Human albumin is essential for indicating the body's overall health. Accurately predicting plasma albumin levels and determining appropriate doses are urgent clinical challenges, particularly in critically ill patients, to maintain optimal blood levels. However, human albumin prediction is non-trivial that has to leverage the dynamics of biochemical markers as well as the experience of treating patients. Moreover, the problem of distribution shift is often encountered in real clinical data, which may lead to a decline in the model prediction performance and reduce the reliability of the model's application. In this paper, we propose a framework named Out-of-Distribution Generalized Dynamic Graph Neural Network for Human Albumin Prediction (DyG-HAP), which is able to provide accurate albumin predictions for Intensity Care Unit (ICU) patients during hospitalization. We first model human albumin prediction as a dynamic graph regression problem to model the dynamics and patient relationship. Then, we propose a disentangled dynamic graph attention mechanism to capture and disentangle the patterns whose relationship to labels under distribution shifts is invariant and variant respectively. Last, we propose an invariant dynamic graph regression method to encourage the model to rely on invariant patterns to make predictions. Moreover, we propose a dataset named Albumin level testing and nutritional dosing data for Intensive Care (ANIC) for evaluation. Extensive experiments demonstrate the superiority of our method compared to several baseline methods in human albumin prediction.

翻译：人血白蛋白是评估人体整体健康状态的关键指标。准确预测血浆白蛋白水平并确定合适给药剂量是亟待解决的临床难题，尤其对重症患者维持最佳血液浓度至关重要。然而，人血白蛋白预测并非易事，必须充分利用生化标志物的动态变化特性及患者治疗经验。此外，真实临床数据常面临分布偏移问题，这可能导致模型预测性能下降并降低应用可靠性。本文提出名为“面向人血白蛋白预测的分布外泛化动态图神经网络”（DyG-HAP）的框架，可精准预测重症监护室（ICU）患者住院期间的白蛋白水平。我们首先将人血白蛋白预测建模为动态图回归问题，以刻画时序动态性与患者间关联性；随后提出解耦动态图注意力机制，分别捕获与标签在分布偏移下保持不变和变化的模式；最后设计不变动态图回归方法，促使模型基于不变模式进行预测。此外，我们构建了名为“重症监护白蛋白水平检测与营养给药数据”（ANIC）的数据集用于评估。大量实验表明，本文方法在人血白蛋白预测任务中显著优于多种基线模型。