用持续采集的PPG预测临床化验轨迹，实现无创实时监测
Estimating Clinical Lab Test Result Trajectories from PPG using Physiological Foundation Model and Patient-Aware State Space Model -- a UNIPHY+ Approach
- 结合基础模型与患者感知状态空间模型，捕捉长期趋势
- 在两个ICU数据集上，对五项关键指标预测误差显著降低
- 适合重症监护中需连续生理监测的场景
临床实验室检测提供诊断和治疗所需的生化指标，但受限于间断且有创的采样方式。相比之下，光电容积脉搏波（PPG）是重症监护室（ICUs）中一种无创、连续记录的信号，反映心血管动态，可作为潜在生理变化的代理指标。我们提出UNIPHY+Lab框架，将大规模PPG基础模型用于局部波形编码，并结合患者感知的Mamba模型进行长程时间建模。该架构解决三个挑战：（1）捕捉实验室值的长期趋势；（2）通过FiLM调制的初始状态考虑患者个体基线差异；（3）对相关生物标志物进行多任务估计。我们在两个ICU数据集上评估了五项关键实验室检测的预测表现。结果表明，在大多数目标指标上，本方法在平均绝对误差（MAE）、均方根误差（RMSE）和决定系数（R²）方面均显著优于LSTM和前向填充基线。本研究证明了从常规PPG监测中连续、个性化估算生化指标的可行性，为危重症中的无创生化监测提供了新路径。
原文摘要 · Abstract (English)
Clinical laboratory tests provide essential biochemical measurements for diagnosis and treatment, but are limited by intermittent and invasive sampling. In contrast, photoplethysmogram (PPG) is a non-invasive, continuously recorded signal in intensive care units (ICUs) that reflects cardiovascular dynamics and can serve as a proxy for latent physiological changes. We propose UNIPHY+Lab, a framework that combines a large-scale PPG foundation model for local waveform encoding with a patient-aware Mamba model for long-range temporal modeling. Our architecture addresses three challenges: (1) capturing extended temporal trends in laboratory values, (2) accounting for patient-specific baseline variation via FiLM-modulated initial states, and (3) performing multi-task estimation for interrelated biomarkers. We evaluate our method on the two ICU datasets for predicting the five key laboratory tests. The results show substantial improvements over the LSTM and carry-forward baselines in MAE, RMSE, and $R^2$ among most of the estimation targets. This work demonstrates the feasibility of continuous, personalized lab value estimation from routine PPG monitoring, offering a pathway toward non-invasive biochemical surveillance in critical care.
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