An Improved Strategy for Blood Glucose Control Using Multi-Step Deep Reinforcement Learning

Diabetes profoundly affects human life and health, regardless of country, age, or gender, and is one of the leading causes of death and disability worldwide [1]. From 1990 to 2021, the age-standardized prevalence of diabetes increased by 90.5 % globally, with increases of more than 100 % in several regions, and it is projected that by 2050, there will be 1.31 billion people with diabetes worldwide [1]. Furthermore, people with diabetes have more than twice the normal risk of early death, resulting in an estimated 150-500 million deaths around the world each year, while generating approximately 12% of health expenditure ($966 billion) [2, 3]. The rising prevalence and serious health and economic hazards have attracted the attention of scientists around the globe, and as a result, the number of studies on diabetes is increasing. The pancreas of a diabetic does not produce or produces very little insulin, or the insulin produced is not used efficiently, leading to high BG and a variety of life-threatening complications such as cardiovascular disease, nerve damage, kidney damage, lower limb amputations, and eye disease leading to decreased vision and even blindness [3]. BG control is their basic treatment, as well as the basis for preventing and treating diabetic complications. Patients mainly maintain the stability of BG by injecting insulin. However, this traditional self-management is usually cumbersome and challenging, as it requires patients to measure their BG levels several times a day, while they suffer from many of the aforementioned complications [2].