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

TL;DR

This paper presents a personalized Gaussian Process model that adapts to individual patients over time to improve the accuracy of predicting Alzheimer's disease progression using cognitive assessment scores.

Contribution

The study introduces a novel personalized Gaussian Process approach that adapts to individual patients, enhancing forecasting accuracy of AD progression over multiple future time points.

Findings

Combining pGP with tGP significantly improves prediction accuracy.

Personalized models outperform population-level models.

The approach effectively predicts ADAS-Cog13 scores up to 24 months ahead.

Abstract

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.