Enhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning.

ABSTRACT:

Background

Emergency department (ED) overcrowding is an important problem in many countries. Accurate predictions of ED patient arrivals can help management to better allocate staff and medical resources. In this study, we investigate the use of calendar and meteorological predictors, as well as feature-engineered variables, to predict daily patient arrivals using datasets from eleven different EDs across three countries.

Methods

Six machine learning (ML) algorithms were tested on forecasting horizons of 7 and 45 days. Three of them - Light Gradient Boosting Machine (LightGBM), Support Vector Machine with Radial Basis Function (SVM-RBF), and Neural Network Autoregression (NNAR) - were never before reported for predicting ED patient arrivals. Algorithms' hyperparameters were

SUBMITTER: Porto BM 

PROVIDER: S-EPMC11653554 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature