Uncertainty-Aware Longitudinal Forecasting of Alzheimer's Disease Progression Using Deep Learning

• URL: http://arxiv.org/abs/2606.24604v1
• Date: Tue, 23 Jun 2026 14:04:27 GMT
• Title: Uncertainty-Aware Longitudinal Forecasting of Alzheimer's Disease Progression Using Deep Learning
• Abstract summary: We propose a probabilistic framework that combines ordinal diagnosis prediction, multi-horizon trajectory generation, and uncertainty estimation.<n>On ADNI, the model outperforms linear, recurrent, and transformer baselines for next-visit diagnosis prediction.
• Score: 10.269961654607108
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Longitudinal modelling of Alzheimer's disease progression is clinically useful only if it can describe not just the most likely next diagnosis, but how a patient may evolve over time and how reliable that forecast is. Most deep learning approaches reduce this problem to single-step classification, treating cognitively normal, mild cognitive impairment, and dementia as flat categories while providing limited insight into how uncertainty accumulates across future visits. We propose a probabilistic framework that combines ordinal diagnosis prediction, multi-horizon trajectory generation, and decomposed uncertainty estimation. A Temporal Fusion Transformer encoder is adapted with a CORAL ordinal output layer, asymmetric loss weighting, and converter oversampling to respect disease-stage ordering and improve sensitivity to MCI-to-dementia transitions. Conditioned on the learned patient-context representation, an autoregressive Mixture Density Network generates five-year probabilistic trajectories for diagnosis state, CDR Sum of Boxes, MMSE orientation, and hippocampal volume. On ADNI, the model outperforms linear, recurrent, and transformer baselines for next-visit diagnosis prediction, with the strongest gains on MCI-versus-dementia discrimination. Generated trajectories achieve near-nominal 90% credible interval coverage, widening uncertainty across the forecast horizon, and biomarker dynamics consistent with expected Alzheimer's disease progression. We further separate aleatoric from epistemic uncertainty using analytic mixture variance and a five-member bootstrap ensemble, which provides the strongest encoder diversity and output-level epistemic signal. Epistemic uncertainty is higher for rare progression archetypes, MCI and dementia patients, and under external evaluation on OASIS-3, where it increases alongside prediction error.

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