Hyun-Lim Yang

Abstract Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection, making optimal management critical. Existing Reinforcement Learning (RL) approaches for sepsis management have mainly relied on structured data (e.g., vital signs, laboratory results), lacking contextual information needed for comprehensive patient understanding.

Abstract Predicting in-hospital cardiac arrest in patients admitted to an intensive care unit (ICU) allows prompt interventions to improve patient outcomes. We developed and validated a machine learning-based real-time model for in-hospital cardiac arrest predictions using electrocardiogram (ECG)-based heart rate variability (HRV) measures. The HRV measures, including time/frequency domains and nonlinear measures, were calculated from 5 min epochs of ECG signals from ICU patients.