发表机构
PES University（PES大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出药代动力学状态空间模型，利用药物输注历史预测术中低血压，实现AUROC 0.7360，较随机基线提升2.73倍，并验证了药代动力学轨迹的预测价值及Mamba架构的部署优势。
AI 中文摘要
术中低血压（IOH）事件是全身麻醉管理过程中常见的并发症，具有严重的下游后果，然而临床管理仍然是被动反应性的而非预测性的。然而，现有的预测模型忽略了药物输注历史这一对预测具有直接药理学相关性的宝贵信号。我们的模型实现了受试者工作特征曲线下面积（AUROC）为0.7360，精确率-召回率曲线下面积（AUPRC）为0.1794，相比随机猜测的AUPRC基线（0.0657）提升了2.73倍；移除丙泊酚和瑞芬太尼效应室浓度后，与完整模型相比AUPRC下降了13.9%。这与药代动力学轨迹在平均动脉压（MAP）中显现之前编码了即将发生的血流动力学变化的假设一致。此外，本文表明，在没有前导间隙过滤的情况下进行训练使AUROC降低了16.7%，实证确认了未过滤的模型学习到的是检测正在进行的低血压而非预测未来事件。最后，基于Mamba的架构在实现上述高预测性能的同时，在一系列序列长度上保持了恒定的内存占用，不同于典型Transformer的二次方VRAM开销，使其成为连续术中部署的更实用选择。
英文摘要
An Intraoperative Hypotension (IOH) event is a frequent complication during administration of general anaesthesia with serious downstream consequences, yet clinical management remains reactive and not predictive. Existing predictive models, however, ignore drug infusion history as a valuable signal for prediction despite its direct pharmacological relevance. Our model achieves an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.7360 and an Area Under the Precision-Recall Curve (AUPRC) of 0.1794, representing a 2.73-fold lift over the random guessing AUPRC baseline (0.0657), with the removal of propofol and remifentanil effect-site concentrations resulting in a 13.9% AUPRC drop compared to the full model. This is consistent with the hypothesis that pharmacokinetic trajectories encode impending haemodynamic changes before they manifest in the Mean Arterial Pressure (MAP). Additionally, this paper shows that training without lead-gap filtering degraded AUROC by 16.7%, empirically confirming that unfiltered models learn to detect ongoing hypotension rather than predict future events. Finally, a Mamba-based architecture achieves the aforementioned high prediction performance while maintaining a constant memory footprint across a range of sequence lengths, unlike the quadratic VRAM overhead typical of vanilla Transformers, making it the more practical choice for continuous intraoperative deployment.
CommentsPublished in Springer Nature after presenting at the International Conference on AI in Healthcare, London
DOI：10.1007/978-3-032-35387-0_22
