AI RESEARCH
Modeling Parkinson's Disease Progression Using Longitudinal Voice Biomarkers: A Comparative Study of Statistical and Neural Mixed-Effects Models
arXiv CS.LG
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ArXi:2507.20058v3 Announce Type: replace-cross Predicting Parkinson's Disease (PD) progression is crucial for personalized treatment, and voice biomarkers offer a promising non-invasive method for tracking symptom severity through telemonitoring. However, analyzing this longitudinal data is challenging due to inherent within-subject correlations, the small sample sizes typical of clinical trials, and complex patient-specific progression patterns.