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 …

Background: Although numerous studies are conducted every year on how to reduce the
fatality rate associated with sepsis, it is still a major challenge faced by patients, clinicians …

Background: Sepsis is among the leading causes of death in intensive care units (ICUs)
worldwide and its recognition, particularly in the early stages of the disease, remains a …

Purpose Early clinical recognition of sepsis can be challenging. With the advancement of
machine learning, promising real-time models to predict sepsis have emerged. We …

Background Early recognition and treatment of sepsis are crucial for improving patient
outcomes. However, the diagnosis of sepsis remains challenging because of vague clinical …

Studies for sepsis prediction using machine learning are developing rapidly in medical
science recently. In this review, we propose a set of new evaluation criteria and reporting …

Introduction This study aimed to develop and validate an interpretable machine-learning
model based on clinical features for early predicting in-hospital mortality in critically ill …

Introduction Patients with sepsis who present to an emergency department (ED) have highly
variable underlying disease severity, and can be categorized from low to high risk …

Background: Early prediction of the clinical outcome of patients with sepsis is of great
significance and can guide treatment and reduce the mortality of patients. However, it is …

Background and objective Sepsis occurs in response to an infection in the body and can
progress to a fatal stage. Detection and monitoring of sepsis require multi-step analysis …