Combining Multitask Learning and Short Time Series Analysis in Parkinson’s Disease Patients Stratification
Quality of life of patients with Parkinson’s disease degrades significantly with disease progression. This paper presents a step towards personalized medicine management of Parkinson’s disease patients, based on discovering groups of similar patients. Similarity is based on patients’ medical conditions and changes in the prescribed therapy when the medical conditions change. The presented methodology combines multitask learning using predictive clustering trees and short time series analysis to better understand when a change in medications is required. The experiments on PPMI (Parkinson Progression Markers Initiative) data demonstrate that using the proposed methodology we can identify some clinically confirmed patients’ symptoms suggesting medications change.
KeywordsDopamine Agonist Medication Group Antiparkinson Medication Therapy Modification Multitask Learning
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 the financial support from the Slovenian Research Agency (research core fundings No. P2-0209 and P2-0103). This research has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No. 720270 (HBP SGA1). The data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (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. List of funding partners can be found at www.ppmi-info.org/fundingpartners.
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