cirpublications.com-iupU-1790883434-k357291895 1790883434 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Health Informatics and Digital Systems J. Health Inform. Digit. Syst. 3149-8973 2025 5 2 Ahmed Youssef Khaled Hassan Mahmoud Elamin 2025 10.68159/k357291895 https://cirpublications.com/pub/journal/2/article/k357291895 Côté MJ, Smith MA. Forecasting the demand for radiology services. Health Syst. 2018;7(2):79-88. Mohammed EA, Naugler C. Open-source software for demand forecasting of clinical laboratory test volumes using time-series analysis. J Pathol Inform. 2017;8(1):7. 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. Huang D, Wu Z. Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization. PLoS One. 2017;12(2):e0172539. Klute B, Homb A, Chen W, Stelpflug A. Predicting outpatient appointment demand using machine learning and traditional methods. J Med Syst. 2019;43(9):288. Asheim A, Bache-Wiig Bjørnsen LP, Næss-Pleym LE, Uleberg O, Dale J, Nilsen SM. Real-time forecasting of emergency department arrivals using prehospital data. BMC Emerg Med. 2019;19(1):42. Zhang Y, Luo L, Zhang F, Kong R, Yang J, Feng Y, et al. Emergency patient flow forecasting in the radiology department. Health Informatics J. 2020;26(4):2362-74. Chong LR, Tsai KT, Lee LL, Foo SG, Chang PC. Artificial intelligence predictive analytics in the management of outpatient MRI appointment no-shows. AJR Am J Roentgenol. 2020;215(5):1155-62. Qiu H, Luo L, Su Z, Zhou L, Wang L, Chen Y, et al. Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure. BMC Med Inform Decis Mak. 2020;20(1):83. Huang Y, Xu C, Ji M, Xiang W, He D. Medical service demand forecasting using a hybrid model based on ARIMA and self-adaptive filtering method. BMC Med Inform Decis Mak. 2020;20(1):237. Tello M, Reich ES, Puckey J, Maff R, Garcia-Arce A, Bhattacharya BS, et al. Machine learning based forecast for the prediction of inpatient bed demand. BMC Med Inform Decis Mak. 2022;22(1):55. Liu X, Gu F, Bai Z, Huang Q, Ma G. Forecasting of daily outpatient visits based on genetic programming. Iran J Public Health. 2022;51(6):1313-21. King Z, Farrington J, Utley M, Kung E, Elkhodair S, Harris S, et al. Machine learning for real-time aggregated prediction of hospital admission for emergency patients. NPJ Digit Med. 2022;5(1):104. Susnjak T, Maddigan P. Forecasting patient flows with pandemic induced concept drift using explainable machine learning. EPJ Data Sci. 2023;12(1):11. Mahmoudian Y, Nemati A, Safaei AS. A forecasting approach for hospital bed capacity planning using machine learning and deep learning with application to public hospitals. Healthc Anal. 2023;4:100245. Peláez-Rodríguez C, Torres-López R, Pérez-Aracil J, López-Laguna N, Sánchez-Rodríguez S, Salcedo-Sanz S. An explainable machine learning approach for hospital emergency department visits forecasting using continuous training and multi-model regression. Comput Methods Programs Biomed. 2024;245:108033. Zhou L, Zhu Q, Chen Q, Wang P, Huang H. Predicting hospital outpatient volume using XGBoost: a machine learning approach. Sci Rep. 2025;15(1):17028. Orhan F, Kurutkan MN. Predicting total healthcare demand using machine learning: separate and combined analysis of predisposing, enabling, and need factors. BMC Health Serv Res. 2025;25(1):366. Chen CW, Hsieh LL, Chu BX. Structural time series modelling for weekly forecasting of enterovirus outpatient, inpatient, and emergency department visits. PLoS One. 2025;20(5):e0323070. Wang X, Aski SN, Uhlemann F, Gupta V, Amthor T. Predicting slot lengths of MRI exams to decrease observed discrepancies between planning and execution. Curr Probl Diagn Radiol. 2024;53(3):359-68. Zander A, Reuter-Oppermann M. Data in ambulatory care logistics: What modelers need and what practice can offer. Health Care Manag Sci. 2025;28(3):505-26. Blanco J, Ferreras M, Cosido O. Predictive modeling of hospital emergency department demand using artificial intelligence: A systematic review. Int J Med Inform. 2025;193:106215. de Almeida JG, Messiou C, Withey SJ, Matos C, Koh DM, Papanikolaou N, et al. Medical machine learning operations: a framework to facilitate clinical AI development and deployment in radiology. Eur Radiol. 2025;35(11):6828-41. Aloini D, Benevento E, Berdini M, Stefanini A. Predicting radiology service times for enhancing emergency department management. Socio Econ Plann Sci. 2025;99:102208. Rahimi Z, Li N, Down DG, Arnold DM. Clustering-based demand forecasting with an application to immunoglobulin products. Oper Res Health Care. 2025;45:200469. Tuominen J, Pulkkinen E, Peltonen J, Kanniainen J, Oksala N, Palomäki A, et al. Forecasting emergency department occupancy with advanced machine learning models and multivariable input. Int J Forecast. 2024;40(4):1410-20. Ricciardi C, Marino MR, Trunfio TA, Majolo M, Romano M, Amato F, et al. Evaluation of different machine learning algorithms for predicting the length of stay in the emergency departments: a single-centre study. Front Digit Health. 2024;5:1323849. Vural O, Ozaydin B, Aram KY, Booth J, Lindsey BF, Ahmed A, et al. An artificial intelligence-based framework for predicting emergency department overcrowding: development and evaluation study. JMIR Med Inform. 2025;13:e73960. Ackermann AE, Fani V, Bandinelli R, Sellitto MA. Short-term prediction in an emergency healthcare unit: comparison between ARIMA, ANN, and logistic map models. Forecasting. 2025;7(3):52.