Feasibility of machine learning methods for predicting hospital emergency room visits for respiratory diseases

The prediction of hospital emergency room visits (ERV) for respiratory diseases after the outbreak of PM2.5 is of great importance in terms of public health, medical resource allocation, and policy decision support. Recently, the machine learning methods bring promising solutions for ERV prediction in view of their powerful ability of short-term forecasting, while their performances still exist unknown. Therefore, we aim to check the feasibility of machine learning methods for ERV prediction of respiratory diseases. Three different machine learning models, including autoregressive integrated moving average (ARIMA), multilayer perceptron (MLP), and long short-term memory (LSTM), are introduced to predict daily ERV in urban areas of Beijing, and their performances are evaluated in terms of the mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R-2). The results show that the performance of ARIMA is the worst, with a maximum R-2 of 0.70 and minimum MAE, RMSE, and MAPE of 99, 124, and 26.56, respectively, while MLP and LSTM perform better, with a maximum R-2 of 0.80 (0.78) and corresponding MAE, RMSE, and MAPE of 49 (33), 62 (42), and 14.14 (9.86). In addition, it demonstrates that MLP cannot detect the time lag effect properly, while LSTM does well in the description and prediction of exposure-response relationship between PM2.5 pollution and infecting respiratory disease.

Automated summary

The study examines the feasibility of machine learning methods for predicting daily ERV of respiratory diseases in urban areas of Beijing. Results show that ARIMA performs the worst, while MLP and LSTM perform better, with LSTM showing strong capability in describing and predicting the relationship between PM2.5 pollution and respiratory disease infection.