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
https://github.com/batuhankmkaraman/LongForMAD.

利用变压器编码器模型来表征纵向患者数据的重要性，以预测阿尔茨海默病（AD）的进展。我们的模型 LongForMAD 利用包含多模态数据的患者访问序列中嵌入的全面时间信息，提供对疾病进展的更深入理解，比仅基于单次访问数据获得的理解更有效。我们在两个患者群体（认知正常和轻度认知障碍）的随访五年内进行了经验分析。实验结果显示，结合更长时间的患者病史的模型可以胜过仅依赖当前信息的模型，这表明更深入的历史背景对于提高未来 AD 进展的预测准确性至关重要。我们的研究结果支持在临床环境中利用纵向数据来增强 AD 的早期检测和监测。