Research and progress of algorithms in blood glucose prediction of diabetic patients
JIANG Zhuoyun
YUAN Suhai
YU Zhichao
LI Zhanhong
ZHANG Guanqun
ZHU Zhigang
Abstract：The blood glucose levels of patients with diabetes mellitus(DM)are influenced by multiple factors such as diet,exer-cise,insulin and stress,making it rather difficult to control.Accurate prediction of blood glucose changes can not only help to predict the risks of hypo-and hyperglycemia,but also optimize treatment plans and improve the quality of life of patients.Deep learning(DL)is widely applied in blood glucose prediction due to its powerful temporal modeling capabilities.The development of continuous blood glucose monitoring(CGM)and wearable sensors provides data support for DL.This paper systematically reviews the research progress of DL in the field of blood glucose prediction,introduces the common data sets and evaluation metrics and compares the performance of different models.Finally,the future development directions such as personalized prediction,multimodal fusion and interpretability mod-els are prospected to provide references for blood glucose prediction research.
Keywords：Diabetes mellitusBlood glucose levelHyperglycemia and hypoglycemiaContinuous blood glucose monitoringDeep learning
Publication Date：2025-12-30
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：8( 415-422 )