The Use of Event-Related Potentials and Machine Learning to Improve Diagnostic Testing and Prediction of Disease Progression in Parkinson's Disease

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dc.contributor.authorVlieger, Robin
dc.contributor.authorDaskalaki, Eleni
dc.contributor.authorApthorp, Deborah
dc.contributor.authorLueck, Christian
dc.contributor.authorSuominen, Hanna
dc.contributor.editorHoney, M.
dc.contributor.editorRonquillo, C.
dc.contributor.editorLee, T.-T.
dc.contributor.editorWestbrooke, L.
dc.coverage.spatialONLINE
dc.date.accessioned2024-01-29T22:08:01Z
dc.date.available2024-01-29T22:08:01Z
dc.date.created23 AUGUST – 2 SEPTEMBER 2021
dc.date.issued2021
dc.date.updated2022-10-02T07:18:43Z
dc.description.abstractCurrent tests of disease status in Parkinson's disease suffer from high variability, limiting their ability to determine disease severity and prognosis. Event-related potentials, in conjunction with machine learning, may provide a more objective assessment. In this study, we will use event-related potentials to develop machine learning models, aiming to provide an objective way to assess disease status and predict disease progression in Parkinson's disease.en_AU
dc.description.sponsorshipThis research was funded by and has been delivered in partnership with Our Health in Our Hands (OHIOH), a strategic initiative of the ANU, which aims to transform health care by developing new personalized health technologies and solutions in collaboration with patients, clinicians, and health-care providers.en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.isbn978-1-64368-220-4en_AU
dc.identifier.urihttp://hdl.handle.net/1885/312392
dc.language.isoen_AUen_AU
dc.provenanceThis article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). doi:10.3233/SHTI210737en_AU
dc.publisherIOS Pressen_AU
dc.relation.ispartofseries15th international congress in nursing informationen_AU
dc.rights© 2021 International Medical Informatics Association (IMIA) and IOS Press.en_AU
dc.rights.licenseCreative Commons Attribution 4.0 International Licenseen_AU
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_AU
dc.sourceNurses and Midwives in the Digital Ageen_AU
dc.subjectDiagnosisen_AU
dc.subjectevent-related potentialsen_AU
dc.subjectmachine learningen_AU
dc.subjectParkinson diseaseen_AU
dc.subjectdisease statusen_AU
dc.subjectpredictionen_AU
dc.titleThe Use of Event-Related Potentials and Machine Learning to Improve Diagnostic Testing and Prediction of Disease Progression in Parkinson's Diseaseen_AU
dc.typeConference paperen_AU
dcterms.accessRightsOpen Accessen_AU
local.bibliographicCitation.lastpage335en_AU
local.bibliographicCitation.startpage333en_AU
local.contributor.affiliationVlieger, Robin, College of Engineering and Computer Science, ANUen_AU
local.contributor.affiliationDaskalaki, Eleni, College of Engineering and Computer Science, ANUen_AU
local.contributor.affiliationApthorp, Deborah, College of Engineering and Computer Science, ANUen_AU
local.contributor.affiliationLueck, Christian, College of Health and Medicine, ANUen_AU
local.contributor.affiliationSuominen, Hanna, College of Engineering and Computer Science, ANUen_AU
local.contributor.authoremailu5331246@anu.edu.auen_AU
local.contributor.authoruidVlieger, Robin, u7021239en_AU
local.contributor.authoruidDaskalaki, Eleni, u1085378en_AU
local.contributor.authoruidApthorp, Deborah, u5331246en_AU
local.contributor.authoruidLueck, Christian, u1807496en_AU
local.contributor.authoruidSuominen, Hanna, u4872279en_AU
local.description.notesImported from ARIESen_AU
local.description.refereedYes
local.identifier.absfor420318 - People with disabilityen_AU
local.identifier.absfor320905 - Neurology and neuromuscular diseasesen_AU
local.identifier.ariespublicationa383154xPUB24117en_AU
local.identifier.doi10.3233/SHTI210737en_AU
local.identifier.scopusID2-s2.0-85122043820
local.identifier.uidSubmittedBya383154en_AU
local.publisher.urlhttps://ebooks.iospress.nl/doi/10.3233/SHTI210737en_AU
local.type.statusPublished Versionen_AU

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