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 …

Problem Sepsis, a life-threatening condition, accounts for the deaths of millions of people
worldwide. Accurate prediction of sepsis outcomes is crucial for effective treatment and …

Introduction Septic patients requiring intensive care unit (ICU) readmission are at high risk of
mortality, but research focusing on the association of ICU readmission due to sepsis and …

Background This study aimed to develop an algorithm using the explainable artificial
intelligence (XAI) approaches for the early prediction of mortality in intensive care unit (ICU) …

Sepsis is a dysregulated immune response to infection that leads to organ dysfunction and
is associated with a high incidence and mortality rate. The lack of reliable biomarkers for …

Conventional severity-of-illness scoring systems have shown suboptimal performance for
predicting in-intensive care unit (ICU) mortality in patients with severe pneumonia. This …

Chronic kidney disease (CKD) is a major worldwide health problem, affecting a large
proportion of the world's population and leading to higher morbidity and death rates. The …

Objectives To compare the artificial intelligence algorithms as powerful machine learning
methods for evaluating patients with suspected sepsis using data from routinely available …

Infection science is a discipline of healthcare which includes clinical microbiology, public
health microbiology, mechanisms of microbial disease, and antimicrobial countermeasures …

Background The lack of transparency is a prevalent issue among the current machine-
learning (ML) algorithms utilized for predicting mortality risk. Herein, we aimed to improve …