Longitudinal Artificial Intelligence for Parkinson’s Disease: Modern Modelling, Feature Selection, and Imputation – A Technical Review and Clinical Roadmap - Supplementary Material

Imputation methods, from simple techniques to deep generative models, are then reviewed, together with an analysis of how imputation choices cascade through downstream modelling, feature selection, and clinical inference. Reported developments include latent-class mixed-effects models reaching an of 0.92 for cognitive-decline prediction in single-cohort Parkinson’s Progression Markers Initiative (PPMI) analyses, multimodal SuStaIn-based subtyping refined with deep learning and multi-omics integration, graph-based personalised progression models achieving area under the receiver operating characteristic curve (AUCs) of 0.71–0.74 on PPMI/PDBP test sets, and multi-objective feature-selection frameworks jointly optimising cost, equity, and interpretability.

Recurring gaps include limited external validation across diverse populations, inconsistent leakage prevention, and weak characterisation of how imputation choices propagate to downstream inference. Priority directions include causal-probabilistic models, uncertainty-driven feature acquisition, federated validation, and transparent imputation protocols with routine fairness auditing, aligned with current reporting standards for systematic reviews and clinical prediction models.</p>