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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 ofIV fluids is a challenging problem due to the complexity of a patient'sphysiology. In this study, we develop a data-driven optimization solution thatderives the optimal quantity of IV fluids for individual patients. The proposedmethod minimizes the probability of severe outcomes by controlling theprescribed quantity of IV fluids and utilizes human-in-the-loop artificialintelligence. We demonstrate the performance of our model on 1122 ICU patientswith sepsis diagnosis extracted from the MIMIC-III dataset. The results showthat, on average, our model can reduce mortality by 22%. This study has thepotential to help physicians synthesize optimal, patient-specific treatmentstrategies.

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