Extracorporeal membrane oxygenation (ECMO) is used to support patients with severe cardiac or respiratory failure, requiring constant manual adjustments of pump flow, sweep gas flow, and oxygen fraction. However, current ECMO management lacks a real-time optimization system tailored to individual patient needs. This manuscript proposes an offline reinforcement learning framework for dynamic ECMO optimization, utilizing real-time measurements of blood gases, hemodynamics, and pump flow. The framework includes a state encoder for various patient data, an action space for adjustments to ECMO settings, and a reward function that balances oxygenation, hemodynamic support, and complication avoidance. A safety shield filters unsafe recommendations before clinician review. The system aims to provide personalized, proactive, and safety-constrained ECMO management, with the goal of guiding future research validation rather than claiming experimental results.
Clinical Intelligence Research Press