研究人员开发了一个干预感知的临床世界模型，用于预测心脏病术后结果。该模型通过一个结构化的潜在状态来表示患者，该状态基于异步临床事件、药物变化和干预措施随时间演变。应用于房颤消融，该模型在预测90天内复发风险方面表现强劲，在DECAAF-II数据集上达到了0.756的AUROC和0.777的AUPRC。该框架还能准确估计瘢痕范围，并可在不同的未来时间点查询风险。 AI
影响 该模型通过为心脏病术后结果提供更准确和动态的风险评估，有可能改善患者护理。
排序理由 该集群包含一篇详细介绍用于医学预测的新模型的学术论文。
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arXiv:2608.13518v1 Announce Type: new Abstract: 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 observati…
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, a…