The Use of Event-Related Potentials and Machine Learning to Improve Diagnostic Testing and Prediction of Disease Progression in Parkinson's Disease
: Vlieger Robin, Daskalaki Elena, Apthorp Deborah, Lueck Christian J, Suominen Hanna
: Michelle Honey, Charlene Ronquillo, Ting-Ting Lee, Lucy Westbrooke
: International Congress in Nursing Informatics
: 2021
: Studies in Health Technology and Informatics
: Nurses and Midwives in the Digital Age: Selected Papers, Posters and Panels from the 15th International Congress in Nursing Informatics
: Studies in health technology and informatics
: Stud Health Technol Inform
: Studies in Health Technology and Informatics
: 284
: 333
: 335
: 0926-9630
: 1879-8365
DOI: https://doi.org/10.3233/SHTI210737
: https://ebooks.iospress.nl/doi/10.3233/SHTI210737
: 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.
