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Finding an optimal individualized treatment regimen is considered one of themost challenging precision medicine problems. Various patient characteristicsinfluence the response to the treatment, and hence, there is noone-size-fits-all regimen. Moreover, the administration of an unsafe doseduring the treatment can have adverse effects on health. Therefore, a treatmentmodel must ensure patient \emph{safety} while \emph{efficiently} optimizing thecourse of therapy. We study a prevalent medical problem where the treatmentaims to keep a physiological variable in a safe range and preferably close to atarget level, which we refer to as \emph{leveling}. Such a task may be relevantin numerous other domains as well. We propose ESCADA, a novel and genericmulti-armed bandit (MAB) algorithm tailored for the leveling task, to makesafe, personalized, and context-aware dose recommendations. We derive highprobability upper bounds on its cumulative regret and safety guarantees.Following ESCADA's design, we also describe its Thompson sampling-basedcounterpart. We discuss why the straightforward adaptations of the classicalMAB algorithms such as GP-UCB may not be a good fit for the leveling task.Finally, we make \emph{in silico} experiments on the bolus-insulin doseallocation problem in type-1 diabetes mellitus disease and compare ouralgorithms against the famous GP-UCB algorithm, the rule-based dosecalculators, and a clinician.

ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicine