Background: Artificial intelligence (AI) provides opportunities to identify the health risks of
patients and thus influence patient safety outcomes. Objective: The purpose of this …

Abstract Introduction: Approximately 7,000 newborns die every day, accounting for almost
half of child deaths under 5 years of age. Deciphering which neonates are at increased risk …

Objective To report the improvements achieved with clinical decision support systems and
examine the heterogeneity from pooling effects across diverse clinical settings and …

Despite enormous enthusiasm, machine learning models are rarely translated into clinical
care and there is minimal evidence of clinical or economic impact. New conference venues …

Objective Predicting the risk of falls in advance can benefit the quality of care and potentially
reduce mortality and morbidity in the older population. The aim of this study was to construct …

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

Background Early warning tools identify patients at risk of deterioration in hospitals.
Electronic medical records in hospitals offer real-time data and the opportunity to automate …

Objective We aimed to develop a model for accurate prediction of general care inpatient
deterioration. Materials and Methods Training and internal validation datasets were built …

Urban development and growth have a significant impact on the environment, contributing to
ongoing climate change and affecting the resilience of urban communities. However, cities …

Objectives This systematic review aimed to assess the performance and clinical feasibility of
machine learning (ML) algorithms in prediction of in-hospital mortality for medical patients …