The emerging field of eXplainable AI (XAI) in the medical domain is considered to be of
utmost importance. Meanwhile, incorporating explanations in the medical domain with …

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: Mechanically ventilated patients in the intensive care unit (ICU) have high
mortality rates. There are multiple prediction scores, such as the Simplified Acute Physiology …

Sepsis is an organ failure disease caused by an infection resulting in extremely high
mortality. Machine learning algorithms XGBoost and LightGBM are applied to construct two …

Background: Severity of illness scores—Acute Physiology and Chronic Health Evaluation,
Simplified Acute Physiology Score, and Sequential Organ Failure Assessment—are current …

Modern Intensive Care Units (ICUs) provide continuous monitoring of critically ill patients
susceptible to many complications affecting morbidity and mortality. ICU settings require a …

The depth and breadth of data produced in the modern cardiac intensive care unit (CICU)
poses challenges to clinicians and researchers. Artificial intelligence (AI) and machine …

Background and objectives Sepsis is accompanied by a considerably high risk of mortality in
the short term, despite the availability of recommended mortality risk assessment tools …

Sepsis is a leading cause of mortality in the intensive care unit. Early prediction of sepsis
can reduce the overall mortality rate and cost of sepsis treatment. Some studies have …

Background Sepsis is the leading cause of intensive care unit (ICU) admission. The purpose
of this study was to explore the prognostic value of the Sequential Organ Failure …