Prognostic factors of Rapid symptoms progression in patients with newly diagnosed parkinson's disease.

Artificial Intelligence in Medicine
Kostas M TsiourisDimitrios I Fotiadis

Abstract

Tracking symptoms progression in the early stages of Parkinson's disease (PD) is a laborious endeavor as the disease can be expressed with vastly different phenotypes, forcing clinicians to follow a multi-parametric approach in patient evaluation, looking for not only motor symptomatology but also non-motor complications, including cognitive decline, sleep problems and mood disturbances. Being neurodegenerative in nature, PD is expected to inflict a continuous degradation in patients' condition over time. The rate of symptoms progression, however, is found to be even more chaotic than the vastly different phenotypes that can be expressed in the initial stages of PD. In this work, an analysis of baseline PD characteristics is performed using machine learning techniques, to identify prognostic factors for early rapid progression of PD symptoms. Using open data from the Parkinson's Progression Markers Initiative (PPMI) study, an extensive set of baseline patient evaluation outcomes is examined to isolate determinants of rapid progression within the first two and four years of PD. The rate of symptoms progression is estimated by tracking the change of the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS...Continue Reading

Citations

Aug 14, 2020·Neurotherapeutics : the Journal of the American Society for Experimental NeuroTherapeutics·Ronald F Pfeiffer
May 14, 2021·Movement Disorders Clinical Practice·Roongroj Bhidayasiri
Aug 31, 2021·Brain Communications·Roberta BiundoAngelo Antonini

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