Forecasting daily emergency department arrivals using high-dimensional multivariate data: a feature selection approach.

ABSTRACT:

Background and objective

Emergency Department (ED) overcrowding is a chronic international issue that is associated with adverse treatment outcomes. Accurate forecasts of future service demand would enable intelligent resource allocation that could alleviate the problem. There has been continued academic interest in ED forecasting but the number of used explanatory variables has been low, limited mainly to calendar and weather variables. In this study we investigate whether predictive accuracy of next day arrivals could be enhanced using high number of potentially relevant explanatory variables and document two feature selection processes that aim to identify which subset of variables is associated with number of next day arrivals. Performance of such predictions over longer horizo

SUBMITTER: Tuominen J 

PROVIDER: S-EPMC9112570 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature