Personalized progression modelling and prediction in Parkinson's disease with a novel multi-modal graph approach.

ABSTRACT: Parkinson's disease (PD) is a complex neurological disorder characterized by dopaminergic neuron degeneration, leading to diverse motor and non-motor impairments. This variability complicates accurate progression modelling and early-stage prediction. Traditional classification methods based on clinical symptoms are often limited by disease heterogeneity. This study introduces an graph-based interpretable personalized progression method, utilizing data from the Parkinson's Progression Markers Initiative (PPMI) and Stroke Parkinson's Disease Biomarker Program (PDBP). Our approach integrates multimodal inter-individual and intra-individual data, including clinical assessments, MRI, and genetic information to make multi-dimension predictions. Validated using the PDBP dataset from 12 to 36 mont

SUBMITTER: Lian J 

PROVIDER: S-EPMC11609293 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature