Forecasting Future Monthly Patient Volume Using Deep Learning and Statistical Models

Overview

Journal Wirel Pers Commun

Publisher Springer

Date 2023 May 11

PMID 37168439

Authors

Zeydin Pala

Ramazan Atici

Erkan Yaldiz

Affiliations

Soon will be listed here.

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.

References

Harrou F, Dairi A, Kadri F, Sun Y . Forecasting emergency department overcrowding: A deep learning framework. Chaos Solitons Fractals. 2020; 139:110247. PMC: 7505132. DOI: 10.1016/j.chaos.2020.110247. View

Luo L, Luo L, Zhang X, He X . Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models. BMC Health Serv Res. 2017; 17(1):469. PMC: 5504658. DOI: 10.1186/s12913-017-2407-9. View

LeCun Y, Bengio Y, Hinton G . Deep learning. Nature. 2015; 521(7553):436-44. DOI: 10.1038/nature14539. View

Kulkarni S, Seneviratne N, Baig M, Khan A . Artificial Intelligence in Medicine: Where Are We Now?. Acad Radiol. 2019; 27(1):62-70. DOI: 10.1016/j.acra.2019.10.001. View

Moghadas S, Shoukat A, Fitzpatrick M, Wells C, Sah P, Pandey A . Projecting hospital utilization during the COVID-19 outbreaks in the United States. Proc Natl Acad Sci U S A. 2020; 117(16):9122-9126. PMC: 7183199. DOI: 10.1073/pnas.2004064117. View

Maleki M, Mahmoudi M, Heydari M, Pho K . Modeling and forecasting the spread and death rate of coronavirus (COVID-19) in the world using time series models. Chaos Solitons Fractals. 2020; 140:110151. PMC: 7381941. DOI: 10.1016/j.chaos.2020.110151. View

Doornik J, Castle J, Hendry D . Short-term forecasting of the coronavirus pandemic. Int J Forecast. 2020; 38(2):453-466. PMC: 7486833. DOI: 10.1016/j.ijforecast.2020.09.003. View

Zhu T, Luo L, Zhang X, Shi Y, Shen W . Time-Series Approaches for Forecasting the Number of Hospital Daily Discharged Inpatients. IEEE J Biomed Health Inform. 2017; 21(2):515-526. DOI: 10.1109/JBHI.2015.2511820. View

van Assen M, Muscogiuri G, Caruso D, Lee S, Laghi A, De Cecco C . Artificial intelligence in cardiac radiology. Radiol Med. 2020; 125(11):1186-1199. DOI: 10.1007/s11547-020-01277-w. View

10.

Jones S, Thomas A, Evans R, Welch S, Haug P, Snow G . Forecasting daily patient volumes in the emergency department. Acad Emerg Med. 2008; 15(2):159-70. DOI: 10.1111/j.1553-2712.2007.00032.x. View

11.

Jones S, Evans R, Allen T, Thomas A, Haug P, Welch S . A multivariate time series approach to modeling and forecasting demand in the emergency department. J Biomed Inform. 2008; 42(1):123-39. DOI: 10.1016/j.jbi.2008.05.003. View

12.

Sun Y, Heng B, Seow Y, Seow E . Forecasting daily attendances at an emergency department to aid resource planning. BMC Emerg Med. 2009; 9:1. PMC: 2640341. DOI: 10.1186/1471-227X-9-1. View

13.

van Lent W, Deetman J, Teertstra H, Muller S, Hans E, van Harten W . Reducing the throughput time of the diagnostic track involving CT scanning with computer simulation. Eur J Radiol. 2012; 81(11):3131-40. DOI: 10.1016/j.ejrad.2012.03.012. View

14.

Nuti S, Vainieri M . Managing waiting times in diagnostic medical imaging. BMJ Open. 2012; 2(6). PMC: 3533122. DOI: 10.1136/bmjopen-2012-001255. View

15.

Jilani T, Housley G, Figueredo G, Tang P, Hatton J, Shaw D . Short and Long term predictions of Hospital emergency department attendances. Int J Med Inform. 2019; 129:167-174. DOI: 10.1016/j.ijmedinf.2019.05.011. View

16.

Hochreiter S, Schmidhuber J . Long short-term memory. Neural Comput. 1997; 9(8):1735-80. DOI: 10.1162/neco.1997.9.8.1735. View

17.

Zhang Y, Luo L, Zhang F, Kong R, Yang J, Feng Y . Emergency patient flow forecasting in the radiology department. Health Informatics J. 2020; 26(4):2362-2374. DOI: 10.1177/1460458220901889. View

18.

Poyiadji N, Klochko C, LaForce J, Brown M, Griffith B . COVID-19 and Radiology Resident Imaging Volumes-Differential Impact by Resident Training Year and Imaging Modality. Acad Radiol. 2020; 28(1):106-111. PMC: 7528904. DOI: 10.1016/j.acra.2020.09.010. View