Patient volume forecasting in the radiology department using deep learning and statistical-based models

Abstract As the demand for medical care increases significantly every day, the issue of managing the volume of patients in hospitals and radiology units becomes more and more important. Due to the radiation emitted by the devices in the radiology unit, the minimum time spent by the patients for radiological imaging in hospitals is of vital importance both for the hospital and the patient. This study aims to estimate the monthly number of images in the hospital radiology unit using deep learning models and statistical-based models, so that it is prepared for the future in a more planned way. While deep learning models such as LSTM, MLP, NNAR and ELM were used for forecasting, statistics- based prediction models such as ARIMA, SES, TBATS, HOLT, and THETAF were also used. In order to evaluate the performance of the models, symmetric mean absolute percentage error (sMAPE), and mean absolute scaled error (MASE), which are very popular recently, were used. Results show that while the LSTM model performed better than the deep learning group in estimating the number of monthly radiological case images, the ARIMA model performed better in the statistical-based group. It is believed that the findings obtained will make important contributions to the future planning of the hospital by increasing both the service quality and patient satisfaction by facilitating the hospital managers in managing the volume of patients coming to the hospital and transferred to the radiology unit more efficiently.