Forecasting Emergency Department Crowding with Advanced Machine Learning Models and Multivariable Input

TL;DR

This study demonstrates that advanced machine learning models, specifically N-BEATS and LightGBM, significantly improve the accuracy of 24-hour emergency department crowding forecasts using extensive multivariable data.

Contribution

It introduces the application of state-of-the-art ML models to ED crowding prediction with comprehensive multivariable inputs, outperforming traditional benchmarks.

Findings

N-BEATS and LightGBM outperform benchmarks by 11% and 9%.

DeepAR predicts next-day crowding with 0.76 AUC.

First study to compare advanced ML models with benchmarks in ED forecasting.

Abstract

Emergency department (ED) crowding is a significant threat to patient safety and it has been repeatedly associated with increased mortality. Forecasting future service demand has the potential patient outcomes. Despite active research on the subject, several gaps remain: 1) proposed forecasting models have become outdated due to quick influx of advanced machine learning models (ML), 2) amount of multivariable input data has been limited and 3) discrete performance metrics have been rarely reported. In this study, we document the performance of a set of advanced ML models in forecasting ED occupancy 24 hours ahead. We use electronic health record data from a large, combined ED with an extensive set of explanatory variables, including the availability of beds in catchment area hospitals, traffic data from local observation stations, weather variables, etc. We show that N-BEATS and LightGBM outpeform benchmarks with 11 % and 9 % respective improvements and that DeepAR predicts next day crowding with an AUC of 0.76 (95 % CI 0.69-0.84). To the best of our knowledge, this is the first study to document the superiority of LightGBM and N-BEATS over statistical benchmarks in the context of ED forecasting.