The primary objective of implementing Electronic Health Records (EHRs) is to improve the
management of patients' health-related information. However, these records have also been …

Deep learning models (aka Deep Neural Networks) have revolutionized many fields
including computer vision, natural language processing, speech recognition, and is being …

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 Severe sepsis and septic shock are still the leading causes of death in Intensive
Care Units (ICUs), and timely diagnosis is crucial for treatment outcomes. The progression of …

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 …

Patient-specific mortality prediction models are an essential component of Clinical Decision
Support Systems developed for caregivers in Intensive Care Units (ICUs), that enable timely …

Background: Clinical decision support systems are used to help predict patient stability and
mortality in the Intensive Care Unit (ICU). Accurate patient information can assist clinicians …

This research proposes a Dynamic Ensemble Learning Algorithm based on K-means
(DELAK) for intensive care unit (ICU) mortality prediction. Nowadays, the widely applied …