Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/30471
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dc.contributor.authorOchoa, Gabrielaen_UK
dc.contributor.authorChristie, Lee Aen_UK
dc.contributor.authorBrownlee, Alexander Een_UK
dc.contributor.authorHoyle, Andrewen_UK
dc.date.accessioned2019-11-19T01:01:36Z-
dc.date.available2019-11-19T01:01:36Z-
dc.date.issued2020-01en_UK
dc.identifier.other101759en_UK
dc.identifier.urihttp://hdl.handle.net/1893/30471-
dc.description.abstractAntibiotic 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.en_UK
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.relationOchoa 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.2019.101759en_UK
dc.rightsThis 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/en_UK
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en_UK
dc.subjectantibiotic resistanceen_UK
dc.subjectantimicrobial resistanceen_UK
dc.subjectAMRen_UK
dc.subjectevolutionary computationen_UK
dc.subjectstochastic modelen_UK
dc.titleMulti-objective Evolutionary Design of Antibiotic Treatmentsen_UK
dc.typeJournal Articleen_UK
dc.rights.embargodate2020-11-18en_UK
dc.rights.embargoreason[AI_in_Medicine__Multi_objective_Evolutionary_Design_of_Antibiotic_Treatments.pdf] Publisher requires embargo of 12 months after formal publication.en_UK
dc.identifier.doi10.1016/j.artmed.2019.101759en_UK
dc.identifier.pmid31980097en_UK
dc.citation.jtitleArtificial Intelligence in Medicineen_UK
dc.citation.issn1873-2860en_UK
dc.citation.issn0933-3657en_UK
dc.citation.volume102en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.author.emailalexander.brownlee@stir.ac.uken_UK
dc.citation.date17/11/2019en_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationRobert Gordon Universityen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.identifier.isiWOS:000514254100004en_UK
dc.identifier.scopusid2-s2.0-85075280457en_UK
dc.identifier.wtid1477056en_UK
dc.contributor.orcid0000-0001-7649-5669en_UK
dc.contributor.orcid0000-0003-2892-5059en_UK
dc.contributor.orcid0000-0002-9117-7041en_UK
dc.date.accepted2019-11-05en_UK
dc.date.filedepositdate2019-11-18en_UK
Appears in Collections:Computing Science and Mathematics Journal Articles

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