Recent developments in data science in general and machine learning in particular have
transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a …

The adoption of artificial intelligence (AI) and machine learning (ML) in risk-sensitive
environments is still in its infancy because it lacks a systematic framework for reasoning …

Established guidelines describe minimum requirements for reporting algorithms in
healthcare; it is equally important to objectify the characteristics of ideal algorithms that …

Background Interest in the usefulness of machine learning (ML) methods for outcomes
prediction has continued to increase in recent years. However, the advantage of advanced …

Purpose The field of artificial intelligence is ever growing and the applications of machine
learning in spine care are continuously advancing. Given the advent of the intelligence …

Background Despite considerable research efforts during the last five decades, the
prediction of suicidal behaviour (SB) using traditional model-based statistical has been …

Background The number of studies applying machine learning (ML) to predict acute kidney
injury (AKI) has grown steadily over the past decade. We assess and critically appraise the …

Importance Massive transfusion is essential to prevent complications during uncontrolled
intraoperative hemorrhage. As massive transfusion requires time for blood product …

Background: Severity of illness scores—Acute Physiology and Chronic Health Evaluation,
Simplified Acute Physiology Score, and Sequential Organ Failure Assessment—are current …

Abstract Machine learning for hospital operations is under-studied. We present a prediction
pipeline that uses live electronic health-records for patients in a UK teaching hospital's …