Data-driven patient-specific fluid resuscitation

Automated fluid resuscitation systems use control algorithms to determine fluid infusion rates for patients recovering from, e.g., hemorrhage or dehydration. Instead of relying on models that require patient-specific parameter identification, we present a data-driven patient-specific alternative that bypasses modeling and identification. Specifically, we implement a predictive controller that only uses blood-pressure data, and manages to regulate the patient’s blood-pressure to a specified setpoint. We demonstrate the results in simulation on 100 virtual patients created using a well-established model of cardiovascular hemodynamics.