Diabetes Prediction With Clinical and Genetic Data Professor Nan-hee Kim's team at Korea University Ansan Hospital registers a patent for the system
Aug 28, 2024
A research team led by Professor Kim Nan-hee of the Department of Endocrinology at Korea University Ansan Hospital (Professors Kim Nan-hee, Park So-young, Medical Life Research Center Kim Min-hee, and Kim Jae-young) recently developed a diabetes prediction system using clinical and genetic data and completed patent registration in Korea.
Diabetes prediction systems are technologies that can predict diabetes using clinical data and race-specific genetic data.
Type 2 diabetes, which accounts for 90% of diabetics, occurs when insulin resistance is developed by genetic and environmental factors such as aging, obesity, drugs, and stress. If diabetes develops, it is difficult to cure and various complications can be accompanied, so prevention before the outbreak is of paramount importance. If the system is commercialized in the medical field, it will help to derive personalized early diagnosis and prevention measures that take into account environmental and natural factors of diabetic patients.
The diabetes prediction system consists of ▶ database unit ▶ data processing unit ▶ prediction unit ▶ analysis unit ▶ user report generation unit.
The database unit serves to receive and manage the user's clinical data and genetic data. At this time, the clinical data includes information such as fasting blood sugar, diet, and obesity, and the genetic data includes genetic information related to type 2 diabetes and risk-confrontation genetic information that increases the risk of developing the disease. The data processing unit extracts the frequency of occurrence of specific genetic mutations of the user related to diabetes from the genetic data for each race stored in the database unit. Based on this, the prediction unit calculates whether diabetes occurs within the next five years, and the analysis unit analyzes the risk factors for diabetes. Finally, the user report generation unit derives a comprehensive diagnosis and prevention plan.
Professor Nan-Hee Kim said "The diabetes prediction system can accurately predict the user's personal and genetic characteristics and lifestyle by reflecting the user's personal and genetic characteristics and lifestyle."This allows us to predict when diabetes is at high risk and take preventive measures early."
Diabetes prediction systems are technologies that can predict diabetes using clinical data and race-specific genetic data.
Type 2 diabetes, which accounts for 90% of diabetics, occurs when insulin resistance is developed by genetic and environmental factors such as aging, obesity, drugs, and stress. If diabetes develops, it is difficult to cure and various complications can be accompanied, so prevention before the outbreak is of paramount importance. If the system is commercialized in the medical field, it will help to derive personalized early diagnosis and prevention measures that take into account environmental and natural factors of diabetic patients.
The diabetes prediction system consists of ▶ database unit ▶ data processing unit ▶ prediction unit ▶ analysis unit ▶ user report generation unit.
The database unit serves to receive and manage the user's clinical data and genetic data. At this time, the clinical data includes information such as fasting blood sugar, diet, and obesity, and the genetic data includes genetic information related to type 2 diabetes and risk-confrontation genetic information that increases the risk of developing the disease. The data processing unit extracts the frequency of occurrence of specific genetic mutations of the user related to diabetes from the genetic data for each race stored in the database unit. Based on this, the prediction unit calculates whether diabetes occurs within the next five years, and the analysis unit analyzes the risk factors for diabetes. Finally, the user report generation unit derives a comprehensive diagnosis and prevention plan.
Professor Nan-Hee Kim said "The diabetes prediction system can accurately predict the user's personal and genetic characteristics and lifestyle by reflecting the user's personal and genetic characteristics and lifestyle."This allows us to predict when diabetes is at high risk and take preventive measures early."
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