Optimal Sepsis Patient Treatment using Human-in-the-loop Artificial Intelligence
This addresses a critical medical challenge for sepsis patients and physicians, offering a patient-specific treatment strategy that is incremental in applying existing AI techniques to a specific healthcare domain.
The study tackled the problem of determining optimal IV fluid quantities for sepsis patients in ICUs, resulting in an average mortality reduction of 22% using a data-driven optimization method with human-in-the-loop AI.
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.