Data-Driven Subtyping of Parkinson's Disease Using Longitudinal Clinical Records: A Cohort Study.

ABSTRACT: Parkinson's disease (PD) is associated with diverse clinical manifestations including motor and non-motor signs and symptoms, and emerging biomarkers. We aimed to reveal the heterogeneity of PD to define subtypes and their progression rates using an automated deep learning algorithm on the top of longitudinal clinical records. This study utilizes the data collected from the Parkinson's Progression Markers Initiative (PPMI), which is a longitudinal cohort study of patients with newly diagnosed Parkinson's disease. Clinical information including motor and non-motor assessments, biospecimen examinations, and neuroimaging results were used for identification of PD subtypes. A deep learning algorithm, Long-Short Term Memory (LSTM), was used to represent each patient as a multi-dimensional time

SUBMITTER: Zhang X 

PROVIDER: S-EPMC6349906 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature