The development of early warning scores or alerting systems for the prediction of adverse events in psychiatric patients: a scoping review.

ABSTRACT:

Background

Adverse events in psychiatric settings present ongoing challenges for both patients and staff. Despite advances in psychiatric interventions and treatments, research on early warning scores and tools to predict patient deterioration is limited. This review provides a summary of the few tools that have been developed in a psychiatric setting, comparing machine learning (ML) and nonmachine learning/traditional methodologies. The outcomes of interest include the selected key variables that contribute to adverse events and the performance and validation measures of the predictive models.

Methods

Three databases, Ovid MEDLINE, PsycINFO, and Embase, were searched between February 2023 and April 2023 to identify all relevant studies that included a combination of (and we

SUBMITTER: Velasquez VT 

PROVIDER: S-EPMC11520586 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature