Objectives: Develop and implement a machine learning algorithm to predict severe sepsis
and septic shock and evaluate the impact on clinical practice and patient outcomes. Design …

Introduction Several methods have been developed to electronically monitor patients for
severe sepsis, but few provide predictive capabilities to enable early intervention; …

Introduction Sepsis management is a challenge for hospitals nationwide, as severe sepsis
carries high mortality rates and costs the US healthcare system billions of dollars each year …

Objectives: Sepsis is among the leading causes of morbidity, mortality, and cost overruns in
critically ill patients. Early intervention with antibiotics improves survival in septic patients …

Background Severe sepsis and septic shock are among the leading causes of death in the
USA. While early prediction of severe sepsis can reduce adverse patient outcomes, sepsis …

Objectives: As a life-threatening condition, sepsis is one of the major public health issues
worldwide. Early prediction can improve sepsis outcomes with appropriate interventions …

Objectives: Early detection of sepsis is critical in clinical practice since each hour of delayed
treatment has been associated with an increase in mortality due to irreversible organ …

Objective Sepsis remains a costly and prevalent syndrome in hospitals; however, machine
learning systems can increase timely sepsis detection using electronic health records. This …

Sepsis remains a major cause of mortality and morbidity worldwide. Algorithms that assist
with the early recognition of sepsis may improve outcomes, but relatively few studies have …

Study objective Sepsis is a common and major health crisis in hospitals globally. An
innovative and feasible tool for predicting sepsis remains elusive. However, early and …