Beyond Motor Symptoms: Toward a Comprehensive Grading of Parkinson's Disease Severity
Résumé
This study applies machine learning (ML) feature analysis to an array of multi-functional neurocognitive symptoms specific to in- dividuals with Parkinson’s Disease (PD). We provide a framework that can assist with modernizing and objectively individualizing the staging of PD. For that purpose, a hybrid feature score technique is proposed to compute a weighted vector for neurocognitive func- tions. The methodology is based on Principal Component Analysis and Random Forest for feature selection and extraction purposes. The study enrolled 37 participants who completed various tablet- based functional neurocognitive assessments for motor, memory, speech, executive function, and single versus multi-functional tasks. The study concludes that current assessment and staging schemes exhibit a significant bias toward fine-motor functionalities. Thus, the inclusion of other neurocognitive functions is essential for accu- rately identifying disease stages. This could be achieved through the integration of multiple functions into a unified score or by adopting function-specific staging. By incorporating ML into disease staging, a more comprehensive understanding of neurocognitive disorders can be obtained, revealing novel insights that affect the design and implementation of staging schemes.
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