A Comprehensive Multifunctional Approach for Measuring Parkinson's Disease Severity

This research study aims to advance the staging of Parkinson's disease (PD) by incorporating machine learning to assess and include a broader multifunctional spectrum of neurocognitive symptoms in the staging schemes beyond motor-centric assessments. Specifically, we provide a novel framework to modernize and personalize PD staging more objectively by proposing a hybrid feature scoring approach.

We recruited 37 individuals diagnosed with PD, each of whom completed a series of tablet-based neurocognitive tests assessing motor, memory, speech, executive functions, and tasks ranging in complexity from single to multifunctional. Then, the collected data were used to develop a hybrid feature scoring system to calculate a weighted vector for each function. We evaluated the current PD staging schemes and developed a new approach based on the features selected and extracted using random forest and principal component analysis.

Our findings indicate a substantial bias in current PD staging systems toward fine motor skills, that is, other neurological functions (memory, speech, executive function, etc.) do not map into current PD stages as well as fine motor skills do. The results demonstrate that a more accurate and personalized assessment of PD severity could be achieved by including a more exhaustive range of neurocognitive functions in the staging systems either by involving multiple functions in a unified staging score or by designing a function-specific staging system.

The proposed hybrid feature score approach provides a comprehensive understanding of PD by highlighting the need for a staging system that covers various neurocognitive functions. This approach could potentially lead to more effective, objective, and personalized treatment strategies. Further, this proposed methodology could be adapted to other neurodegenerative conditions such as Alzheimer's disease or amyotrophic lateral sclerosis.