Visualization and Analysis of Parkinson’s Disease Status and Therapy Patterns
Parkinson’s disease is a neurodegenerative disease affecting people worldwide. Since the reasons for Parkinson’s disease are still unknown and currently there is no cure for the disease, the management of the disease is directed towards handling of the underlying symptoms with antiparkinson medications. In this paper, we present a method for visualization of the patients’ overall status and their antiparkinson medications therapy. The purpose of the proposed visualization method is multi-fold: understanding the clinicians’ decisions for therapy modifications, identification of the underlying guidelines for management of Parkinson’s disease, as well as identifying treatment differences between groups of patients. The resulting patterns of disease progression show that there are differences between male and female patients.
KeywordsData mining Parkinson’s disease Disease progression Therapy modifications Visualization
This work was supported by the PD_manager project, funded within the EU Framework Programme for Research and Innovation Horizon 2020 grant 643706. We acknowledge also the support of the Slovenian Research Agency (research core funding program P2-0103 and project P2-0209).
Data used in the preparation of this article were obtained from the Parkinsons Progression Markers Initiative (PPMI) (www.ppmi-info.org/data). For up-to-date information on the study, visit www.ppmi-info.org. PPMI—a public-private partnership—is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners. Corporate Funding Partners: AbbVie, Avid Radiopharmaceuticals, Biogen, BioLegend, Bristol-Myers Squibb, GE Healthcare, GLAXOSMITHKLINE (GSK), Eli Lilly and Company, Lundbeck, Merck, Meso Scale Discovery (MSD), Pfizer Inc, Piramal Imaging, Roche, Sanofi Genzyme, Servier, Takeda, Teva, UCB. Philanthropic Funding Partners: Golub Capital. List of funding partners can be also found at www.ppmi-info.org/fundingpartners.
- 1.Blockeel, H., Raedt, L.D., Ramon, J.: Top-down induction of clustering trees. In: Proceedings of the 15th International Conference on Machine Learning, ICML 1998. pp. 55–63 (1998)Google Scholar
- 6.Goetz, C., et al.: Movement disorder society-sponsored revision of the unified Parkinson’s disease rating scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov. Disord. 23(15), 2129–2170 (2008)Google Scholar
- 7.Guthrie, D., Allison, B., Liu, W., Guthrie, L., Wilks, Y.: A closer look at skip-gram modelling. In: Proceedings of the 5th International Conference on Language Resources and Evaluation, LREC 2006. pp. 1–4 (2006)Google Scholar
- 9.Marek, K., et al.: The Parkinson’s progression markers initiative (PPMI). Prog. Neurobiol. 95(4), 629–635 (2011)Google Scholar
- 10.National Collaborating Centre for Chronic Conditions: Parkinson’s Disease: National Clinical Guideline for Diagnosis and Management in Primary and Secondary Care. Royal College of Physicians, London (2006)Google Scholar
- 12.Plaisant, C., Milash, B., Rose, A., Widoff, S., Shneiderman, B.: Lifelines: visualizing personal histories. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 221–227. CHI 1996, ACM, New York, NY, USA (1996)Google Scholar
- 14.Struyf, J., Ženko, B., Blockeel, H., Vens, C., Džeroski, S.: CLUS: User’s Manual (2010)Google Scholar
- 15.Tsanas, A.: Accurate telemonitoring of Parkinsons disease symptom severity using nonlinear speech signal processing and statistical machine learning. Ph.D. thesis, Oxford University, UK (2012)Google Scholar
- 18.Tsanas, A., Little, M.A., McSharry, P.E., Ramig, L.O.: Enhanced classical Dysphonia measures and sparse regression for telemonitoring of Parkinson’s disease progression. In: Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing, ICASSP 2010, pp. 594–597. IEEE (2010)Google Scholar
- 20.Tufte, E.R.: The Visual Display of Quantitative Information. Graphics Press, Cheshire, CT, USA (2001)Google Scholar
- 21.Valmarska, A., Miljkovic, D., Konitsiotis, S., Gatsios, D., Lavrač, N., Robnik-Šikonja, M.: Symptoms and medications change patterns for Parkinson’s disease patients stratification. Artif. Intell. Med. (2018). https://doi.org/10.1016/j.artmed.2018.04.010
- 23.Valmarska, A., Miljkovic, D., Robnik-Šikonja, M., Lavrač, N.: Multi-view approach to Parkinson’s disease quality of life data analysis. In: Proceedings of the International Workshop on New Frontiers in Mining Complex Patterns, pp. 163–178. Springer (2016)Google Scholar