Uncertainty-aware personalized estimation of Parkinsons disease severity from longitudinal speech

Parkinsons disease is a progressive neurological disorder characterized by motor impairments whose severity is commonly assessed using the Unified Parkinsons Disease Rating Scale (UPDRS). Although clinically established, UPDRS assessment requires in-person evaluation by trained specialists and is inherently subjective, limiting its suitability for frequent monitoring. Speech production is affected early in Parkinsons disease and provides a non-invasive modality for remote symptom assessment. In this study, an uncertainty-aware personalized framework is proposed for estimating Parkinsons disease severity from speech signals. The approach integrates longitudinal temporal modeling of longitudinal speech recordings with patient-specific representations and a probabilistic latent disease state. Continuous motor UPDRS scores are estimated jointly with ordinal disease severity stages, enabling both fine-grained regression and clinically interpretable stratification. Predictive uncertainty is explicitly quantified, yielding confidence-aware severity estimates suitable for telemonitoring applications. The method is evaluated on a longitudinal speech dataset using a strict patient-wise split, ensuring that all test subjects are unseen during training. On the held-out test set, the proposed model achieves high predictive accuracy (mean absolute error 0.56 UPDRS points, root mean squared error 0.74, and coefficient of determination R2 = 0.99) for motor UPDRS estimation. Ordinal severity classification attains an accuracy of 0.92 across mild, moderate, and severe disease stages. Comparative experiments against classical machine learning methods and global temporal baselines demonstrate consistent performance improvements.These results indicate that personalized, uncertainty-aware modeling of speech signals can support accurate and clinically meaningful remote monitoring of Parkinsons disease severity.