Improved Long-Term Forecasting of Emergency Department Arrivals with LSTM-Based Networks

Patient admission to Emergency Departments suffers from a great variability. This makes the resource allocation difficult to adjust, resulting in an inefficient service. Several studies have addressed this issue with machine learning’s regressors, time series analysis. This research proposes the use of improved recurrent neural networks that consider the dynamic nature of the data, introducing contextual variables that allow improving the predictability. Another important requirement from ED’s administration is to have a wider predicting horizon for short- and long-term resource allocations. The results obtained using the data from one single Hospital in Madrid confirm that the use of deep learning with contextual variables improve the predictability to 6% MAPE for seven days and four months forecasts. As future research lines, the influence of special events, such as seasonal epidemics, pollution episodes, sports or leisure events, as well as the extension of this study to different types of hospitals’ emergency departments.