Federated Multi-Task Learning Framework for Predicting Service Demand Across Emergency, Imaging, Pharmacy, Laboratory, and Inpatient Units without Sharing Patient-Level Operational Data

cirpublications.com-sTFH-1790880677-d768883933 1790880677 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 2026 6 1 Federated Multi-Task Learning Framework for Predicting Service Demand Across Emergency, Imaging, Pharmacy, Laboratory, and Inpatient Units without Sharing Patient-Level Operational Data Gabriel Costa Rafael Mendes Bruno Teixeira Lucas Ribeiro 2026 10.68159/d768883933 https://cirpublications.com/pub/journal/2/article/d768883933 Porto BM, Fogliatto FS. Enhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning. BMC Med Inform Decis Mak. 2024;24(1):377. Becker AS, Erinjeri JP, Chaim J, Kastango N, Elnajjar P, Hricak H, et al. Automatic forecasting of radiology examination volume trends for optimal resource planning and allocation. J Digit Imaging. 2022;35(1):1–8. 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. Pall R, Gauthier Y, Auer S, Mowaswes W. Predicting drug shortages using pharmacy data and machine learning. Health Care Manag Sci. 2023;26(3):395–411. 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. Zhang Y, Yang Q. A survey on multi-task learning. IEEE Trans Knowl Data Eng. 2021;34(12):5586–609. Vandenhende S, Georgoulis S, Van Gansbeke W, Proesmans M, Dai D, Van Gool L. Multi-task learning for dense prediction tasks: A survey. IEEE Trans Pattern Anal Mach Intell. 2022;44(7):3614–33. Brisimi TS, Chen R, Mela T, Olshevsky A, Paschalidis IC, Shi W. Federated learning of predictive models from federated electronic health records. Int J Med Inform. 2018;112:59–67. Guo P, Wang P, Zhou J, Jiang S, Patel VM. Multi-institutional collaborations for improving deep learning-based magnetic resonance image reconstruction using federated learning. In: Proc IEEE/CVF Conf Comput Vis Pattern Recognit; 2021. p. 2423–32. Rieke N, Hancox J, Li W, Milletari F, Roth HR, Albarqouni S, et al. The future of digital health with federated learning. NPJ Digit Med. 2020;3(1):119. Harrou F, Dairi A, Kadri F, Sun Y. Forecasting emergency department overcrowding: A deep learning framework. Chaos Solitons Fractals. 2020;139:110247. Pala Z, Atıcı R, Yaldız E. Forecasting future monthly patient volume using deep learning and statistical models. Wireless Pers Commun. 2023;130(2):1479–502. Rojas F, Yáñez J, Cortés M. Hybrid statistical–metaheuristic inventory modeling: Integrating SARIMAX with skew-normal and zero-inflated errors in clinical laboratory demand forecasting. Mathematics. 2025;13(18):3001. Chang K, Balachandar N, Lam C, Yi D, Brown J, Beers A, et al. Distributed deep learning networks among institutions for medical imaging. J Am Med Inform Assoc. 2018;25(8):945–54. Xu J, Glicksberg BS, Su C, Walker P, Bian J, Wang F. Federated learning for healthcare informatics. J Healthc Inform Res. 2021;5(1):1–19. 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. Barrow D, Kourentzes N, Utley M, Kirkland H. Drug demand forecasting for hospital pharmacies using temporal hierarchies. J Oper Res Soc. 2026;77(1):1–8. Lim B, Arık SÖ, Loeff N, Pfister T. Temporal fusion transformers for interpretable multi-horizon time series forecasting. Int J Forecast. 2021;37(4):1748–64. Bonawitz K, Ivanov V, Kreuter B, Marcedone A, McMahan HB, Patel S, et al. Practical secure aggregation for privacy-preserving machine learning. In: Proc ACM SIGSAC Conf Comput Commun Secur; 2017. p. 1175–91. Kaissis GA, Makowski MR, Rückert D, Braren RF. Secure, privacy-preserving and federated machine learning in medical imaging. Nat Mach Intell. 2020;2(6):305–11. Li T, Sahu AK, Talwalkar A, Smith V. Federated learning: Challenges, methods, and future directions. IEEE Signal Process Mag. 2020;37(3):50–60. Whitt W, Zhang X. Forecasting arrivals and occupancy levels in an emergency department. Oper Res Health Care. 2019;21:1–8. Yuan W, Ye G, Zhao X, Nguyen QV, Cao Y, Yin H. Tackling data heterogeneity in federated time series forecasting. arXiv. 2024;arXiv:2411.15716. Smith V, Chiang CK, Sanjabi M, Talwalkar AS. Federated multi-task learning. In: Adv Neural Inf Process Syst. 2017;30. Beaulieu-Jones BK, Wu ZS, Williams C, Lee R, Bhavnani SP, Byrd JB, et al. Privacy-preserving generative deep neural networks support clinical data sharing. Circ Cardiovasc Qual Outcomes. 2019;12(7):e005122. Cheng Q, Argon NT, Evans CS, Liu Y, Platts-Mills TF, Ziya S. Forecasting emergency department hourly occupancy using time series analysis. Am J Emerg Med. 2021;48:177–82. Kairouz P, McMahan HB, Avent B, Bellet A, Bennis M, Bhagoji AN, et al. Advances and open problems in federated learning. Found Trends Mach Learn. 2021;14(1–2):1–210. Sheller MJ, Reina GA, Edwards B, Martin J, Bakas S. Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation. In: Int MICCAI Brainlesion Workshop; 2018. p. 92–104. Manthe M, Duffner S, Lartizien C. Federated brain tumor segmentation: An extensive benchmark. Med Image Anal. 2024;97:103270.