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This paper presents a novel method for predicting hemodynamic responses in hemorrhage resuscitation. The proposed approach, namely, robust nonlinear state space modeling (RNSSM), aims to overcome challenges of identifying reliable models using limited and noisy critical care data by innovatively integrating autoencoder learning and variational Gaussian inference in a unified framework. Simulation results demonstrate the initial feasibility and performance evidence of the RNSSM approach as a digital twin of an animal study in hemorrhage resuscitation scenarios. 

Fluid therapy is a common treatment for hypovolemic scenarios to restore the lost blood volume and stabilize acutely ill patients. Automating fluid therapy can lead to a reduction of delay in care, a decrement in dosing errors, and a reduction of cognitive load on clinicians responsible for patient care resulting in improved patient outcomes. However, this process is highly challenging due to the complexity of patient’s physiology and the variability of hemodynamic responses among patients. This work presents a novel machine learning approach based on reinforcement learning (RL) for automated fluid management, where the RL agent is designed to recommend subject-specific infusion dosages without having the knowledge of dose-response models and only by interacting with the environment (virtual subject generator). Compared to the state-of-the-art focusing on the entire population’s data, the proposed approach uses individual patient’s data to recommend patient-specific fluid dosage adjustment. Simulation results demonstrate that the proposed approach outperforms a proportional-integral-derivative (PID) and a rule-based fluid resuscitation controller previously reported for an animal study.