ParkProfile
Full Title
Temporal Multimodal PD Patients ProfilingDescription
Parkinson’s disease (PD) is a progressive, multifaceted neurodegenerative disorder characterized by substantial variability in its symptoms, progression rates, and treatment responses. This heterogeneity makes accurate prediction and effective clinical management highly challenging. As the field moves toward precision medicine, there is a pressing need for methodologies that can stratify patients into clinically meaningful subgroups, thereby enabling personalized prognosis, treatment planning, and improved quality of life.
This project addresses that need by developing and applying advanced clustering techniques to uncover distinct PD progression subtypes from multimodal patient data. Unlike conventional approaches that often overlook temporal dynamics, this work focuses on trajectory and triclustering-based methods that explicitly account for disease progression over time, temporal coherence, and heterogeneous data sources. By leveraging these methods, the study seeks to construct a nuanced understanding of PD subgroups and their clinical significance.