Admissible Diffusion for Multimodal Interventional Trajectories
This study addresses the lack of causal interventional prediction capability in clinical trajectory generation by proposing the ADMIT framework. This framework integrates irregular multimodal representations with a treatment-conditioned latent diffusion model, innovatively translating medical prior knowledge into explicit physiological constraints. Through state projection and action gating mechanisms, it distinguishes causal hypotheses from constraint satisfaction to generate intervention trajectories aligned with clinical logic. Experimental results demonstrate that the proposed method significantly improves hidden state recovery quality and reduces treatment contrast error. Furthermore, simulation experiments show its effectiveness in predicting tumor volume changes, thereby enhancing the credibility of counterfactual intervention predictions.