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

TL;DR

This paper presents a human-in-the-loop AI approach to optimize IV fluid treatment for sepsis patients, reducing mortality by 22% using data-driven methods on ICU patient data.

Contribution

It introduces a novel data-driven optimization method that personalizes sepsis treatment by controlling IV fluids, integrating human expertise into AI decision-making.

Findings

Reduced patient mortality by 22% on average.

Validated on 1122 ICU sepsis patients from MIMIC-III.

Demonstrated potential to assist physicians in treatment decisions.

Abstract

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.