Clinical decision-making in healthcare is already being influenced by predictions or
recommendations made by data-driven machines. Numerous machine learning applications …

The recurring instances of infectious disease outbreaks, coupled with complications such as
comorbidity challenges and antibiotic resistance, consistently underscore the limitations …

Abstract Nowadays Artificial Intelligence (AI) has become a fundamental component of
healthcare applications, both clinical and remote, but the best performing AI systems are …

During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of
patients at the emergency department is critical to inform decision-making. We propose a …

Context: Recently, the machine learning (ML) field has been rapidly growing, mainly owing
to the availability of historical datasets and advanced computational power. This growth is …

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

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 …

The growing concern of pediatric mortality demands heightened preparedness in clinical
settings, especially within intensive care units (ICUs). As respiratory-related admissions …

The recent availability of Electronic Health Records (EHR) has allowed for the development
of algorithms predicting inpatient risk of deterioration and trajectory evolution. However …

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 …