Automated Real-Time Cytokine Storm Mitigation via Extracorporeal Membrane Oxygenation (ECMO) Parameter Optimization using Bayesian Network Reinforcement Learning (BNRL)

**Abstract:** Cytokine storms, characterized by exaggerated inflammatory responses, pose a significant threat in critical care settings, often leading to acute respiratory distress syndrome (ARDS) and multi-organ failure. Traditional ECMO therapy, while life-saving, relies heavily on clinician judgment regarding parameter adjustments (flow rate, ventilator settings). This paper proposes a novel system for real-time, automated ECMO parameter optimization utilizing a Bayesian Network Reinforcement Learning (BNRL) agent, designed to dynamically adapt to evolving patient conditions and proactively mitigate the cytokine storm progression. Leveraging continuous physiological monitoring data, the BNRL system learns the complex causal relationships between ECMO parameters, inflammatory markers, and patient outcomes, enabling superior, personalized treatment strategies compared to standard protocols. The system can be commercialized within 2-3 years and has the potential to drastically reduce mortality rates associated with cytokine storm-induced ARDS.