Abstract
Background and Aims
Physical activities have a significant impact on blood glucose homeostasis of patients with type 1 diabetes. The risk of hypoglycemia (low blood glucose) is significantly higher during and after physical activities, especially for individuals who experience hypoglycemia unawareness. Our research aims to reduce the risk of hypoglycemia and empower type 1 diabetes patients in making decisions regarding food choices, insulin, exercise intensities and other factors in connection with physical activities.
Methods
Using historical physical activity data, a feedforward neural network is trained to provide a prediction of the blood glucose outcome during and after physical activities. Based on the personalized neural network and Q learning (a model free reinforcement learning), optimal actions that minimize the risk of hypoglycemia and improve blood glucose regulation are provided to the patient.
Results
In-silico results on a blood glucose simulator with the Breton’s physical activity model show that the proposed methodology is capable of maintaining the blood glucose in the healthy range during and after physical activities. No hypoglycemia has occured when following the recommendation of the decision support system.
Conclusions
The research shows the potential of using machine learning in reducing the risk of hypoglycemia and better glycemic control during and after physical activities. Important research are identified and conducted to reduce the roller coaster effect and provide better combination of food intake, insulin and other factors for patients in their daily management of type 1 diabetes.
