Multi-dimensional attention framework for personalised Alzheimer's disease progression prediction across sporadic and genetic risk cohorts

Abstract

Alzheimer's disease progresses heterogeneously across diverse cohorts, yet current predictive models fail to capture this complexity while remaining clinically interpretable. Here we present a multi-dimensional attention framework that simultaneously captures both temporal dynamics and biomarker importance to predict disease progression across three fundamentally different populations: the general late-onset population using the Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) dataset (N=1669), cases with Down Syndrome-associated Alzheimer's disease using the Alzheimer's Biomarker Consortium - Down Syndrome (ABC-DS) dataset (N=396), and cases with autosomal dominant Alzheimer's disease using the Dominantly Inherited Alzheimer Network (DIAN) dataset (N=425). Trained on each dataset independently, our framework achieved multi-class Area Under the Receiver Operating Characteristic Curve (mAUC) values of 0.793 (TADPOLE), 0.680 (ABC-DS), and 0.902 (DIAN) when predicting individuals' future diagnostic status (cognitively normal/stable, mild cognitive impairment, or Alzheimer's disease) from their longitudinal biomarker history, outperforming conventional approaches. The model generates individual-specific attention maps revealing distinct biomarker importance over time. Transfer learning from TADPOLE—which included neuroimaging data—improved prediction performance on the imaging-free ABC-DS dataset from 0.680 to 0.771, demonstrating that disease mechanisms transcend both etiological boundaries and data modalities. Ultimately, this framework could enable precision medicine approaches for data-limited cohorts across the Alzheimer's disease spectrum.