Background Many mortality prediction models have been developed for patients in intensive
care units (ICUs); most are based on data available at ICU admission. We investigated …

Summary Background Intensive-care units (ICUs) treat the most critically ill patients, which is
complicated by the heterogeneity of the diseases that they encounter. Severity scores based …

Purpose: To evaluate the application of machine learning methods, specifically Deep Neural
Networks (DNN) models for intensive care (ICU) mortality prediction. The aim was to predict …

Improving predictive models for intensive care unit (ICU) inpatients requires a new strategy
that periodically includes the latest clinical data and can be updated to reflect local …

Background The large amount of clinical signals in intensive care units can easily
overwhelm health-care personnel and can lead to treatment delays, suboptimal care, or …

To improve the performance of Intensive Care Units (ICUs), the field of bio-statistics has
developed scores which try to predict the likelihood of negative outcomes. These help …

Abstract Machine learning can predict outcomes and determine variables contributing to
precise prediction, and can thus classify patients with different risk factors of outcomes. This …

Most existing studies used logistic regression to establish scoring systems to predict
intensive care unit (ICU) mortality. Machine learning-based approaches can achieve higher …

Background Prognostication is an essential tool for risk adjustment and decision making in
the intensive care unit (ICU). Research into prognostication in ICU has so far been limited to …

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