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 Machine learning (ML) is increasingly being used in many areas of health care.
Its use in infection management is catching up as identified in a recent review in this journal …

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

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 …

This article is one of ten reviews selected from the Annual Update in Intensive Care and
Emergency Medicine 2022. Other selected articles can be found online at https://www …

In a modern life, early healthcare prediction plays an important role to prevent the loss of life
caused by prediction delays in treatment. Nowadays, the researchers focused on the Big …

Background The design of computerized systems able to support automated detection of
threatening conditions in critically ill patients such as systemic inflammatory response …

In 2016, the SOFA score was proposed as the main evaluation system for diagnosis in the
definition of sepsis 3.0, and the SOFA score has become a new research focus in sepsis …