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 as improved, individualized predictions of the disease course. We used unsupervised and supervised machine learning methods on comprehensive, longitudinal clinical data from the Parkinson's Disease Progression Marker Initiative (n = 294 cases) to identify patient subtypes and to predict disease progression. The resulting models were validated in an independent, clinically well-characterized cohort from the Parkinson's Disease Biomarker Program (n = 263 cases). Our analysis distinguished three distinct dis

SUBMITTER: Dadu A 

PROVIDER: S-EPMC9758217 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature