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.

翻译：预测帕金森病（PD）的进展至关重要，而语音生物标志物为通过远程监测追踪症状严重程度（UPDRS评分）提供了一种非侵入性方法。由于受试者内部相关性以及复杂、非线性的患者特异性进展模式，分析此类纵向数据具有挑战性。本研究将线性混合模型（LMMs）与两种先进的混合方法进行基准比较：广义神经网络混合模型（GNMM）（Mandel 2021），其在广义线性混合模型（GLMM）结构中嵌入了神经网络；以及神经混合效应（NME）模型（Wortwein 2023），该模型允许在整个网络中引入非线性的受试者特异性参数。利用牛津帕金森病远程监测语音数据集，我们评估了这些模型在预测总UPDRS评分方面的性能，旨在为PD研究和临床应用提供实用指导。