Mortality prediction of intensive care unit (ICU) patients facilitates hospital benchmarking
and has the opportunity to provide caregivers with useful summaries of patient health at the …

Improving the Intensive Care Unit (ICU) management network and building cost-effective
and well-managed healthcare systems are high priorities for healthcare units. Creating …

Background and objective Alert of patient deterioration is essential for prompt medical
intervention in the Medical Intensive Care Unit (MICU). Logistic Regression (LR) has been …

Abstract Interest in Machine Learning applications to tackle clinical and biological problems
is increasing. This is driven by promising results reported in many research papers, the …

Progress of machine learning in critical care has been difficult to track, in part due to
absence of public benchmarks. Other fields of research (such as computer vision and …

Objective The growing availability of rich clinical data such as patients' electronic health
records provide great opportunities to address a broad range of real-world questions in …

Background Machine learning (ML) holds the promise of becoming an essential tool for
utilising the increasing amount of clinical data available for analysis and clinical decision …

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 …

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 …

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