Background Timely identification of patients at a high risk of clinical deterioration is key to
prioritizing care, allocating resources effectively, and preventing adverse outcomes. Vital …

Objective This systematic literature review aims to demonstrate how Artificial Intelligence (AI)
is currently used in emergency departments (ED) and how it alters the work design of ED …

Neonatal intensive care units (NICUs) greatly expand the use of technology. There is a need
to accurately diagnose discomfort, pain, and complications, such as sepsis, mainly before …

Introduction Despite the growing investment in and adoption of artificial intelligence (AI) in
medicine, the applications of AI in an emergency setting remain unclear. This sco** …

Background: The prognostic value of the national early warning score (NEWS) in patients
with infections remains controversial. We aimed to evaluate the prognostic accuracy of …

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 …

Because of its multifactorial nature, predicting the presence of cancer using a single
biomarker is difficult. We aimed to establish a novel machine-learning model for predicting …

Objective: This study was designed to test the diagnostic value of visibility graph features
derived from the heart rate time series to predict late onset sepsis (LOS) in preterm infants …

A massive amount of multimodal data are continuously collected in the intensive care unit
(ICU) along each patient stay, offering a great opportunity for the development of smart …

Objective Having shown promise in other medical fields, we sought to determine whether
machine learning (ML) models perform better than usual care in diagnostic and prognostic …