Differentiable Predictive Control for Precise Oxygen Level Maintenance for Critical Patients

Precisely managing oxygen levels is crucial for patients with critical illnesses, helping to prevent a wide range of severe conditions and physical harm. Despite its importance, current healthcare systems lack operationally effective and efficient solutions for oxygen level maintenance. To address this gap, we present the first-ever framework for precise oxygen level management using Differentiable Predictive Control (DPC). By employing a sophisticated neural policy and leveraging the differentiable nature of the system model, DPC fine-tunes oxygen delivery based on patient-specific conditions with high accuracy. This end-to-end automated system continuously monitors real-time patient data to optimize oxygen flow, maximizing comfort while minimizing waste. Our approach not only enhances patient care but also improves resource efficiency and reduces costs in critical care settings. Empirical results further demonstrate the robustness and effectiveness of our model.