Stratification of Parkinson’s Disease Patients via Multi-view Clustering

Parkinson’s disease is a neurodegenerative disease characterised by heterogeneity of the sets of symptoms patients experience and the trajectories of disease progression. The PPMI study includes patients’ symptoms explaining different aspects of patients’ life, i.e. motor, non-motor, and autonomic symptoms. This paper proposes a multi-view clustering approach for determining groups of Parkinson’s disease patients from the PPMI study with distinct disease trajectories over 4 years. The proposed multi-view clustering approach searches groups of patients who share similar disease progression trajectories over multiple types of symptoms. We detected two groups of patients with different disease progression trajectories and significant differences in severity of motor, non-motor, and autonomic symptoms. On the other hand, while we did not detect any significant differences between the patients from the two groups based on their demographics, medications treatment or their disease types, we identified over-sensitivity to bright light as a possible early screening symptom for type of disease progression.