  Effective postprandial glucose control is important to glucose management for
subjects with diabetes mellitus. In this work, a data-driven meal bolus
decision method is proposed without the need of subject-specific glucose
management parameters. The postprandial glucose dynamics is learnt using
Gaussian process regression. Considering the asymmetric risks of hyper- and
hypoglycemia and the uncertainties in the predicted glucose trajectories, an
asymmetric risk-sensitive cost function is designed. Bayesian optimization is
utilized to solve the optimization problem, since the gradient of the cost
function is unavailable. The proposed approach is evaluated using the 10-adult
cohort of the FDA-accepted UVA/Padova T1DM simulator and compared with the
standard insulin bolus calculator. For the case of announced meals, the
proposed method achieves satisfactory and similar performance in terms of mean
glucose and percentage time in [70, 180] mg/dL without increasing the risk of
hypoglycemia. Similar results are observed for the case without the meal
information (assuming that the patient follows a consistent diet) and the case
of basal rate mismatches. In addition, advisory-mode analysis is performed
based on clinical data, which indicates that the method can determine safe and
reasonable meal boluses in real clinical settings. The results verify the
effectiveness and robustness of the proposed method and indicate the
feasibility of achieving improved postprandial glucose regulation through a
data-driven optimal control method.
