Algorithm–based clinical decision support (CDS) systems associate patient-derived health
data with outcomes of interest, such as in-hospital mortality. However, the quality of such …

OBJECTIVES: To train a model to predict vasopressor use in ICU patients with sepsis and
optimize external performance across hospital systems using domain adaptation, a transfer …

Aims: To compute the financial and mortality impact of InSight, an algorithm-driven
biomarker, which forecasts the onset of sepsis with minimal use of electronic health record …

Several methods have been used for detecting Type 2 diabetes mellitus (T2DM), but deep
learning has not been successfully used to predict T2DM due to the low accuracy and …

Introduction Identifying and analyzing mortality risk factors will lead to more accurate
planning and prevention in health platforms. This research provides models for predicting …

Abstract Clinical Decision Support Systems (CDSSs) support medical personnel by offering
aid in decision-making and timely interventions in patient care. Typically such systems are …

Background Patients with alcohol use disorder (AUD) are common attendees of the
intensive care unit (ICU). Early assessment of the prognosis for critically ill patients with AUD …

Purpose-The present paper attempts to present the research that has been made on
prediction models using deep learning methods with data retrieved from mimic III database …

Background and aim: Clinical risk assessments should be made to protect patients from
negative outcomes, and the definition, frequency and severity of the risk should be …

Healthcare analytics is a field that deals with the examination of underlying patterns in
healthcare data in order to determine ways in which clinical care can be improved-in terms …