  This work considers the problem of personalized dose guidance using Bayesian
optimization that learns the optimum drug dose tailored to each individual,
thus improving therapeutic outcomes. Safe learning using interior point method
ensures patient safety with high probability. This is demonstrated using the
problem of learning the optimum bolus insulin dose in patients with type 1
diabetes to counteract the effect of meal consumption. Starting from no a
priori information about the patients, our dose guidance algorithm is able to
improve the therapeutic outcome (measured in terms of % time-in-range) without
jeopardizing patient safety. Other potential healthcare applications are also
discussed.
