Optimal dynamic empirical therapy in a health care facility: an artificial intelligence approach

Abstract

AbstractWe propose a solution to the problem of finding an empirical therapy policy in a health care facility that minimizes the cumulative infected patient-days over a given time horizon. We assume that the parameters of the model are known and that when the policy is implemented, all patients receive the same treatment at a given time. We model the emergence and spread of antimicrobial resistance at the population level with the stochastic version of a compartmental model. The model features two drugs and the possibility of double resistance. Our solution method is a variant of the Monte-Carlo tree search algorithm. In our example, this method allows to reduce the cumulative infected patient-days over two years by 22% compared to the best standard therapy.