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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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Date

2021

Authors

Vlieger, Robin
Daskalaki, Eleni
Apthorp, Deborah
Lueck, Christian
Suominen, Hanna

Journal Title

Journal ISSN

Volume Title

Publisher

IOS Press

Abstract

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.

Description

Keywords

Diagnosis, event-related potentials, machine learning, Parkinson disease, disease status, prediction

Citation

URI

http://hdl.handle.net/1885/312392

Collections

ANU Research Publications

Source

Nurses and Midwives in the Digital Age

Type

Conference paper

Book Title

Entity type

Access Statement

Open Access

License Rights

Creative Commons Attribution 4.0 International License

DOI

10.3233/SHTI210737

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SHTI-284-SHTI210737.pdf (114.71 KB)

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