Performance evaluation of Emergency Department patient arrivals forecasting models by including meteorological and calendar information: A comparative study. (August 2021)

• Record Type: Journal Article
• Authors: Sudarshan, Vidya K.; Brabrand, Mikkel; Range, Troels Martin; Wiil, Uffe Kock
• Is Part Of: Computers in biology and medicine. Volume 135(2021)
• Publication Date: 2021-08
• Subjects: Forecasting models -- CNN -- LSTM -- Deep learning -- Machine learning -- Random forest regressor -- Emergency department; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104541
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: The volume of daily patient arrivals at Emergency Departments (EDs) is unpredictable and is a significant reason of ED crowding in hospitals worldwide. Timely forecast of patients arriving at ED can help the hospital management in early planning and avoiding of overcrowding. Many different ED patient arrivals forecasting models have previously been proposed by using time series analysis methods. Even though the time series methods such as Linear and Logistic Regression, Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), Exponential Smoothing (ES), and Artificial Neural Network (ANN) have been explored extensively for the ED forecasting model development, the few significant limitations of these methods associated in the analysis of time series data make the models inadequate in many practical situations. Therefore, in this paper, Machine Learning (ML)-based Random Forest (RF) regressor, and Deep Neural Network (DNN)-based Long Short-Term Memory (LSTM) and Convolutional Neural network (CNN) methods, which have not been explored to the same extent as the other time series techniques, are implemented by incorporating meteorological and calendar parameters for the development of forecasting models. The performances of the developed three models in forecasting ED patient arrivals are evaluated. Among the three models, CNN outperformed for short-term (3 days in advance) patient arrivals prediction with Mean Absolute Percentage Error (MAPE) of 9.24% and LSTM performed better for moderate-term (7 days in advance) patient arrivals prediction with MAPE of 8.91% using weather forecast information. Whereas, LSTM model outperformed with MAPE of 8.04% compared to 9.53% by CNN and 10.10% by RF model for current day prediction of patient arrivals using 3 days past weather information. Thus, for short-term ED patient arrivals forecasting, DNN-based model performed better compared to RF regressor ML-based model. Graphical abstract: Image 1 Highlights: Emergency Department (ED) patient arrivals forecasting models are proposed. Random Forest (RF) regressor, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) methods are used. Models are implemented by incorporating meteorological and calendar variables. The performances of three different models in forecasting the ED arrivals are evaluated. Achieved the Mean Average Percentage Error (MAPE) of 8.04% by LSTM model for the current day forecasting of ED arrivals. Achieved the MAPE of 8.91% by LSTM for the 7 days in advance forecasting of patient arrivals. Among the three models, LSTM outperformed for current day and weekly patient arrivals prediction compared to CNN and RF.