Identification and prediction of Parkinson’s disease subtypes and progression using machine learning in two cohorts

Identification and prediction of Parkinson’s disease subtypes and progression using machine learning in two cohorts

Abstract The clinical manifestations of Parkinson’s disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor features. There is an unmet need for the characterization of distinct disease subtypes as well a...

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Main Authors: Anant Dadu, Vipul Satone, Rachneet Kaur, Sayed Hadi Hashemi, Hampton Leonard, Hirotaka Iwaki, Mary B. Makarious, Kimberley J. Billingsley, Sara Bandres‐Ciga, Lana J. Sargent, Alastair J. Noyce, Ali Daneshmand, Cornelis Blauwendraat, Ken Marek, Sonja W. Scholz, Andrew B. Singleton, Mike A. Nalls, Roy H. Campbell, Faraz Faghri
Format: Article
Language:English
Published: Nature Portfolio 2022-12-01
Series:npj Parkinson's Disease
Online Access:https://doi.org/10.1038/s41531-022-00439-z

Internet

https://doi.org/10.1038/s41531-022-00439-z

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