Forecasting Emergency Department Crowding with Advanced Machine Learning
  Models and Multivariable Input
Jalmari Tuominen Eetu Pulkkinen Jaakko Peltonen Juho Kanniainen Niku Oksala Ari Palomäki Antti Roine
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have become outdated due to quick influx of advanced machine learning models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.