Parkinson disease subtypes based on short time series and multi-view clusteringKRALJEVSKA, MELANIJA (Avtor) Robnik Šikonja, Marko (Mentor) Valmarska, Anita (Komentor) machine learningdata scienceclusteringParkinson's diseasemulti-view learningskip n-gramsParkinson's disease (PD) is a progressive brain disorder which is characterized by movement problems such as tremor, stiffness, slowness of movement and dizziness, as well as non-motor symptoms, which include sleep disorders, constipation, problems concentrating, depression and emotional changes. Due to the clinical heterogeneity of PD, the existence of subtypes of PD patients has been addressed in many clinical and research studies and may contribute to a more personalized treatment and improved quality of life. We apply a methodology for discovering PD patient subtypes to patient data from the Fox Insight study (FI). The data sets are composed from questionnaires, containing patient symptoms and medication data collected through routine study visits. Dividing patients in subtypes can be translated to a problem of clustering time series data. We address this problem by using single-view clustering with k-means algorithm and multi-view spectral clustering. We describe the obtained subtypes with decision rules. Understanding decision making is crucial in medicine and we use decision trees as simple, explainable tools for describing subtypes. An important part of managing the disease is understanding the disease progression. By observing the patient's subtype changes between consecutive visits with skip-grams, we analyze the disease progression.20212021-03-02 10:05:00Diplomsko delo/naloga125035sl