Faculty Opinions recommendation of Discovery of Parkinson's disease states and disease progression modelling: a longitudinal data study using machine learning.

Author(s):  
Honghuang Lin
2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Mohammad R. Salmanpour ◽  
Mojtaba Shamsaei ◽  
Ghasem Hajianfar ◽  
Hamid Soltanian-Zadeh ◽  
Arman Rahmim

2021 ◽  
Author(s):  
Robin Vlieger ◽  
Elena Daskalaki ◽  
Deborah Apthorp ◽  
Christian J Lueck ◽  
Hanna Suominen

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.


2020 ◽  
Vol 13 (5) ◽  
pp. 508-523 ◽  
Author(s):  
Guan‐Hua Huang ◽  
Chih‐Hsuan Lin ◽  
Yu‐Ren Cai ◽  
Tai‐Been Chen ◽  
Shih‐Yen Hsu ◽  
...  

2021 ◽  
Vol 81 ◽  
pp. 307-311 ◽  
Author(s):  
Claudio Liguori ◽  
Valentino De Franco ◽  
Rocco Cerroni ◽  
Matteo Spanetta ◽  
Nicola Biagio Mercuri ◽  
...  

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