A General Neural Architecture for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Management

People with type 1 diabetes must constantly monitor their blood glucose levels and take actions to keep them from getting either too high or too low. Having a snack will raise blood glucose levels; however, the amount of carbohydrates that should be consumed to reach a target level depends on the recent history of blood glucose levels, meals, boluses, and the basal rate of insulin. Conversely, to lower the blood glucose level, one can administer a bolus of insulin; however, determining the right amount of insulin in the bolus can be cognitively demanding, as it depends on similar contextual factors. In this paper, we show that a generic neural architecture previously used for blood glucose prediction in a what-if scenario can be converted to make either carbohydrate or bolus recommendations. Initial experimental evaluations on the task of predicting carbohydrate amounts necessary to reach a target blood glucose level demonstrate the feasibility and potential of this general approach.