Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/30471
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Multi-objective Evolutionary Design of Antibiotic Treatments
Author(s): Ochoa, Gabriela
Christie, Lee A
Brownlee, Alexander E
Hoyle, Andrew
Contact Email: alexander.brownlee@stir.ac.uk
Keywords: antibiotic resistance
antimicrobial resistance
AMR
evolutionary computation
stochastic model
Issue Date: Jan-2020
Citation: Ochoa G, Christie LA, Brownlee AE & Hoyle A (2020) Multi-objective Evolutionary Design of Antibiotic Treatments. Artificial Intelligence in Medicine, 102, Art. No.: 101759. https://doi.org/10.1016/j.artmed.2019.101759
Abstract: Antibiotic resistance is one of the major challenges we face in modern times. Antibiotic use, especially their overuse, is the single most important driver of antibiotic resistance. Efforts have been made to reduce unnecessary drug prescriptions, but limited work is devoted to optimising dosage regimes when they are prescribed. The design of antibiotic treatments can be formulated as an optimisation problem where candidate solutions are encoded as vectors of dosages per day. The formulation naturally gives rise to competing objectives, as we want to maximise the treatment effectiveness while minimising the total drug use, the treatment duration and the concentration of antibiotic experienced by the patient. This article combines a recent mathematical model of bacterial growth including both susceptible and resistant bacteria, with a multi-objective evolutionary algorithm in order to automatically design successful antibiotic treatments. We consider alternative formulations combining relevant objectives and constraints. Our approach obtains shorter treatments, with improved success rates and smaller amounts of drug than the standard practice of administering daily fixed doses. These new treatments consistently involve a higher initial dose followed by lower tapered doses.
DOI Link: 10.1016/j.artmed.2019.101759
Rights: This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. Accepted refereed manuscript of: Ochoa G, Christie LA, Brownlee AE & Hoyle A (2020) Multi-objective Evolutionary Design of Antibiotic Treatments. Artificial Intelligence in Medicine, 102, Art. No.: 101759. DOI: https://doi.org/10.1016/j.artmed.2019.101759 © 2019, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Licence URL(s): http://creativecommons.org/licenses/by-nc-nd/4.0/

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