Personalized Dose Guidance using Safe Bayesian Optimization

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.