论文提出干预感知模型，用房颤消融后不规则记录预测复发，DECAAF-II上AUROC 0.756，推理时免随访MRI强度。
提出干预感知临床世界模型，将术后恢复视为随时间展开的不规则轨迹。模型将基线影像编码为3D空间潜在状态，并用手术上下文与生理嵌入更新。在DECAAF-II数据集上，预测房颤消融90天复发的AUROC为0.756、AUPRC为0.777。无需随访MRI强度，疤痕范围MAE为2.971个百分点。潜在状态支持90天内不同时间范围的风险查询与空白期记录编辑。
Intervention-Aware Clinical World Model for Post-Op Outcome Forecasting in Cardiology
Many clinical prediction models treat post-intervention outcomes as a one-step mapping from baseline measurements to a future endpoint. However, recovery after a procedure often unfolds as an irregular trajectory: clinical observations, medication changes, repeat interventions, and physiological measurements are recorded asynchronously and can change risk assessment over time. We propose an intervention-aware clinical world model that represents each patient with a structured latent state and evolves it through time-ordered post-intervention events. The model first encodes baseline imaging into a 3D spatial latent state. It then updates this state using procedural context, static covariates, elapsed time, and peri-event physiological embeddings. Follow-up imaging provides training-only supervision through a latent forecasting objective. We apply the framework to atrial fibrillation ablation. During the 90-day recovery window, irregular post-procedure records provide clinically meaningful evidence for long-term recurrence risk. In repeated internal cross-validation on DECAAF-II, our model achieves AUROC 0.756 and AUPRC 0.777 for recurrence prediction. It also achieves a scar-extent MAE of 2.971 percentage points without requiring follow-up MRI intensities at inference. The learned state supports recurrence-risk queries at different horizons and retrospective input editing of blanking-period records.