A dynamic risk prediction framework for Alzheimer's disease and related dementias with interpretability.

Abstract

Alzheimer's disease and related dementias (ADRD) develop years before symptoms emerge, making early prediction critical. Electronic health records (EHR) offer a scalable alternative to neuroimaging but are challenged by irregular encounters, data sparsity, and limited interpretability. We propose Gated Recurrent Unit with Decay & Attention (GRU-DA), which integrates GRU-D missingness modeling with RETAIN-style attention for interpretable risk monitoring. The model was trained on the University of Texas Physicians EHR (15,172 ADRD cases with 1:10 matched controls) and externally validated in the All of Us cohort. EHR data up to 10 years before diagnosis were used, with random follow-up initiation to reflect real-world encounters. Both GRU-DA and GRU-D outperformed competing models, particularly beyond 5 years of follow-up and achieved AUROC ~ 0.7 after 8.5 years. Prediction performance depended more on data availability than follow-up length: 1 year with 15% data availability (AUROC 0.75, Average Precision 0.5) was comparable to 7.5 years with 10% availability. For individual cases, GRU-DA produced stable risk predictions with some variability in timestep and feature-level attributions across folds. These results demonstrate EHR data can support dynamic ADRD risk monitoring up to 10 years before diagnosis, with effectiveness strongly influenced by data completeness.