Objective To provide an overview and critical appraisal of early warning scores for adult
hospital patients. Design Systematic review. Data sources Medline, CINAHL, PsycInfo, and …

Objective: Electronic health records (EHRs) are an increasingly common data source for
clinical risk prediction, presenting both unique analytic opportunities and challenges. We …

The potential for big data analytics to improve cardiovascular quality of care and patient
outcomes is tremendous. However, the application of big data in health care is at a nascent …

Rationale: Early warning system (EWS) scores are used by hospital care teams to recognize
early signs of clinical deterioration and trigger more intensive care. Objective: To …

Background Timely identification of patients at a high risk of clinical deterioration is key to
prioritizing care, allocating resources effectively, and preventing adverse outcomes. Vital …

Rationale: Most ward risk scores were created using subjective opinion in individual
hospitals and only use vital signs. Objectives: To develop and validate a risk score using …

Objective: To assess clinician perceptions of a machine learning–based early warning
system to predict severe sepsis and septic shock (Early Warning System 2.0). Design …

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 …

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

Sepsis contributes to up to half of all deaths in hospitalized patients, and early interventions,
such as appropriate antibiotics, have been shown to improve outcomes. Most research has …