Assessing the significance of longitudinal data in Alzheimer's Disease forecasting

TL;DR

This paper demonstrates that incorporating extended longitudinal patient data using a transformer model significantly improves the accuracy of forecasting Alzheimer's Disease progression over five years.

Contribution

The study introduces LongForMAD, a transformer-based model that effectively utilizes multimodal longitudinal data to predict Alzheimer's progression, highlighting the importance of historical patient information.

Findings

Longitudinal data improves prediction accuracy.

Extended patient histories outperform single-visit models.

Model performs well across different patient groups.

Abstract

In this study, we employ a transformer encoder model to characterize the significance of longitudinal patient data for forecasting the progression of Alzheimer's Disease (AD). Our model, Longitudinal Forecasting Model for Alzheimer's Disease (LongForMAD), harnesses the comprehensive temporal information embedded in sequences of patient visits that incorporate multimodal data, providing a deeper understanding of disease progression than can be drawn from single-visit data alone. We present an empirical analysis across two patient groups-Cognitively Normal (CN) and Mild Cognitive Impairment (MCI)-over a span of five follow-up years. Our findings reveal that models incorporating more extended patient histories can outperform those relying solely on present information, suggesting a deeper historical context is critical in enhancing predictive accuracy for future AD progression. Our results support the incorporation of longitudinal data in clinical settings to enhance the early detection and monitoring of AD. Our code is available at \url{https://github.com/batuhankmkaraman/LongForMAD}.