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KR-20260063782-A - Method and Apparatus for Predicting and Managing Diabetes Based on Personal Health Records Using Artificial Intelligence

KR20260063782AKR 20260063782 AKR20260063782 AKR 20260063782AKR-20260063782-A

Abstract

A method and apparatus for predicting and managing diabetes based on personal health records utilizing artificial intelligence are disclosed, which simulate a customized personal profile based on personal health records to predict the likelihood of developing diabetes several years in the future and provide a standard for personal health management to suppress the onset of diabetes. The method for predicting and managing diabetes includes the step of providing a dashboard controlled by a processor of a computing device or a user terminal and comprising a data collection area, a health status integrated analysis area, and a health management service area; the step of supporting operations of collecting basic member information, collecting Bluetooth-linked customer health information, collecting blood glucose information, collecting survey health information, and collecting general check-up information included in the data collection area through a first user interface connected to the processor; the step of supporting operations of monitoring behavioral patterns, analyzing blood glucose, analyzing surveys, and conducting comprehensive analysis included in the health status integrated analysis area through a second user interface connected to the processor; and the step of supporting operations of a health management service included in the health management service area through a third user interface connected to the processor.

Inventors

  • 이대호

Assignees

  • 주식회사 원소프트다임

Dates

Publication Date
20260507
Application Date
20241031

Claims (12)

  1. A method for predicting and managing diabetes based on personal health records, performed by at least one computing device or user terminal, A step of providing a dashboard controlled by a processor of the computing device or the user terminal, and comprising a data collection area, a health status integrated analysis area, and a health management service area; A step of supporting the operation of collecting basic member information, collecting Bluetooth-linked customer health information, collecting blood glucose information, collecting survey health information, and collecting general checkup information included in the data collection area through a first user interface connected to the processor; A step of supporting the operation of behavioral pattern monitoring, blood glucose analysis, survey analysis, and comprehensive analysis included in the health status integrated analysis area through a second user interface connected to the processor; and A step of supporting the operation of a health care service included in the health care service area through a third user interface connected to the processor; Diabetes prediction and management method including
  2. In claim 1, A method for predicting and managing diabetes, further comprising the step of supporting the operation of the data collection area by a basic member information collection module, a Bluetooth-linked customer health information collection module, a blood glucose information collection module, a survey health information collection module, and a general medical examination information collection module mounted on the processor.
  3. In claim 2, A method for predicting and managing diabetes, further comprising the step of supporting the operation of the health status integrated analysis area by a behavioral pattern monitoring module, a blood glucose analysis module, a survey analysis module, and a comprehensive analysis module mounted on the processor.
  4. In claim 3, A step of generating a prediction result that predicts the onset of diabetes or the occurrence of diabetes complications based on basic health information obtained from an information providing device by the comprehensive analysis module above; and A step of simulating future predicted changes in diabetes morbidity or the occurrence of diabetes complications by adding control variables such as age, weight or body mass index, blood pressure, and blood sugar, obtained in real-time via an online survey or an IoT device, to the above prediction results; Diabetes prediction and management method including further
  5. In any one of claims 1 to 4, A method for predicting and managing diabetes, further comprising the step of supporting the health management service area by a life coaching provision module, a health management goal recommendation module, a recommendation result provision module, and a health information sharing module mounted on the processor.
  6. In claim 5, A method for predicting and managing diabetes, further comprising the step of transmitting a signal instructing an information providing device that collects and stores the user's health information by means of the health information sharing module to share at least one first health information among the user's personal health records to an external user terminal or diabetes prediction and management device.
  7. As a personal health record-based diabetes prediction and management device, processor; A dashboard controlled by the above-mentioned processor and comprising a data collection area, a health status integrated analysis area, and a health management service area; A first user interface connected to the above processor and supporting the operation of collecting basic member information, collecting Bluetooth-linked customer health information, collecting blood glucose information, collecting survey health information, and collecting general checkup information included in the data collection area; A second user interface connected to the processor and supporting the operation of behavioral pattern monitoring, blood glucose analysis, survey analysis, and comprehensive analysis included in the health status integrated analysis area; and A diabetes prediction and management device comprising a third user interface connected to the above processor and supporting the operation of a health care service included in the health care service area.
  8. In claim 7, A diabetes prediction and management device further comprising a basic member information collection module, a Bluetooth-linked customer health information collection module, a blood glucose information collection module, a survey health information collection module, and a general checkup information collection module, which are mounted on the above processor and support the above data collection area.
  9. In claim 8, A diabetes prediction and management device further comprising a behavioral pattern monitoring module, a blood glucose analysis module, a survey analysis module, and a comprehensive analysis module, which are mounted on the above processor and support the above health status integrated analysis area.
  10. In claim 8, The above-described comprehensive analysis module generates a prediction result predicting the onset of diabetes or the occurrence of diabetes complications based on basic health information obtained from an information providing device, and simulates future predicted changes regarding the onset of diabetes or the occurrence of diabetes complications by adding control variables such as age, weight or body mass index, blood pressure, and blood sugar, obtained in real-time via an online survey or an Internet of Things device, to the prediction result.
  11. In any one of claims 7 to 10, A diabetes prediction and management device that is mounted on the above processor and further includes a life coaching provision module, a health management goal recommendation module, a recommendation result provision module, and a health information sharing module, which support the above health management service area.
  12. In claim 11, The above-mentioned health information sharing module is a diabetes prediction and management device that transmits a signal instructing the information providing device, which collects and stores the user's health information, to share at least one first health information among the user's personal health records to a user terminal or outside the diabetes prediction and management device.

Description

Method and Apparatus for Predicting and Managing Diabetes Based on Personal Health Records Using Artificial Intelligence The present invention relates to a technology for predicting and managing diabetes, and more specifically, to a method and apparatus for predicting and managing diabetes based on personal health records utilizing artificial intelligence, which simulates a customized personal profile based on personal health records to predict the likelihood of developing diabetes several years in the future and provides reference values for personal health management to suppress the onset of diabetes. Diabetes refers to a group of metabolic diseases characterized by persistently high blood sugar levels, caused by the pancreas failing to produce sufficient insulin or the body's cells failing to respond properly to the produced insulin. Chronic hyperglycemia caused by factors such as insulin deficiency is accompanied by various characteristic metabolic abnormalities. Since insulin is primarily involved in carbohydrate metabolism, abnormalities in carbohydrate metabolism are the fundamental problem in diabetes caused by insulin deficiency. When diabetes occurs, the metabolism of all nutrients in the body is affected, so it is considered to be a comprehensive metabolic disease. In the presence of diabetes, symptoms associated with high blood sugar include frequent urination and intensified thirst and hunger; if left untreated, this can lead to other complications. Acute complications include diabetic ketoacidosis and hyperglycemic hyperosmolar nonketotic coma, while serious long-term complications include cardiovascular disease, stroke, chronic renal failure, diabetic foot ulcers, diabetic retinopathy, and hypoglycemia. Among these, hypoglycemia is one of the frequently occurring complications in diabetic patients, referring to a state in which blood glucose levels are severely reduced due to insulin use or strict blood glucose management. When hypoglycemia occurs, the supply of glucose to the brain and nervous system becomes insufficient, and the brain's nervous system senses an energy shortage, triggering the body's autonomic nervous system. As a result, dizziness and fatigue may occur, and if severe, it can lead to epileptic seizures, loss of consciousness, or even death. When hypoglycemia occurs, the body secretes adrenaline to overcome it; consequently, this causes an increase in blood pressure, heart rate, palpitations, tremors, and anxiety, while the action of the parasympathetic nervous system may result in cold sweats, hunger, and abnormal sensations. In particular, severe hypoglycemia refers to a condition where the severity of the low blood sugar leads to a decline in consciousness to the point where the patient is unable to take measures to raise their blood sugar levels on their own. When severe hypoglycemia occurs, the patient is at high risk of death without assistance from others. Furthermore, it is known that diabetic patients who have experienced severe hypoglycemia have a higher probability of developing complications later on, such as cardiovascular disease, arrhythmias, and cognitive decline. As such, there is still a need for improved measures to predict and manage diabetes or diabetic complications. The accompanying drawings, which are included as part of the detailed description to aid in understanding the present invention, provide embodiments of the present invention and, together with the detailed description, illustrate the technical concept of the present invention. FIG. 1 is a schematic block diagram illustrating a MyHealthWay system to which a method for predicting and managing diabetes according to an embodiment of the present invention can be applied. Figure 2 is a block diagram of a diabetes prediction and management device that can be employed in the My Health Way system of Figure 1. FIG. 3 is a block diagram illustrating artificial intelligence that can be employed in the diabetes prediction and management device of FIG. 2 (hereinafter briefly referred to as the 'prediction device'). FIG. 4 is a block diagram illustrating a hardware and/or software configuration that can be employed in the prediction device of FIG. 2. Figure 5 is an example diagram illustrating the operating principle of a prediction model that can be employed in the prediction device of Figure 2. Figure 6 is an example diagram of a neural network model to explain an advanced strategy for predicting diabetes prevalence through transfer learning that can be employed in the prediction device of Figure 2. FIG. 7 is a block diagram illustrating another hardware and/or software configuration that can be employed in the prediction device of FIG. 2. Figure 8 is a graph illustrating the probability of developing diabetes on a user interface screen that can be adopted in the prediction device of Figure 2. Figure 9 is an example diagram illustrating diabetes prediction and management results on a user interface scree