# Personalized Gaussian Processes for Forecasting of Alzheimer's Disease Assessment Scale-Cognition Sub-Scale (ADAS-Cog13)

arXiv: https://arxiv.org/abs/1802.08561 · HTML: https://mlsanity.com/paper/1802.08561/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2018.

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

- [Comparison of single and multitask learning for predicting cognitive decline based on MRI data](https://mlsanity.com/paper/2109.10266/) (2021)
- [Longitudinal Causal Image Synthesis](https://mlsanity.com/paper/2410.17691/) (2024)
- [A Weighted Vision Transformer-Based Multi-Task Learning Framework for Predicting ADAS-Cog Scores](https://mlsanity.com/paper/2508.17613/) (2025)

## Abstract (arXiv)

In this paper, we introduce the use of a personalized Gaussian Process model (pGP) to predict per-patient changes in ADAS-Cog13 -- a significant predictor of Alzheimer's Disease (AD) in the cognitive domain -- using data from each patient's previous visits, and testing on future (held-out) data. We start by learning a population-level model using multi-modal data from previously seen patients using a base Gaussian Process (GP) regression. The personalized GP (pGP) is formed by adapting the base GP sequentially over time to a new (target) patient using domain adaptive GPs. We extend this personalized approach to predict the values of ADAS-Cog13 over the future 6, 12, 18, and 24 months. We compare this approach to a GP model trained only on past data of the target patients (tGP), as well as to a new approach that combines pGP with tGP. We find that the new approach, combining pGP with tGP, leads to large improvements in accurately forecasting future ADAS-Cog13 scores.