[论文] Intervention-Aware Clinical World Model for Post-Op Outcome Forecastin...

论文概要

研究领域: CV
作者: Yunsung Chung, Yingshuo Liu, Abboud F. Hassan, Han Feng, Mary M. Maleckar, Nassir Marrouche, Jihun Hamm
发布时间: 2026-08-13
arXiv: 2608.13518

中文摘要

许多临床预测模型将干预后结果视为从基线测量到未来终点的一步映射。然而，手术后的恢复通常表现为不规则轨迹：临床观察、药物变化、重复干预和生理测量被异步记录，并可以随时间改变风险评估。我们提出一种干预感知的临床世界模型，用结构化潜状态表示每个患者，并通过时间排序的干预后事件演化该状态。模型首先将基线影像编码为3D空间潜状态。然后使用程序上下文、静态协变量、经过时间和事件周围生理嵌入更新该状态。随访影像通过潜预测目标提供仅训练监督。我们将该框架应用于心房颤动消融。在90天恢复窗口期内，不规则的术后记录为长期复发风险提供临床有意义的证据。在DECAAF-II的重复内部交叉验证中，我们的模型达到复发预测AUROC 0.756和AUPRC 0.777。它还达到疤痕范围MAE 2.971个百分点，而无需在推理时随访MRI强度。学习到的状态支持不同时间范围的复发风险查询和空白期记录的回顾性输入编辑。

原文摘要

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.

自动采集于 2026-08-15

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