Other publication
The Use of Event-Related Potentials and Machine Learning to Improve Diagnostic Testing and Prediction of Disease Progression in Parkinson's Disease
Authors: Vlieger Robin, Daskalaki Elena, Apthorp Deborah, Lueck Christian J, Suominen Hanna
Editors: Michelle Honey, Charlene Ronquillo, Ting-Ting Lee, Lucy Westbrooke
Conference name: International Congress in Nursing Informatics
Publication year: 2021
Journal: Studies in Health Technology and Informatics
Book title : Nurses and Midwives in the Digital Age: Selected Papers, Posters and Panels from the 15th International Congress in Nursing Informatics
Journal name in source: Studies in health technology and informatics
Journal acronym: Stud Health Technol Inform
Series title: Studies in Health Technology and Informatics
Volume: 284
First page : 333
Last page: 335
ISSN: 0926-9630
eISSN: 1879-8365
DOI: https://doi.org/10.3233/SHTI210737
Web address : https://ebooks.iospress.nl/doi/10.3233/SHTI210737
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/176265723
Current 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.
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