The residual gap-aware transformer reduces MSE by 13.1% and boosts correlation by 26.4% over a mixed-effects baseline in predicting 24-month CDR-SB change on anchored ADNI data.
Modeling Parkinson's disease progression from longitudinal voice biomarkers: A comparative study of statistical and neural mixed effects models
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Forecasting Medium-Horizon Alzheimer's Disease Progression: Residual Gap-Aware Transformers for 24-Month CDR-SB Change from ADNI Clinical and Biomarker Histories
The residual gap-aware transformer reduces MSE by 13.1% and boosts correlation by 26.4% over a mixed-effects baseline in predicting 24-month CDR-SB change on anchored ADNI data.