Effective forecasting of key features in hospital emergency department: Hybrid deep learning-driven methods

Abstract

Forecasting the different types of emergency department (ED) demands (patient flows) in hospital systems much aids ED managers in looking into various options to appropriately allocating the restricted resources available per patient attendance. Deep learning networks have recently gained great success in modeling time-dependent in time series data. Thus, this work advocates the use of deep learning-driven models for patient flows forecasting. Notably, we examine and compare seven deep learning models, Deep Belief Network (DBN), Restricted Boltzmann machines (RBM), Long Short Term Memory (LSTM), Gated recurrent unit (GRU), combined GRU and convolutional neural networks (CNN-GRU), LSTM-CNN, and Generative Adversarial Network based on Recurrent Neural Networks (GAN-RNN), to forecast patient flow in a hospital emergency department. We introduce a forecaster layer as output for each model to enable traffic flow forecasting. Patient flow data from different ED services, including biology, radiology, scanner, and echography, in Lille regional hospital in France, is used as a case study in assessing the considered forecasting models. Four metrics of effectiveness are adopted for evaluating and comparing the forecasting methods. The results show the promising performance of deep learning models for ED patient flow forecasting compared to shallow methods (i.e., ridge regression and support vector regression). In addition, the results highlighted the superior performance of the DBN compared to the other models by achieving an averaged mean absolute percentage error of around 4.097% and R2 of 0.973.