Predicting Parkinson's Disease Progression Using Statistical and Neural Mixed Effects Models: A Comparative Study on Longitudinal Biomarkers

• URL: http://arxiv.org/abs/2507.20058v1
• Date: Sat, 26 Jul 2025 20:56:32 GMT
• Title: Predicting Parkinson's Disease Progression Using Statistical and Neural Mixed Effects Models: A Comparative Study on Longitudinal Biomarkers
• Authors: Ran Tong, Lanruo Wang, Tong Wang, Wei Yan,
• Abstract summary: Predicting Parkinson's Disease (PD) progression is crucial, and voice biomarkers offer a non-invasive method for tracking symptom severity (UPDRS) through telemonitoring.<n>This study benchmarks LMMs against two advanced hybrid approaches.<n>We evaluate these models' performance in predicting Total UPDRS to offer practical guidance for PD research and clinical applications.
• Score: 7.953527165578974
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Predicting Parkinson's Disease (PD) progression is crucial, and voice biomarkers offer a non-invasive method for tracking symptom severity (UPDRS scores) through telemonitoring. Analyzing this longitudinal data is challenging due to within-subject correlations and complex, nonlinear patient-specific progression patterns. This study benchmarks LMMs against two advanced hybrid approaches: the Generalized Neural Network Mixed Model (GNMM) (Mandel 2021), which embeds a neural network within a GLMM structure, and the Neural Mixed Effects (NME) model (Wortwein 2023), allowing nonlinear subject-specific parameters throughout the network. Using the Oxford Parkinson's telemonitoring voice dataset, we evaluate these models' performance in predicting Total UPDRS to offer practical guidance for PD research and clinical applications.

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