Prediction of emergency department presentations for acute coronary syndrome using a machine learning approach.

ABSTRACT: The relationship between weather and acute coronary syndrome (ACS) incidence has been the subject of considerable research, with varying conclusions. Harnessing machine learning techniques, our study explores the relationship between meteorological factors and ACS presentations in the emergency department (ED), offering insights into seasonal variations and inter-day fluctuations to optimize patient care and resource allocation. A retrospective cohort analysis was conducted, encompassing ACS presentations to Dutch EDs from 2010 to 2017. Temporal patterns were analyzed using heat-maps and time series plots. Multivariable linear regression (MLR) and Random Forest (RF) regression models were employed to forecast daily ACS presentations with prediction horizons of one, three, seven, and thirty

SUBMITTER: Kurucz VC 

PROVIDER: S-EPMC11452569 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature