  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.
