Estimating treatment effects for time-to-treatment antibiotic stewardship in sepsis

Ruoqi Liu, Katherine M. Hunold, Jeffrey M. Caterino, Ping Zhang

Abstract: Sepsis treatment needs to be well timed to be effective and to avoid antibiotic resistance. Machine learning can help to predict optimal treatment timing, but confounders in the data hamper reliability. Liu and colleagues present a method to predict patient-specific treatment effects with increased accuracy, accompanied by an uncertainty estimate.