A Study of Methods for Predicting and Controlling Blood Glucose in Diabetic Patients

Abstract

With the development of society and the improvement of people's living standard, diabetes has gradually become one of the world public health problems that endanger human life safety and affect the development of global economy. Type 1 diabetes is a chronic metabolic disorder that prevents the pancreas from producing insulin and requires lifelong treatment with daily insulin injections to prevent high blood sugar. The lack of insulin leads to the continuous high level of blood sugar in patients. The most important thing for patients is to monitor blood sugar changes and control blood sugar within the normal range. In this paper, a systematic literature search is carried out to study these two aspects, and key information such as the learning model adopted in the literature, the main results, the development of relevant technologies, and the limitations are summarized. From the perspective of glucose-insulin prediction model, due to the complex structure of physiological model, many parameters and difficult to identify, most of the modeling methods are data-driven. There is a great room for improvement in the research on how to mine and utilize existing models to effectively establish accurate glucose prediction models for different objects. From the perspective of control theory, after insulin injection, there is a certain delay in the reduction of blood glucose concentration, and the onset time is different depending on the injection site. The rapid development of deep learning and the increase in available data offer the possibility of addressing these challenges in the near future. When designing closed-loop glucose algorithms, consider using a variety of approaches to establish a personalized glucose control algorithm for each diabetic patient.