Abstract This study presents a novel automated fluid resuscitation framework designed to maintain hemodynamic stability in the presence of limited and noisy physiological data. We propose a robust nonlinear state-space modeling (RNSSM) algorithm, trained via variational autoencoder learning, to capture mean arterial pressure (MAP) responses to fluid infusion in hemorrhagic scenarios. The model is integrated with a radial basis function (RBF) optimal control approach that combines function approximation and predictive optimization to regulate fluid infusion dosages during resuscitation. The accuracy of the RNSSM was confirmed using real-world data. Additionally, the superior performance of the RBF optimal controller in fluid dose adjustment was demonstrated in comparison with state-of-the-art fluid resuscitation control algorithms. Simulation results indicate that this approach addresses key limitations of existing methods by enabling more accurate, subject-specific hemodynamic regulation for fluid management in critical care.
Estiri et al. (Fri,) studied this question.