Biology subjects

Houy, N.

Publications and source records attributed to Houy, N..

3 recordsLinked to original sources

bioRxiv2019

Informed and uninformed empirical therapy policies

We argue that a proper distinction must be made between informed and uninformed decision making when setting empirical therapy policies, as this allows to estimate the value of gathering more information and to set research priorities. We rely on the stochastic version of a compartmental model to describe the spread of an infecting organism in a health care facility, and the emergence and spread of resistance to two drugs. We focus on information and uncertainty regarding the parameters of this model. We consider a family of adaptive policies. In the uninformed setting, the best adaptive policy allows to reduce the average cumulative infected patient-days over two years by 39.3% (95% CI: 30.3% - 48.1%) compared to the combination therapy. Choosing empirical therapy policies while knowing the exact parameter values allows to further decrease the cumulative infected patient-days on average by 3.9% (95% CI: 2.1% - 5.8%). In our setting, the benefit of perfect information might be offset by increased drug consumption.

epidemiology↗

bioRxiv2019

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

We present a solution method to the problem of choosing empirical treatments that minimize the cumulative infected patient-days in the long run in a health care facility. We rely on the stochastic version of a compartmental model to describe the spread of an infecting organism in the health care facility, and the emergence and spread of resistance to two drugs. We assume that the parameters of the model are known. Empirical treatments are chosen at the beginning of each period based on the count of patients with each health status. The same treatment is then administered to all patients, including uninfected patients, during the period and cannot be adjusted until the next period. Our solution method is a variant of the Monte-Carlo tree search algorithm. In our simulations, it allows to reduce the average cumulative infected patient-days over two years by 47.0% compared to the best standard therapy. We explain how our algorithm can be used either to perform online optimization, or to produce data for quantitative analysis.

epidemiology↗

bioRxiv2019

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

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.

epidemiology↗