Deep learning modelling to forecast emergency department visits using calendar, meteorological, internet search data and stock market price

Abstract

Background: Accurate prediction of hospital emergency department (ED) patient visits and acuity levels have potential to improve resource allocation including manpower planning and hospital bed allocation. Internet search data have been used in medical applications like disease pattern prediction and forecasting ED volume. Past studies have also found stock market price positively correlated with ED volume.

Objective: To determine whether incorporating Internet search data and stock market price to calendar and meteorological data can improve deep learning prediction of ED patient volumes, and whether hybrid deep learning architectures are better in prediction.

Methods: Permutations of various input variables namely calendar, meteorological, Google Trends online search data, Standard and Poor's (S&P) 500 index, and Straits Times Index (STI) data were incorporated into deep learning models long short-term memory (LSTM), one-dimensional convolutional neural network (1D CNN), stacked 1D CNN-LSTM, and five CNN-LSTM hybrid modules to predict daily Singapore General Hospital ED patient volume from 2010-2012.

Results: Incorporating STI to calendar and meteorological data improved performance of CNN-LSTM hybrid models. Addition of queried absolute Google Trends search terms to calendar and meteorological data improved performance of two out of five hybrid models. The best LSTM model across all predictor permutations had mean absolute percentage error of 4.8672 %.

Conclusion: LSTM provides strong predictive ability for daily ED patient volume. Local stock market index has potential to predict ED visits. Amongst predictors evaluated, calendar and meteorological data was sufficient for a relatively accurate prediction.

Keywords: Artificial intelligence; CNN; Deep learning; Emergency department; LSTM.