Temporal Graph Transformer for Predicting Interdepartmental Workflow Congestion Using Patient Movement Data, Service Requests, Staff Schedules, Unit-Level Dependency Networks, and Real-Time Capacity Signals

cirpublications.com-uzxL-1790883435-l907800310 1790883435 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 2 Temporal Graph Transformer for Predicting Interdepartmental Workflow Congestion Using Patient Movement Data, Service Requests, Staff Schedules, Unit-Level Dependency Networks, and Real-Time Capacity Signals Elif Yilmaz Mehmet Demir Ayse Kaya Hasan Aydin 2026 10.68159/l907800310 https://cirpublications.com/pub/journal/2/article/l907800310 El-Bouri R, Taylor T, Youssef A, Zhu T, Clifton DA. Machine learning in patient flow: a review. Prog Biomed Eng. 2021;3(2):022002. 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. Pianykh OS, Guitron S, Parke D, Zhang C, Pandharipande P, Brink J, et al. Improving healthcare operations management with machine learning. Nat Mach Intell. 2020;2(5):266-73. Nguyen Q, Wybrow M, Burstein F, Taylor D, Enticott J. Understanding the impacts of health information systems on patient flow management: A systematic review across several decades of research. PLoS One. 2022;17(9):e0274493. Hong WS, Haimovich AD, Taylor RA. Predicting hospital admission at emergency department triage using machine learning. PLoS One. 2018;13(7):e0201016. De Hond A, Raven W, Schinkelshoek L, Gaakeer M, Ter Avest E, Sir O, et al. Machine learning for developing a prediction model of hospital admission of emergency department patients: Hype or hope? Int J Med Inform. 2021;152:104496. Patel D, Cheetirala SN, Raut G, Tamegue J, Kia A, Glicksberg B, et al. Predicting adult hospital admission from emergency department using machine learning: An inclusive gradient boosting model. J Clin Med. 2022;11(23):6888. Choi E, Bahadori MT, Song L, Stewart WF, Sun J. GRAM: Graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM; 2017. p. 787-95. Reychav I, McHaney R, Babbar S, Weragalaarachchi K, Azaizah N, Nevet A. Graph network techniques to model and analyze emergency department patient flow. Mathematics. 2022;10(9):1526. Rossi E, Chamberlain B, Frasca F, Eynard D, Monti F, Bronstein M. Temporal graph networks for deep learning on dynamic graphs. arXiv. 2020;arXiv:2006.10637. Kumar S, Zhang X, Leskovec J. Predicting dynamic embedding trajectory in temporal interaction networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York: ACM; 2019. p. 1269-78. Xu D, Ruan C, Korpeoglu E, Kumar S, Achan K. Inductive representation learning on temporal graphs. arXiv. 2020;arXiv:2002.07962. Sankar A, Wu Y, Gou L, Zhang W, Yang H. DySAT: Deep neural representation learning on dynamic graphs via self-attention networks. In: Proceedings of the 13th ACM International Conference on Web Search and Data Mining. New York: ACM; 2020. p. 519-27. Pareja A, Domeniconi G, Chen J, Ma T, Suzumura T, Kanezashi H, et al. EvolveGCN: Evolving graph convolutional networks for dynamic graphs. Proc AAAI Conf Artif Intell. 2020;34(4):5363-70. Wang Z, Li Q, Yu D, Han X. Temporal graph transformer for dynamic network. In: International Conference on Artificial Neural Networks. Cham: Springer; 2022. p. 694-705. Zhong Y, Huang C. A dynamic graph representation learning based on temporal graph transformer. Alex Eng J. 2023;63:359-69. Chen H, Jiao P, Du M, Guo X, Zhao Z, Jin D, et al. TGFormer: Towards temporal graph transformer with auto-correlation mechanism. Pattern Recognit. 2026;170:112053. Yu B, Yin H, Zhu Z. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv. 2017;arXiv:1709.04875. Guo S, Lin Y, Feng N, Song C, Wan H. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. Proc AAAI Conf Artif Intell. 2019;33(1):922-9. Wu Z, Pan S, Long G, Jiang J, Zhang C. Graph WaveNet for deep spatial-temporal graph modeling. arXiv. 2019;arXiv:1906.00121. Zheng C, Fan X, Wang C, Qi J. GMAN: A graph multi-attention network for traffic prediction. Proc AAAI Conf Artif Intell. 2020;34(1):1234-41. Zhang X, Huang C, Xu Y, Xia L, Dai P, Bo L, et al. Traffic flow forecasting with spatial-temporal graph diffusion network. Proc AAAI Conf Artif Intell. 2021;35(17):15008-15. Jiang W, Luo J. Graph neural network for traffic forecasting: A survey. Expert Syst Appl. 2022;207:117921. Abuhay TM, Nigatie YG, Metsker OG, Yakovlev AN, Kovalchuk SV. Investigating coordination of hospital departments in delivering healthcare for acute coronary syndrome patients using data-driven network analysis. In: International Conference on Computational Science. Cham: Springer; 2020. p. 430-40. Nas S, Koyuncu M. Emergency department capacity planning: A recurrent neural network and simulation approach. Comput Math Methods Med. 2019;2019:4359719. Abuhay TM, Robinson S, Mamuye A, Kovalchuk SV. Machine learning integrated patient flow simulation: Why and how? J Simul. 2023;17(5):580-93. Yuan H, Yu H, Gui S, Ji S. Explainability in graph neural networks: A taxonomic survey. IEEE Trans Pattern Anal Mach Intell. 2023;45(5):5782-99. Li B, Li Q, Zhou X, Deng M, Ling H. Dynamic Graph Transformer with Spatio-Temporal Attention for Streamflow Forecasting. Hydrology. 2025;12(12):322.