Abstract
Background and Aims
Despite the development of artificial pancreas, many diabetics are using insulin injection devices. And about 80% of insulin delivery products are bolus injection devices and the rate of used as home care is higher than the rate used in hospitals and clinics. Therefore, insulin bolus calculation algorithm for insulin injection devices will be helpful for many diabetics. In this study, we propose a reinforcement learning based bolus calculation algorithm.
Methods
We determine the injection timing of insulin bolus through the PK/PD (pharmacokinetics/pharmacodynamics) curve of OGTT (Oral Glucose Tolerance Test) and the GCT (Glucose Clamp Test). And injection amount of insulin bolus is determined before each three meals using DQN (Deep Q-Networks) reinforcement learning algorithm. Basal insulin is injected with an optimal amount. Also, to learn the method to prevent hypoglycemia more effectively, snacks are not allowed before bedtime and higher penalty is given in hypoglycemia. The proposed method is evaluated on one adult from the US-FDA approved UVa/Padova simulator under a multi-meal scenario.
Results
The insulin bolus calculation algorithm achieves a mean glucose of 114.54 mg/dl and a time in range of 89.30%. Insulin is injected 25 minutes before each meal and the algorithm showed a tendency to change the amount of insulin injection according to the amount of meal.
Conclusions
The reinforcement learning based insulin bolus calculation algorithm is effective in determining the amount of insulin bolus according to the amount of food and the personalized insulin injection timing according to PK/PD characteristics.
