Objective The study sought to describe the prevalence and nature of clinical expert
involvement in the development, evaluation, and implementation of clinical decision support …

A major focus of academia, industry, and global governmental agencies is to develop and
apply artificial intelligence and other advanced analytical tools to transform health care …

Translating machine learning (ML) models effectively to clinical practice requires
establishing clinicians' trust. Explainability, or the ability of an ML model to justify its …

The pressure of ever-increasing patient demand and budget restrictions make hospital bed
management a daily challenge for clinical staff. Most critical is the efficient allocation of …

Early diagnosis of critically ill patients depends on the attention and observation of medical
staff about different variables, as vital signs, results of laboratory tests, among other …

This work attempts to determine whether it is possible to predict the occurrence of in-hospital
Cardiac Arrest (CA) using vital signs routinely recorded in a hospital's Electronic Health …

Background Artificial intelligence (AI) is a rapidly evolving field which will have implications
on both individual patient care and the health care system. There are many benefits to the …

Objective: Storage of physiological waveform data for retrospective analysis presents
significant challenges. Resultant data can be very large, and therefore becomes expensive …

Background: Without timely diagnosis and treatment, tachycardia, also called
tachyarrhythmia, can cause serious complications such as heart failure, cardiac arrest, and …

Purpose To develop a model that predicts whether a child will develop a recurrent
obstruction after pyeloplasty, determine their survival risk score, and expected time to re …