각 개인에게 최적화된 당뇨병 및 합병증 관리를 제공하며, 이에 더 나아가 개개인 별로 개인행동을 개선하도록 유도함으로써 개인별로 체계적으로 당뇨병 교육을 수행하고, 최적화된 당뇨병 관리를 시행하고자 함에 그 목적이 있다.In addition, in the present invention, in consideration of the individual circumstances of each diabetic patient, the optimized management of diabetes and complications is provided for each individual, and further, by inducing improvement of individual behavior for each individual, the diabetes education is systematically performed for each individual, The purpose is to implement optimized diabetes management.
상기와 같은 목적을 수행하기 위해 본 발명은 소정의 정보 내용이 구비된 활용 데이터, 환자별 스마트폰 및 별도의 어플리케이션으로 구현되되, 병원 혹은 별도의 장소에 구비된 중앙서버에 구비된 분석데이터부에 의해 해당 자료들을 분석하고, 그 분석 결과를 도출하여 최적의 솔루션을 환자에게 제공한다.In order to achieve the above object, the present invention is implemented as utilization data with predetermined information content, a smartphone for each patient, and a separate application, in an analysis data unit provided in a central server provided in a hospital or a separate place. By analyzing the data, the analysis result is derived and the optimal solution is provided to the patient.
상기와 같은 구성으로 이루어진 본 발명에 의한다면, 사용자는 스마트폰을 통하여 혈당에 관한 정보를 체계적, 직관적 또는 효과적으로 관리할수 있다. 또한, 사용자의 생활 습관, 사용자가 처한 환경이 당뇨병이나 당뇨 합병증에 미치는 영향을 개개인의 특성에 맞춰 당뇨병 관리를 시행할 수 있다.According to the present invention configured as described above, a user can systematically, intuitively or effectively manage blood sugar information through a smart phone. In addition, diabetes management can be performed according to the individual characteristics of the user's lifestyle and the influence of the user's environment on diabetes or diabetic complications.
또한, 본 발명을 의사도 이용할 수 있어, 환자에 대한 즉각적이고 효율적인 환자 관리 및 당뇨병에 관한 처치를할 수 있어, 환자의 질병을 더욱더 효과적으로 치유할 수 있다. 또한, 본 발명에 의한 경우, 환자의 스마트폰과 병원의 소정의 장치가 통신망을 통하여 연결되어 당뇨병에 관한 유비쿼터스 의료 환경을 구현할 수 있다.In addition, the present invention can also be used by a physician, so that the patient can be treated promptly and efficiently for patient management and diabetes treatment, so that the patient's disease can be cured more effectively. In addition, according to the present invention, the patient's smartphone and a predetermined device in the hospital are connected through a communication network to implement a ubiquitous medical environment related to diabetes.
도 1은 본 발명의 개략적인 구성도이다.1 is a schematic configuration diagram of the present invention.
이하 첨부된 도면을 참조로 본 발명인 실생활 데이터를 활용한 당화혈색소 예측 시스템 및 방법의 바람직한 실시 예를 설명한다.Hereinafter, a preferred embodiment of a system and method for predicting glycated hemoglobin using real life data of the present inventors will be described with reference to the accompanying drawings.
도 1은 본 발명의 개략적이 구성도로, 도시된 바와 같이 본 발명의 일 구성요소인 활용 데이터에 대해 설명하면 다음과 같다.1 is a schematic configuration diagram of the present invention, and as shown, utilization data, which is a component of the present invention, will be described as follows.
활용 데이터는 당뇨병 자가관리 시스템을 통해 누적되는 데이터와 병원 방문시 측정하는 당화혈색소이다.Utilization data is data accumulated through the diabetes self-management system and glycated hemoglobin measured during hospital visits.
이때, 알고리즘 개방을 위한 독립변수로는 첫 번째 병원 방문과 그 다음 병원 방문 일정 사이에 당뇨병 자가관리 어플리케이션을 통해 누적된 환자의 실생활 데이터 - 공복시 혈당, 입력된 자가 평가 식사량, 활동량에 대한 소모 열량, 시스템 발송 자가 관리를 위한 메시지 열람 여부, 환자 요약 리포트 열람 여부 및 헬스매거진 열람 여부 등 시스템 활용 정도에 대한 데이터이다.At this time, the independent variables for the algorithm open are the patient's real life data accumulated through the diabetes self-management application between the first hospital visit and the next hospital visit schedule - fasting blood sugar, input self-evaluation meal amount, calories consumed for activity amount, It is data on the degree of system utilization, such as whether to read messages for self-management, whether to read patient summary reports, and whether to read health magazines.
그 다음으로 알고리즘 개발을 위한 종속변수로는 실생활 데이터가 누적된 기간 후에 병원 방문 시 측정하는 당화혈색소(HbA1c1)이다.The next dependent variable for algorithm development is glycated hemoglobin (HbA1c1), which is measured at hospital visits after a period of accumulation of real life data.
그 다음으로 분석데이터에 대해 설명하면 다음과 같다.Next, the analysis data will be described as follows.
분석 대상자 수는 병원에서 당화혈색소를 측정한 일정 전에 자가관리 어플리케이션의 혈당, 식사, 신체활동, 시스템 발송 메시지 열람 등에 대한 기록이 모두 누적되어 있는 환자 53명 자료 활용하였다.For the number of subjects to be analyzed, data from 53 patients who had accumulated records of blood glucose, meals, physical activity, and viewing of messages sent from the system were used in the self-management application before the schedule for measuring glycated hemoglobin at the hospital.
독립변수로는 실생활 데이터 각각의 누적 기간 동안의 평균 값, 환자의 성별과 연령, - age(연속변수), gender(1=남성, 0=여성), 식전혈당으로 G_value 연속변수(단위: mg/dL), 실제 식사량 D_feel (1=식사지침 만큼, 0=식사지침보다 적게 또는 많이), 활동량 소모칼로리 A_calorie 연속변수, 메시지 열람여부 m_read(1=읽음, 0=안 읽음), 레포트 열람 여부 r_read(1=읽음, 0=안 읽음), 헬스 매거진 열람 여부 HM_read(1=읽음, 0=안 읽음)에 대한 각각의 평균 값으로 분석에 활용하였다.As independent variables, the average value for each cumulative period of real life data, the patient's gender and age, - age (continuous variable), gender (1=male, 0=female), G_value continuous variable (unit: mg/ dL), actual meal amount D_feel (1 = as much as the meal guideline, 0 = less or more than the meal guideline), activity amount calorie consumption A_calorie continuous variable, message reading status m_read(1=reading, 0=not reading), report reading status r_read( 1 = read, 0 = not read), health magazine reading status HM_read (1 = read, 0 = not read) were used for analysis as the average value of each.
종속변수로는 실생활 데이터 누적 기간 후의 병원 방문하여 측정된 당화혈색소 값 (단위: %)이다.The dependent variable is the glycated hemoglobin value (unit: %) measured by visiting a hospital after the period of accumulation of real life data.
분석 방법 및 결과에 대해 설명하면 다음과 같다.The analysis method and results will be described as follows.
분석 방법은 병원에서 측정한 당화혈색소와 각 환자들이 스마트 폰 어플리케이션으로 수집된 App data들은 시간의 흐름에 따른 선후관계(여러 시점의 App data들이 다음 방문 시의 HbA1c를 예측)가 존재하는 data들이었다. 그러므로 동시대적인 data를 다루는 GLM에 비해 시계열의 data의 특성을 반영하면서 nonlinear 모델인 Generalized autoregressive moving average models (GARMA)을 예측 모델로 적용하여 분석하였다.As for the analysis method, the glycated hemoglobin measured at the hospital and the App data collected by each patient through a smartphone application were data in which there was a chronological relationship (App data at multiple points in time predict HbA1c at the next visit). . Therefore, compared to GLM that deals with contemporary data, generalized autoregressive moving average models (GARMA), a nonlinear model, were applied and analyzed as a predictive model while reflecting the characteristics of time series data.
분석 결과에 대해 살펴보면, 아래와 같다.The analysis results are as follows.
모델: y = a + b*x + p1*zlag1(y-(a+b*x)) + p2*zlag2(y-(a+b*x)) Model: y = a + b*x + p1*zlag1(y-(a+b*x)) + p2*zlag2(y-(a+b*x))
통계 분석 결과: GARMA model로 추정한 HbA1c 예측 모델 설명력은 81%로 높게 나타났다.Statistical analysis result: The explanatory power of the HbA1c prediction model estimated by the GARMA model was high at 81%.
당화혈색소(Hba1c) = 2.377 + 0.018*나이 + 0.803*성별 + 0.026*식전혈당 - 0.005*실제 식사량 - 0.001*활동량 소모칼로리 + 0.469*메세지 열람여부 + 0.304* 레포트 열람여부 + 0.196 * 헬스 매거진 열람여부 + 0.297* zlag1(hba1c - (2.377 + 0.018*나이 + 0.803*성별 + 0.026*식전혈당 - 0.005*실제 식사량 - 0.001*활동량 소모칼로리 + 0.469*메세지 열람여부 + 0.304* 레포트 열람여부 + 0.196 * 헬스 매거진 열람여부)) + 0.232 * zlag2(hba1c - (2.377 + 0.018*나이 + 0.803*성별 + 0.026*식전혈당 - 0.005*실제 식사량 - 0.001*활동량 소모칼로리 + 0.469*메세지 열람여부 + 0.304* 레포트 열람여부 + 0.196 * 헬스 매거진 열람여부)) Glycated hemoglobin (Hba1c) = 2.377 + 0.018*age + 0.803*gender + 0.026*pre-meal blood sugar - 0.005*actual meal amount - 0.001*active calorie consumption + 0.469* message reading + 0.304* report reading + 0.196 * health magazine reading + 0.297* zlag1(hba1c - (2.377 + 0.018*age + 0.803*gender + 0.026*pre-meal blood sugar - 0.005*actual meal amount - 0.001*active calorie consumption + 0.469* message reading + 0.304* report reading + 0.196 * health magazine Reading status)) + 0.232 * zlag2(hba1c - (2.377 + 0.018*age + 0.803*gender + 0.026*pre-meal blood sugar - 0.005*actual meal amount - 0.001*active calorie consumption + 0.469* message reading status + 0.304* report reading status + 0.196 * Health magazine reading))
Claims (2)
상기 스마트폰과 실시간으로 연동되는 중앙서버;를 포함하고,
실생활 데이터 누적 기간 동안 상기 어플리케이션을 통해 사용자에 의해 입력된 독립변수가 상기 중앙서버로 전송되고, 실생활 데이터 누적 기간 종료 후 측정된 사용자의 종속변수가 상기 중앙서버에 전송되는 것을 특징으로 하되,
상기 독립변수와 종속변수를 기초로 하기의 수학식에 의해 사용자의 당화혈색소를 예측하는, 실생활 데이터를 활용한 당화혈색소 예측 시스템 및 방법.
당화혈색소(Hba1c) = 2.377 + 0.018*나이 + 0.803*성별 + 0.026*식전혈당 - 0.005*실제 식사량 - 0.001*활동량 소모칼로리 + 0.469*메세지 열람여부 + 0.304* 레포트 열람여부 + 0.196 * 헬스 매거진 열람여부 + 0.297* zlag1(hba1c - (2.377 + 0.018*나이 + 0.803*성별 + 0.026*식전혈당 - 0.005*실제 식사량 - 0.001*활동량 소모칼로리 + 0.469*메세지 열람여부 + 0.304* 레포트 열람여부 + 0.196 * 헬스 매거진 열람여부)) + 0.232 * zlag2(hba1c - (2.377 + 0.018*나이 + 0.803*성별 + 0.026*식전혈당 - 0.005*실제 식사량 - 0.001*활동량 소모칼로리 + 0.469*메세지 열람여부 + 0.304* 레포트 열람여부 + 0.196 * 헬스 매거진 열람여부)) Smartphone with a separate application installed;
Including; a central server interworking with the smartphone in real time;
The independent variable input by the user through the application during the real life data accumulation period is transmitted to the central server, and the dependent variable of the user measured after the real life data accumulation period ends is transmitted to the central server,
A system and method for predicting glycated hemoglobin using real life data, which predicts a user's glycated hemoglobin by the following equation based on the independent and dependent variables.
Glycated hemoglobin (Hba1c) = 2.377 + 0.018*age + 0.803*gender + 0.026*pre-meal blood sugar - 0.005*actual meal amount - 0.001*active calorie consumption + 0.469* message reading + 0.304* report reading + 0.196 * health magazine reading + 0.297* zlag1(hba1c - (2.377 + 0.018*age + 0.803*gender + 0.026*pre-meal blood sugar - 0.005*actual meal amount - 0.001*active calorie consumption + 0.469* message reading + 0.304* report reading + 0.196 * health magazine Reading status)) + 0.232 * zlag2(hba1c - (2.377 + 0.018*age + 0.803*gender + 0.026*pre-meal blood sugar - 0.005*actual meal amount - 0.001*active calorie consumption + 0.469* message reading status + 0.304* report reading status + 0.196 * Health magazine reading))
상기 성별은 남성의 경우 1, 여성의 경우 0으로 지정되고,
실제 식사량은 사용자에 대한 식사지침양을 준수한 경우 1, 해당 식사지침양보다 적거나 많은 경우 0,
사용자의 스마트폰으로 전송한 메세지를 읽은 경우 1, 읽지 않은 경우 0,
사용자의 스마트폰으로 전송한 헬스 매거진에 대해 읽은 경우 1, 읽지 않은 경우 0으로 되는 것을 특징으로 하는, 실생활 데이터를 활용한 당화혈색소 예측 시스템 및 방법.
The method according to claim 1,
The gender is designated as 1 for men and 0 for women,
The actual amount of food is 1 when the dietary guidelines for users are followed, 0 when it is less or more than the dietary guidelines.
1 if the message sent to the user's smartphone was read, 0 if not read,
A glycated hemoglobin prediction system and method using real life data, characterized in that 1 if read and 0 if not read about the health magazine transmitted to the user's smartphone.
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