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dc.contributor.authorStorås, Andrea
dc.contributor.authorÅsberg, Anders
dc.contributor.authorHalvorsen, Pål
dc.contributor.authorRiegler, Michael Alexander
dc.contributor.authorStrumke, Inga
dc.date.accessioned2023-03-27T13:46:19Z
dc.date.available2023-03-27T13:46:19Z
dc.date.issued2022-08-31
dc.description.abstractTacrolimus is one of the cornerstone immunosup-pressive drugs in most transplantation centers worldwide following solid organ transplantation. Therapeutic drug monitoring of tacrolimus is necessary in order to avoid rejection of the transplanted organ or severe side effects. However, finding the right dose for a given patient is challenging, even for experienced clinicians. Consequently, a tool that can accurately estimate the drug exposure for individual dose adaptions would be of high clinical value. In this work, we propose a new technique using machine learning to estimate the tacrolimus exposure in kidney transplant recipients. Our models achieve predictive errors that are at the same level as an established population pharmacokinetic model, but are faster to develop and require less knowledge about the pharmacokinetic properties of the drug.en_US
dc.identifier.citationStorås, Åsberg, Halvorsen, Riegler, Strumke: Predicting Tacrolimus Exposure in Kidney Transplanted Patients Using Machine Learning. In: Shen, González AR, Santosh, Lai, Sicilia, Almeida JR, Kane B. 2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS), 2022. IEEE (Institute of Electrical and Electronics Engineers) p. 38-43en_US
dc.identifier.cristinIDFRIDAID 2094240
dc.identifier.doi10.1109/CBMS55023.2022.00014
dc.identifier.isbn978-1-6654-6770-4
dc.identifier.issn2372-9198
dc.identifier.urihttps://hdl.handle.net/10037/28866
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titlePredicting Tacrolimus Exposure in Kidney Transplanted Patients Using Machine Learningen_US
dc.type.versionacceptedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)