Background Mortality prediction models are applied in the intensive care unit (ICU) to stratify
patients into different risk categories and to facilitate benchmarking. To ensure that the …

Introduction Several methods have been developed to electronically monitor patients for
severe sepsis, but few provide predictive capabilities to enable early intervention; …

Objectives We validate a machine learning-based sepsis-prediction algorithm (InSight) for
the detection and prediction of three sepsis-related gold standards, using only six vital signs …

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

Intensive care is an ideal environment for the use of Big Data Analysis (BDA) and Machine
Learning (ML), due to the huge amount of information processed and stored in electronic …

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 …

Rationale Prediction of patients at risk for mortality can help triage patients and assist in
resource allocation. Objectives Develop and evaluate a machine learning-based algorithm …

Background Despite wide use of severity scoring systems for case-mix determination and
benchmarking in the intensive care unit (ICU), the possibility of scoring bias across …

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 …

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 …