Using Long Short-Term Memory (LSTM) Neural Networks to Predict Emergency Department Wait Time.

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.

Using Long Short-Term Memory (LSTM) Neural Networks to Predict Emergency Department Wait Time.

Emergency Department (ED) overcrowding is a major global healthcare issue. 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 15% (3 minutes) (p<0.001). The LSTM model statistically outperforms the LR model, however, both models could be practically useful in ED wait time prediction.