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. 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 statistical baseline of Generalized Additive Mixed Models (GAMMs). Using the Oxford Parkinson's telemonitoring voice dataset (), 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.

翻译：预测帕金森病（PD）进展对个性化治疗至关重要，而语音生物标志物通过远程监测为追踪症状严重程度提供了一种有前景的非侵入性方法。然而，由于固有的受试者内相关性、临床试验中典型的小样本量以及复杂的患者特异性进展模式，分析这些纵向数据具有挑战性。尽管深度学习具有较高的理论灵活性，但与传统的统计方法相比，其在小型队列纵向研究中的应用仍探索不足。本研究将神经混合效应（NME）框架应用于帕金森远程监测，并将其与广义神经网络混合模型（GNMM）及半参数统计基线——广义可加混合模型（GAMM）进行基准比较。利用牛津帕金森远程监测语音数据集（），我们证明，尽管神经网络架构具有灵活性，但在小样本情况下，它们容易出现过拟合。我们的结果表明，GAMM提供了最优平衡，相比神经基线（MSE > 90）实现了更优的预测精度（MSE 6.56），同时保持了临床可解释性。我们讨论了这些发现对于在数据稀缺条件下开发稳健且可部署的远程监测系统的关键意义，强调了使用更大、更多样化的数据集进行神经模型验证的必要性。