Abstract
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.
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Notes
- 1.
MDS-UPDRS is Movement Disorder Society sponsored revision of the Unified Parkinson’s Disease Rating Scale.
- 2.
Parkinson’s Progression Markers Initiative, https://www.ppmi-info.org/.
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Acknowledgment
The research was supported by the Slovenian Research Agency (research core funding programs P2-0209, P6-0411 and P2-0103) and the Slovenian Ministry of Education, Science and Sport (project R 2.1 - Public call for the promotion of researchers at the beginning of a career 2.1). Data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) (www.ppmi-info.org/data). For up-to-date information on the study, visit www.ppmi-info.org.
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Valmarska, A., Lavrač, N., Robnik–Šikonja, M. (2021). Stratification of Parkinson’s Disease Patients via Multi-view Clustering. In: Tucker, A., Henriques Abreu, P., Cardoso, J., Pereira Rodrigues, P., Riaño, D. (eds) Artificial Intelligence in Medicine. AIME 2021. Lecture Notes in Computer Science(), vol 12721. Springer, Cham. https://doi.org/10.1007/978-3-030-77211-6_25
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