# Meta-Weighted Gaussian Process Experts for Personalized Forecasting of AD Cognitive Changes

arXiv: https://arxiv.org/abs/1904.09370 · HTML: https://mlsanity.com/paper/1904.09370/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2019.

## Built on by (most cited first, 3 of 3)

- [Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review](https://mlsanity.com/paper/2108.04174/) (2021)
- [Adaptive Shrinkage Estimation For Personalized Deep Kernel Regression In Modeling Brain Trajectories](https://mlsanity.com/paper/2504.08840/) (2025)
- [Uncertainty-Calibrated Prediction of Randomly-Timed Biomarker Trajectories with Conformal Bands](https://mlsanity.com/paper/2511.13911/) (2025)

## Abstract (arXiv)

We introduce a novel personalized Gaussian Process Experts (pGPE) model for predicting per-subject ADAS-Cog13 cognitive scores -- a significant predictor of Alzheimer's Disease (AD) in the cognitive domain -- over the future 6, 12, 18, and 24 months. We start by training a population-level model using multi-modal data from previously seen subjects using a base Gaussian Process (GP) regression. Then, we personalize this model by adapting the base GP sequentially over time to a new (target) subject using domain adaptive GPs, and also by training subject-specific GP. While we show that these models achieve improved performance when selectively applied to the forecasting task (one performs better than the other on different subjects/visits), the average performance per model is suboptimal. To this end, we used the notion of meta learning in the proposed pGPE to design a regression-based weighting of these expert models, where the expert weights are optimized for each subject and his/her future visit. The results on a cohort of subjects from the ADNI dataset show that this newly introduced personalized weighting of the expert models leads to large improvements in accurately forecasting future ADAS-Cog13 scores and their fine-grained changes associated with the AD progression. This approach has potential to help identify at-risk patients early and improve the construction of clinical trials for AD.