We developed longitudinal machine learning models to predict which patients with mild cognitive impairment will progress to Alzheimer's disease. Using multimodal ADNI data, we found that incorporating patient trajectories modestly improves prediction, but sophisticated deep learning models did not consistently outperform simpler machine learning approaches. Overall, longitudinal information appears to be more important than model complexity for individualized risk prediction.