用物理约束神经算子从非侵入信号推断血流动力学，解决尺度对称性难题。
HIPNO: Symmetry-Aware Physics-Informed Neural Operators for Noninvasive Hemodynamic Inference
- 基于物理规律设计对称性感知的神经算子，解耦血流与阻力等关键变量。
- 在超94万例术中数据上，压力波衰减时间预测误差降低32%。
- 输出可解释坐标，适合临床监护与心脏输出校准，支持真实物理量恢复。
连续血流动力学监测对手术和重症治疗至关重要，但金标准信号因侵入性风险仅用于危重病例。本文提出HIPNO（通过物理信息神经算子进行血流动力学推断），从常见非侵入信号重建血流动力学状态，拓展先进监测的可及性。针对物理信息建模中的尺度对称性问题——不同流量、阻力与顺应性组合产生相同压力观测——我们识别观测模型的对称群，并将网络参数化于商空间。对于三元件风琴模型，商坐标为顺应性归一化流量 $U=Q/C$、衰减时间常数 $τ_{WK}=R_2 C$、特征阻抗坐标 $κ=R_1 C$。在2562名患者共945,499个术中窗口的数据上，HIPNO对压力波衰减时间 $τ_{wave}$ 的对数尺度误差比群体基线低32%，同时保持平均动脉压精度。由于血管衰减与流量驱动占据独立坐标，反事实扰动在几乎所有预设场景中均产生预期方向响应，占比达90%以上，而仅依赖压力的基线无法实现此分离。这些坐标还被用于校准监测的心输出量模型。最后，该框架明确了恢复绝对物理尺度所需的外部顺应性或流量参考。
原文摘要 · Abstract (English)
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.
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