comparison of long short-term memory and convolutional neural network models for emergency department patients’ arrival daily forecasting

Background: one of the most critical challenges in the emergency department (ed) is overcrowding, which creates negative consequences for patients and staff. therefore, predicting the rate of patients entering the ed can help manage resources in this department effectively. objectives: according to the time of data collection, we intended to predict the volume of patient admissions to the ed in epidemic conditions, such as covid-19 and non-epidemic. in addition, we planned to compare the performance of the lstm and cnn models. methods: the collected data consists of three main categories. the first category pertains to air pollutants, provided by the tehran air quality control organization. the second type relates to data from the iran meteorological organization, and the third category includes the number of patients admitted to the ed of a hospital in tehran. we also incorporated binary indicators for epidemic and non-epidemic conditions in the dataset. the data collection period spans from february 2018 to march 2022. we employed the dickey-fuller test to assess the stationarity of the data. after preprocessing, we independently developed long short-term memory (lstm) neural network and convolutional neural network (cnn) models, considering various time windows of previous days. keras and tensorflow libraries in python, along with the google colab environment, were utilized to execute the models. results: the lstm model exhibited the lowest root mean square error (rmse) and mean absolute error (mae) with a time window of the last seven days, while the cnn model outperformed the lstm model with a time window of the previous 13 days. additionally, the cnn model required less execution time than the lstm model. conclusions: in conclusion, deep learning algorithms prove suitable for analyzing multivariate time series data. the cnn model demonstrated the lowest prediction error.