Artificial intelligence-based clustering and characterization of Parkinson's disease trajectories.

ABSTRACT: Parkinson's disease (PD) is a highly heterogeneous disease both with respect to arising symptoms and its progression over time. This hampers the design of disease modifying trials for PD as treatments which would potentially show efficacy in specific patient subgroups could be considered ineffective in a heterogeneous trial cohort. Establishing clusters of PD patients based on their progression patterns could help to disentangle the exhibited heterogeneity, highlight clinical differences among patient subgroups, and identify the biological pathways and molecular players which underlie the evident differences. Further, stratification of patients into clusters with distinct progression patterns could help to recruit more homogeneous trial cohorts. In the present work, we applied an artificia

SUBMITTER: Birkenbihl C 

PROVIDER: S-EPMC9938890 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature