Forecasting Future Monthly Patient Volume using Deep Learning and Statistical Models

Abstract

The variety of diseases is increasing day by day, and the demand for hospitals, especially for emergency and radiology units, is also increasing. As in other units, it is necessary to prepare the radiology unit for the future, to take into account the needs and to plan for the future. Due to the radiation emitted by the devices in the radiology unit, minimizing the time spent by the patients for the radiological image is of vital importance both for the unit staff and the patient. In order to solve the aforementioned problem, in this study, it is desired to estimate the monthly number of images in the radiology unit by using deep learning models and statistical-based models, and thus to be prepared for the future in a more planned way. For prediction processes, both deep learning models such as LSTM, MLP, NNAR and ELM, as well as statistical based prediction models such as ARIMA, SES, TBATS, HOLT and THETAF were used. In order to evaluate the performance of the models, the symmetric mean absolute percentage error (sMAPE) and mean absolute scaled error (MASE) metrics, which have been in demand recently, were preferred. The results showed that the LSTM model outperformed the deep learning group in estimating the monthly number of radiological case images, while the AUTO.ARIMA model performed better in the statistical-based group. It is believed that the findings obtained will speed up the procedures of the patients who come to the hospital and are referred to the radiology unit, and will facilitate the hospital managers in managing the patient flow more efficiently, increasing both the service quality and patient satisfaction, and making important contributions to the future planning of the hospital.