  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}.
