# Personalized Gaussian Processes for Future Prediction of Alzheimer's Disease Progression

arXiv: https://arxiv.org/abs/1712.00181 · HTML: https://mlsanity.com/paper/1712.00181/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2017.

## Built on by (most cited first, 4 of 4)

- [No-Regret Algorithms for Private Gaussian Process Bandit Optimization](https://mlsanity.com/paper/2102.12467/) (2021)
- [Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression Modeling](https://mlsanity.com/paper/2207.11846/) (2022)
- [Uncovering Bias in Personal Informatics](https://mlsanity.com/paper/2303.15592/) (2023)
- [Reconstructing and forecasting disease trajectories of patients with Alzheimer's disease using routine data in resource-constrained settings](https://mlsanity.com/paper/2606.07798/) (2026)

## Abstract (arXiv)

In this paper, we introduce the use of a personalized Gaussian Process model (pGP) to predict the key metrics of Alzheimer's Disease progression (MMSE, ADAS-Cog13, CDRSB and CS) based on each patient's previous visits. We start by learning a population-level model using multi-modal data from previously seen patients using the base Gaussian Process (GP) regression. Then, this model is adapted sequentially over time to a new patient using domain adaptive GPs to form the patient's pGP. We show that this new approach, together with an auto-regressive formulation, leads to significant improvements in forecasting future clinical status and cognitive scores for target patients when compared to modeling the population with traditional GPs.