Optimal Sepsis Patient Treatment using Human-in-the-loop Artificial Intelligence

This study proposes a clinical prescriptive model with human in the loop functionality that recommends optimal, individual-specific amounts of IV fluids for the treatment of septic patients in ICUs. The proposed methodology combines constrained optimization and machine learning techniques to arrive at optimal solutions. A key novelty of the proposed clinical model is utilization of a physician's input to derive optimal solutions. The efficacy of the method is demonstrated using a real world medical dataset. We further validated the robustness of the proposed approach to show that our method benefits from the human in the loop component, but is also robust to poor input, which is a crucial consideration for new physicians.