Digital Phenotyping of Parkinson's Disease via Natural Language Processing

Frontostriatal degeneration in Parkinson's disease (PD) is associated with language deficits, which can be identified using natural language processing, a remarkable tool for digital phenotyping. Current evidence is limited in linguistic coverage and mostly blind to the disorder's cognitive phenotypes. We validated an AI-driven approach to capture digital language markers of PD with and without mild cognitive impairment (PD-MCI, PD-nMCI) relative to healthy controls (HCs). Analyzing the connected speech samples of participants, we extracted linguistic features with CLAN software. Classification was performed using Support Vector Machine and Recursive Feature Elimination. Discrimination between PD and HCs reached an AUC of 77%, with even better results for subgroup analyses (AUC 85% PD-nMCI vs. HCs; 83% PD-MCI vs. HCs; 75% PD-nMCI vs. PD-MCI). Key linguistic features included retracing ratio, action verb ratio, utterance error ratio, and verbless-utterance ratio, highlighting the foundational capabilities of linguistic digital markers for early diagnosis and phenotyping of PD.