Personalized Dose Guidance using Safe Bayesian Optimization

TL;DR

This paper presents a safe Bayesian optimization approach for personalized drug dose guidance, demonstrated on insulin dosing for type 1 diabetes, ensuring safety while improving therapeutic outcomes without prior patient data.

Contribution

It introduces a novel safe Bayesian optimization method with an interior point approach for personalized dose learning, applicable even with no initial patient data.

Findings

Improved % time-in-range for insulin dosing

Safe learning with high probability of patient safety

Applicable to other healthcare dose optimization tasks

Abstract

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.