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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 …

Deep neural networks (DNNs) are an indispensable machine learning tool despite the
difficulty of diagnosing what aspects of a model's input drive its decisions. In countless real …

Background Sepsis is a significant cause of mortality in-hospital, especially in ICU patients.
Early prediction of sepsis is essential, as prompt and appropriate treatment can improve …

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 …

Objectives: Sepsis is a major public health concern with significant morbidity, mortality, and
healthcare expenses. Early detection and antibiotic treatment of sepsis improve outcomes …

The potential of artificial intelligence (AI) to reduce health care disparities and inequities is
recognized, but it can also exacerbate these issues if not implemented in an equitable …

Acute critical illness is often preceded by deterioration of routinely measured clinical
parameters, eg, blood pressure and heart rate. Early clinical prediction is typically based on …

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 …

Health care applications of artificial intelligence (AI) have recently emerged. Artificial
intelligence approaches, such as deep learning, rely on vast amounts of data and complex …