In Studies in health technology and informatics ; h5-index 23.0
Emergency Department (ED) overcrowding is a major global healthcare issue. Many research studies have been conducted to predict ED wait time using various machine learning prediction models to enhance patient experience and improve care efficiency and resource allocation. In this paper, we used Long Short-Term Memory (LSTM) recurrent neural networks to build a model to predict ED wait time in the next 2 hours using a randomly generated patient timestamp dataset of a typical patient hospital journey. Compared with Linear Regression model, the average mean absolute error for the LSTM model is decreased by 9.7% (3 minutes) (p < 0.01). The LSTM model statistically outperforms the LR model, however, both models could be practically useful in ED wait time prediction.
Cheng Nok, Kuo Alex
ED overcrowding, ED wait time prediction, Long Short-Term Memory (LSTM), recurrent neural network