Attention mechanisms can effectively estimate personalized treatment benefits in survival analysis by learning to aggregate pairwise patient comparisons, especially when standard methods struggle with nonlinear treatment effects.
This paper introduces Surv-IPTB, an attention-based model that estimates the probability individual patients will benefit from a specific treatment using survival data.