Anticipatory Behavior in Blood Glucose Control: Using Meal Prior Probabilities to Prepare for Future Meal Disturbances

Abstract

AbstractAutomatic regulation of blood glucose in patients with Type 1 Diabetes is challenged by unknown or unannounced food consumption. Yet we know most food is ingested in discrete meals, spaced by brief periods of fasting. Treating meals as discrete events, this paper proposes a set of prior probabilities relating distinct meals independently of daily patterns. Using these prior probabilities, closed-loop blood glucose control can attain anticipatory behavior, implicitly lowering glucose levels when patients should be hungry and meals are most likely to occur. This improves overall performance and individual meal responses. The benefits occur because blood glucose prediction, which anticipates future food intake, can achieve near zero mean errors even over a long prediction horizon. As a side effect, such predictors can easily be tuned or tested against unrestricted out-patient data which, by definition, contains unknown meals. We validate our approach in both prediction and control of blood glucose levels. We see improved prediction accuracy over 1-4 hour horizons and a significant reduction in the blood glucose risk index for simulated closed-loop control.