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: 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 …

Many studies have been published on a variety of clinical applications of artificial
intelligence (AI) for sepsis, while there is no overview of the literature. The aim of this review …

Background: Sepsis, a life-threatening infection-induced inflammatory condition, has
significant global health impacts. Timely detection is crucial for improving patient outcomes …

Background COVID-19 remains a major healthcare concern. Vital signs are routinely
measured on admission and may provide an early, cost-effective indicator of outcome–more …

Purpose: To evaluate the application of machine learning methods, specifically Deep Neural
Networks (DNN) models for intensive care (ICU) mortality prediction. The aim was to predict …

Background Sepsis is a life-threatening condition that occurs due to the body's reaction to
infections, and it is a leading cause of morbidity and mortality in hospitals. Early prediction of …

Problem framing is critical to developing risk prediction models because all subsequent
development work and evaluation takes place within the context of how a problem has been …

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 …

Sepsis is a major cause of death worldwide. Over the past years, prediction of clinically
relevant events through machine learning models has gained particular attention. In the …