面向心脏病术后结局预测的干预感知临床世界模型

中文摘要 AI 辅助

许多临床预测模型将干预后结局视为从基线测量到未来终点的一步映射，但术后恢复常呈现不规则轨迹：临床观察、药物调整、重复干预及生理测量异步记录，会随时间改变风险评估。我们提出一种干预感知临床世界模型，用结构化潜态表示每位患者，并通过时序干预后事件演化该状态。该模型首先将基线成像编码为3D空间潜态，再利用手术背景、静态协变量、经过时间及事件周围生理嵌入更新此状态；随访成像通过潜态预测目标提供仅训练用监督。我们将该框架应用于心房颤动消融，在90天恢复窗口内，术后不规则记录为长期复发风险提供临床有意义的证据。在DECAAF-II上的重复内部交叉验证中，我们的模型在复发预测上取得AUROC 0.756、AUPRC 0.777，且在推理时无需随访MRI强度，疤痕范围平均绝对误差（MAE）达2.971个百分点。学习到的状态支持不同时间范围的复发风险查询及空白期记录的回溯性输入编辑。

英文摘要

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.

发表机构

• Tulane University（杜兰大学）
• Simula Research Laboratory（Simula研究实验室）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

备注

Medical World Models (MWM) Workshop at MICCAI 2026