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 …

The use of artificial intelligence, and the deep-learning subtype in particular, has been
enabled by the use of labeled big data, along with markedly enhanced computing power …

Early recognition and treatment of sepsis are linked to improved patient outcomes. Machine
learning-based early warning systems may reduce the time to recognition, but few systems …

At the beginning of the artificial intelligence (AI)/machine learning (ML) era, the expectations
are high, and experts foresee that AI/ML shows potential for diagnosing, managing and …

Sepsis is a leading cause of death in hospitals. Early prediction and diagnosis of sepsis,
which is critical in reducing mortality, is challenging as many of its signs and symptoms are …

How the nursing profession should adapt for a digital future Page 1 FUTURE OF NURSING
How the nursing profession should adapt for a digital future Transformation into a digitally …

Deep learning has been applied to many areas in health care, including imaging diagnosis,
digital pathology, prediction of hospital admission, drug design, classification of cancer and …

Objectives: Sepsis is among the leading causes of morbidity, mortality, and cost overruns in
critically ill patients. Early intervention with antibiotics improves survival in septic patients …

Background: Clinical narratives represent the main form of communication within health
care, providing a personalized account of patient history and assessments, and offering rich …

Background Machine learning (ML) is a growing field in medicine. This narrative review
describes the current body of literature on ML for clinical decision support in infectious …