To date, there are no reviews on machine learning (ML) for predicting outcomes in trauma.
Consequently, it remains unclear as to how ML-based prediction models compare in the …

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 …

Background Improved mortality prediction for patients in intensive care units is a big
challenge. Many severity scores have been proposed, but findings of validation studies have …

Background Mortality prediction of hospitalized patients is an important problem. Over the
past few decades, several severity scoring systems and machine learning mortality …

The intensive care unit (ICU) admits the most seriously ill patients requiring extensive
monitoring. Early ICU mortality prediction is crucial for identifying patients who are at great …

Over the past few years, there has been increased interest in data mining and machine
learning methods to improve hospital performance, in particular hospitals want to improve …

Background Early identification of critically ill patients who will require prolonged
mechanical ventilation (PMV) has proven to be difficult. The purpose of this study was to use …

In emergency departments, the most common cause of death associated with suspected
infected patients is sepsis. In this study, deep learning algorithms were used to predict the …

Early hospital mortality prediction is critical as intensivists strive to make efficient medical
decisions about the severely ill patients staying in intensive care units (ICUs). As a result …

Current mortality prediction models and scoring systems for intensive care unit patients are
generally usable only after at least 24 or 48 h of admission, as some parameters are unclear …