Data-driven subtyping of Parkinson's disease: comparison of current methodologies and application to the Bochum PNS cohort.

ABSTRACT: Considerable efforts have been made to better describe and identify Parkinson's disease (PD) subtypes. Cluster analyses have been proposed as an unbiased development approach for PD subtypes that could facilitate their identification, tracking of progression, and evaluation of therapeutic responses. A data-driven clustering analysis was applied to a PD cohort of 114 subjects enrolled at St. Josef-Hospital of the Ruhr University in Bochum (Germany). A wide spectrum of motor and non-motor scores including polyneuropathy-related measures was included into the analysis. K-means and hierarchical agglomerative clustering were performed to identify PD subtypes. Silhouette and Calinski-Harabasz Score Elbow were then employed as supporting evaluation metrics for determining the optimal number of cl

SUBMITTER: Chen Q 

PROVIDER: S-EPMC10199871 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature