Please use this identifier to cite or link to this item: http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/10004
Title: Trans AMR: An Interpretable Transformer Model for Accurate Prediction of Antimicrobial Resistance using Antibiotic Administration Data
Authors: Mukunthan, T.
Wenwu, W.
Michael, K.
Roberto Lo, R.
Anil, F.
Keywords: Transformer;Multi-drug AMR;Antimicrobial stewardship;Missing labels;XAI;Multi-label prediction
Issue Date: 2023
Publisher: IEEE
Abstract: AntimicrobialResistance(AMR)isagrowingpublicandveterinaryhealthconcern,andthe abilitytoaccuratelypredictAMRfromantibioticsadministrationdataiscrucialforeffectivelytreatingand managinginfections.Whilegenomics-basedapproachescanprovidebetterresults,sequencing,assembling, andapplyingMachineLearning(ML)methodscantakeseveralhours.Therefore,alternativeapproachesare required.ThisstudyfocusedonusingMLforantimicrobialstewardshipbyutilisingdataextractedfrom hospitalelectronichealthrecords,whichcanbedoneinreal-time,anddevelopinganinterpretable1DTransformermodelforpredictingAMR.Amulti-baselineIntegratedGradientpipelinewasalsoincorporated tointerpretthemodel,andquantitativevalidationmetricswereintroducedtovalidatethemodel.The performanceoftheproposed1D-Transformermodelwasevaluatedusingadatasetofurinarytractinfection (UTI)patientswithfourantibiotics.Theproposed1D-Transformermodelachieved10%higherareaunder curve(AUC)inpredictingAMRandoutperformedtraditionalMLmodels.TheExplainableArtificial Intelligence(XAI)pipelinealsoprovidedinterpretableresults,identifyingthesignaturescontributingto thepredictions.Thiscouldbeusedasadecisionsupporttoolforpersonalisedtreatment,introducing AMR-awarefoodandmanagementofAMR,anditcouldalsobeusedtoidentifysignaturesfortargeted interventions.
URI: http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/10004
Appears in Collections:Electrical & Electronic Engineering



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