HIPNO：用于无创血流动力学推断的对称感知物理信息神经算子
HIPNO: Symmetry-Aware Physics-Informed Neural Operators for Noninvasive Hemodynamic Inference
浏览论文内容
中文总结 AI 辅助
本研究提出HIPNO，通过利用3元件Windkessel模型的商空间参数化网络，从无创信号推断血流动力学，在945499个术中窗口中预测τ_wave的对数误差较基线低32%，实现了血管衰减与流量驱动的坐标分离。
中文摘要 AI 辅助
连续血流动力学监测指导手术和重症监护中的治疗决策，但由于侵入式测量存在风险，仅在重症病例中测量金标准信号。本研究提出HIPNO（Hemodynamic Inference via Physics-informed Neural Operators，基于物理信息神经算子的血流动力学推断），以从普遍存在的无创信号中恢复血流动力学状态，扩大高级监测的可及性。HIPNO解决了物理信息血流动力学推断中的尺度对称问题，其中流量、阻力和顺应性的不同组合可产生相同的观测压力。我们确定了观测模型的对称群，并在其商空间中参数化网络。对于3元件Windkessel模型，商坐标为顺应性归一化流量U=Q/C、衰减时间常数τ_WK=R₂C以及特征阻抗坐标κ=R₁C。在来自2562名患者的945499个术中窗口中，HIPNO预测τ_wave（源自压力的血管衰减代理）的对数尺度误差比群体基线低32%，同时保持平均动脉压的准确性。由于血管衰减和流量驱动位于不同坐标中，在几乎所有预设场景下，反事实扰动在至少90%的窗口中产生预期的方向响应，这是仅压力基线无法实现的分离。这些坐标还用作监测心输出量校准模型的输入。最后，该公式确定了恢复绝对物理尺度所需的外部顺应性或流量参考。
英文摘要
Continuous hemodynamic monitoring guides treatment decisions in surgery and intensive care. However, gold-standard signals are only measured in severe cases due to risks associated with invasive measurement. In this work, we introduce HIPNO (Hemodynamic Inference via Physics-informed Neural Operators) to recover hemodynamic state from ubiquitous, non-invasive signals and expand access to advanced monitoring. HIPNO addresses a problem of scale symmetry in physics-informed hemodynamic inference, where different combinations of flow, resistance, and compliance can generate the same observed pressure. We identify the symmetry group of the observation model and parameterize the network in its quotient space. For the 3-element Windkessel model, the quotient coordinates are the compliance-normalized flow $U=Q/C$, the decay time constant $τ_{WK}=R_2 C$, and the characteristic-impedance coordinate $κ=R_1 C$. Across 945499 intraoperative windows from 2562 patients, HIPNO predicts $τ_{wave}$, a proxy for vascular decay derived from pressure, with 32% lower error on the log scale than a population baseline while preserving mean arterial pressure accuracy. Because vascular decay and flow drive occupy separate coordinates, counterfactual perturbations produce the expected directional responses in at least 90% of windows in almost all prespecified scenarios, a separation unavailable to pressure-only baselines. The coordinates are also used as inputs to a calibration model for monitored cardiac output. Finally, the formulation identifies the external compliance or flow reference required to recover absolute physical scale.
发表机构
- UCLA（加州大学洛杉矶分校）
- Scalable Analytics Institute (ScAi)（可扩展分析研究所（ScAi））
机构由 AI 辅助整理，请以论文原文为准。
