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- W2938001544 abstract "Abstract We 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." @default.
- W2938001544 created "2019-04-25" @default.
- W2938001544 creator A5053448585 @default.
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- W2938001544 date "2019-04-12" @default.
- W2938001544 modified "2023-10-18" @default.
- W2938001544 title "Optimal dynamic empirical therapy in a health care facility: an artificial intelligence approach" @default.
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- W2938001544 doi "https://doi.org/10.1101/603464" @default.
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