Objective To assess the methodological quality of studies on prediction models developed
using machine learning techniques across all medical specialties. Design Systematic …

The primary objective of implementing Electronic Health Records (EHRs) is to improve the
management of patients' health-related information. However, these records have also been …

Despite widespread adoption of electronic health records (EHRs), most hospitals are not
ready to implement data science research in the clinical pipelines. Here, we develop …

Background Advances in machine learning (ML) provide great opportunities in the
prediction of hospital readmission. This review synthesizes the literature on ML methods and …

Using artificial intelligence and machine learning techniques in different medical fields,
especially emergency medicine is rapidly growing. In this paper, studies conducted in the …

Aim. To synthesise the available evidence on the applicability of artificial intelligence in
nursing care. Background. Artificial intelligence involves the replication of human cognitive …

Rationale: Late recognition of patient deterioration in hospital is associated with worse
outcomes, including higher mortality. Despite the widespread introduction of early warning …

Objective The primary objective is to develop an automated method for detecting patients
that are ready for discharge from intensive care. Design We used two datasets of routinely …

Over the last half-century, the telemedicine intensive care unit (tele-ICU) has grown from a
daily video conference to a comprehensive high-bandwidth system connecting more than …

Background Hospital readmissions are a key quality metric, which has been tied to
reimbursement. One strategy to reduce readmissions is to direct resources to patients at the …