2020/05/18 by Woo Seok Lee, Junghyo Jo, Taegeun Song
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Health Professions · Mathematics · Medicine · #Artificial Intelligence in Healthcare #Artificial intelligence #Biology #Computer science #Continuous glucose monitoring #Diabetes Management and Research #Diabetes mellitus #Diabetes, Cardiovascular Risks, and Lipoproteins #Endocrinology #Internal medicine #Machine learning #Medicine #Stage (stratigraphy) #Type 1 diabetes #cs.LG #eess.SP #q-bio.QM #stat.ML
paper · pdf · doi:10.1007/s40042-021-00056-8
4 pages, 2 figure
arxiv created 2020/05/18 · openalex publication_date 2021/02/01 · arxiv updated 2021/02/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Machine learning shows remarkable success for recognizing patterns in data. Here we apply the machine learning (ML) for the diagnosis of early stage diabetes, which is known as a challenging task in medicine. Blood glucose levels are tightly regulated by two counter-regulatory hormones, insulin and glucagon, and the failure of the glucose homeostasis leads to the common metabolic disease, diabetes mellitus. It is a chronic disease that has a long latent period the complicates detection of the disease at an early stage. The vast majority of diabetics result from that diminished effectiveness of insulin action. The insulin resistance must modify the temporal profile of blood glucose. Thus we propose to use ML to detect the subtle change in the temporal pattern of glucose concentration. Time series data of blood glucose with sufficient resolution is currently unavailable, so we confirm the proposal using synthetic data of glucose profiles produced by a biophysical model that considers the glucose regulation and hormone action. Multi-layered perceptrons, convolutional neural networks, and recurrent neural networks all identified the degree of insulin resistance with high accuracy above 85%.