Optimal Sepsis Patient Treatment using Human-in-the-loop Artificial
  Intelligence
  Sepsis is one of the leading causes of death in Intensive Care Units (ICU).
The strategy for treating sepsis involves the infusion of intravenous (IV)
fluids and administration of antibiotics. Determining the optimal quantity of
IV fluids is a challenging problem due to the complexity of a patient's
physiology. In this study, we develop a data-driven optimization solution that
derives the optimal quantity of IV fluids for individual patients. The proposed
method minimizes the probability of severe outcomes by controlling the
prescribed quantity of IV fluids and utilizes human-in-the-loop artificial
intelligence. We demonstrate the performance of our model on 1122 ICU patients
with sepsis diagnosis extracted from the MIMIC-III dataset. The results show
that, on average, our model can reduce mortality by 22%. This study has the
potential to help physicians synthesize optimal, patient-specific treatment
strategies.
