Integrative Biomedical Research
Integrative Biomedical Research (Journal of Angiotherapy) | Online ISSN 3068-6326
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Volume 10 Number 1 2026
RESEARCH ARTICLE (Open Access)
Predicting Parkinson’s Disease Progression Using Statistical And Neural Mixed Effects Models: Comparative Study On Longitudinal Biomarkers
Ran Tong 1*, Lanruo Wang 2, Tong Wang 3, Wei Yan 4
Integrative Biomedical Research 10 (1) 1-18 https://doi.org/10.25163/biomedical.10110648
Submitted: 17 January 2026 Revised: 18 February 2026 Accepted: 26 February 2026 Published: 27 February 2026
Abstract
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 telemon- itoring. 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 progres- sion patterns. While deep learning offers high theoretical flexibility, its application to small-cohort longitudinal studies remains under-explored compared to traditional statistical methods. This study presents an application of the Neural Mixed Effects (NME) framework to Parkinson’s telemonitoring, benchmarking it against Generalized Neural Network Mixed Models (GNMM) and semi-parametric Generalized Additive Mixed Models (GAMMs). Using the Oxford Parkinson’s telemonitoring voice dataset (N = 42), we demonstrate that while neural architectures offer flexibility, they are prone to significant overfitting in small-sample regimes. Our results indicate that GAMMs provide the optimal balance, achieving superior predictive accuracy (MSE 6.56) compared to neural baselines (MSE > 90) while maintaining clinical interpretability. We discuss the critical implications of these findings for developing robust, deployable telemonitoring systems where data scarcity is a constraint, highlighting the necessity for larger, diverse datasets for neural model validation.
Keywords: Parkinson’s Disease · Biostatistics · Longitudinal Data · Neural Networks · Artificial Intelligence
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