Objective To retrieve and appraise studies of deployed artificial intelligence (AI)-based
sepsis prediction algorithms using systematic methods, identify implementation barriers …

Sepsis remains a major challenge internationally for healthcare systems. Its incidence is
rising due to poor public awareness and delays in its recognition and subsequent …

Sepsis is a life-threatening disease that is associated with organ dysfunction. It occurs due to
the body's dysregulated response to infection. It is difficult to identify sepsis in its early …

Background Predictive models have been used in clinical care for decades. They can
determine the risk of a patient developing a particular condition or complication and inform …

Introduction The present study presents the development and validation of a clinical
prediction model using random survival forest (RSF) and stepwise Cox regression, aiming to …

Introduction The increasing availability of data and computing power has made machine
learning (ML) a viable approach to faster, more efficient healthcare delivery. Methods A …

Abstract Purpose Sepsis suspicion by Emergency Medical Services (EMS) is associated
with improved patient outcomes. This study assessed sepsis incidence and recognition by …

Aim To determine intensive care nurses' awareness of identification of early sepsis findings.
Background The incidence of sepsis is increasing in intensive care units, and if not identified …

Purpose Sepsis is the main cause of death in intensive care unit. Maladaptive cytokine storm
and T-cell lymphopenia are critical prognosis predictors of sepsis. Electroacupuncture (EA) …

We conducted a systematic review using PRISMA (Preferred Reporting Items for Systematic
Reviews and Meta-Analysis) guidelines of articles published until September 2022 from …