<?xml version="1.0" encoding="UTF-8"?><doi_batch version="4.3.7" xmlns="http://www.crossref.org/schema/4.3.7" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.3.7 http://www.crossref.org/schema/deposit/crossref4.3.7.xsd">
		<head>
		<doi_batch_id>cirpublications.com-USjz-1790880670-e265368208</doi_batch_id>
		<timestamp>1790880670</timestamp>
		<depositor>
			<depositor_name>Clinical Intelligence Research Press</depositor_name>
			<email_address>info@cirpublications.com</email_address>
		</depositor>
		<registrant>Clinical Intelligence Research Press</registrant>
	</head>
	<body>
		<journal>
			<journal_metadata>
				<full_title>Journal of Artificial Intelligence for Healthcare Systems</full_title>
				<abbrev_title>J. Artif. Intell. Healthc. Syst.</abbrev_title>
				<issn>3149-8981</issn>
			</journal_metadata>
			<journal_issue>
				<publication_date>
					<year>2025</year>
				</publication_date>
				<journal_volume>
					<volume>4</volume>
				</journal_volume>
				<issue>2</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Federated Reinforcement Learning for Coordinated Bed Allocation and Nurse Staffing During Pandemic Surges</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Lucia</given_name>
            <surname>Morales</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Diego</given_name>
            <surname>Perez</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Valeria</given_name>
            <surname>Soto</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Martin</given_name>
            <surname>Alvarez</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Fernando</given_name>
            <surname>Diaz</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2025</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/e265368208</doi>
					<resource>https://cirpublications.com/pub/journal/1/article/e265368208</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/e265368208-d8a9e5b4-ccf4-4214-bfee-772f7555fbc9">
					  <unstructured_citation>Wu Q, Han J, Yan Y, Kuo YH, Shen ZJ. Reinforcement learning for healthcare operations management: methodological framework, recent developments, and future research directions. Health Care Manag Sci. 2025;28(2):298-333.</unstructured_citation>
						 <doi>10.1007/s10729-024-09716-2</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-a8a6cf4b-083c-4ce8-b66e-cf2938fd73b5">
					  <unstructured_citation>Lee S, Lee YH. Improving emergency department efficiency by patient scheduling using deep reinforcement learning. Healthcare (Basel). 2020;8(2):77.</unstructured_citation>
						 <doi>10.3390/healthcare8020077</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-9d09a151-8b24-4033-bfe1-5d5be15ce31e">
					  <unstructured_citation>Muklason A, Kusuma SD, Riksakomara E, Premananda IG, Anggraeni W, Mahananto F, et al. Solving nurse rostering optimization problem using reinforcement learning-simulated annealing with reheating hyper-heuristics algorithm. Procedia Comput Sci. 2024;234:486-93.</unstructured_citation>
						 <doi>10.1016/j.procs.2024.03.031</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-7a681520-8321-4ccd-9092-5edfd4050b69">
					  <unstructured_citation>Sharma S, Guleria K. A comprehensive review on federated learning based models for healthcare applications. Artif Intell Med. 2023;146:102691.</unstructured_citation>
						 <doi>10.1016/j.artmed.2023.102691</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-9d7b7640-6933-4c7b-92c4-c523ebefbeef">
					  <unstructured_citation>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.</unstructured_citation>
						 <doi>10.1038/s42256-020-0186-1</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-1c16bd34-4eed-4f5f-a2aa-348841e87aae">
					  <unstructured_citation>Li X, Gu Y, Dvornek N, Staib LH, Ventola P, Duncan JS. Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results. Med Image Anal. 2020;65:101765.</unstructured_citation>
						 <doi>10.1016/j.media.2020.101765</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-c877f298-8c8a-45e4-83db-4e56e1de5d6b">
					  <unstructured_citation>Yang D, Xu Z, Li W, Myronenko A, Roth HR, Harmon S, et al. Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan. Med Image Anal. 2021;70:101992.</unstructured_citation>
						 <doi>10.1016/j.media.2021.101992</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-e5b89bbe-bed5-4df9-9dab-c51f1f7ee835">
					  <unstructured_citation>Schäfer F, Walther M, Grimm DG, Hübner A. Combining machine learning and optimization for the operational patient-bed assignment problem. Health Care Manag Sci. 2023;26(4):785-801.</unstructured_citation>
						 <doi>10.1007/s10729-023-09644-2</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-4d55d826-1256-4f38-8d29-1f54a746f5cd">
					  <unstructured_citation>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.</unstructured_citation>
						 <doi>10.1186/s12911-022-01777-y</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-61f9b532-07c7-4c28-9465-af253183d07d">
					  <unstructured_citation>Jaotombo F, Pauly V, Fond G, Orleans V, Auquier P, Ghattas B, et al. Machine-learning prediction for hospital length of stay using a French medico-administrative database. J Mark Access Health Policy. 2023;11(1):2149318.</unstructured_citation>
						 <doi>10.1080/20016689.2022.2149318</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-b555c02c-f0a7-40b4-89cb-097638184c75">
					  <unstructured_citation>Kim JK. Enhancing patient flow in emergency departments: a machine learning and simulation-based resource scheduling approach. Appl Sci. 2024;14(10):4264.</unstructured_citation>
						 <doi>10.3390/app14104264</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-9843d29b-5df0-407e-bcbf-f15f48c5f767">
					  <unstructured_citation>Wood RM, McWilliams CJ, Thomas MJ, Bourdeaux CP, Vasilakis C. COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care. Health Care Manag Sci. 2020;23(3):315-24.</unstructured_citation>
						 <doi>10.1007/s10729-020-09511-7</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-ce214d85-2b94-4413-b2b8-34e4d5c0ca37">
					  <unstructured_citation>Melman GJ, Parlikad AK, Cameron EA. Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation. Health Care Manag Sci. 2021;24(2):356-74.</unstructured_citation>
						 <doi>10.1007/s10729-021-09548-7</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-f8c8fa5a-18c2-4273-9986-cf4ca9ad0ac4">
					  <unstructured_citation>Baas S, Dijkstra S, Braaksma A, van Rooij P, Snijders FJ, Tiemessen L, et al. Real-time forecasting of COVID-19 bed occupancy in wards and intensive care units. Health Care Manag Sci. 2021;24(2):402-19.</unstructured_citation>
						 <doi>10.1007/s10729-021-09549-6</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-6d461fa8-15fb-409a-8e05-7726e314dae5">
					  <unstructured_citation>Johnson MR, Naik H, Chan WS, Greiner J, Michaleski M, Liu D, et al. Forecasting ward-level bed requirements to aid pandemic resource planning: lessons learned and future directions. Health Care Manag Sci. 2023;26(3):477-500.</unstructured_citation>
						 <doi>10.1007/s10729-023-09617-5</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-eb7d6803-fdf8-41e7-bb75-71fb384793a0">
					  <unstructured_citation>Bertsimas D, Boussioux L, Cory-Wright R, Delarue A, Digalakis V, Jacquillat A, et al. From predictions to prescriptions: a data-driven response to COVID-19. Health Care Manag Sci. 2021;24(2):253-72.</unstructured_citation>
						 <doi>10.1007/s10729-020-09542-0</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-dd1003a8-e764-47e7-8e34-cf86fb2487b3">
					  <unstructured_citation>Bekker R, Uit het Broek M, Koole G. Modeling COVID-19 hospital admissions and occupancy in the Netherlands. Eur J Oper Res. 2023;304(1):207-18.</unstructured_citation>
						 <doi>10.1016/j.ejor.2022.02.051</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-a1759ce7-3ad3-4688-98bf-25af8b355205">
					  <unstructured_citation>Redondo E, Nicoletta V, Bélanger V, Garcia-Sabater JP, Landa P, Maheut J, et al. A simulation model for predicting hospital occupancy for COVID-19 using archetype analysis. Healthc Anal. 2023;3:100197.</unstructured_citation>
						 <doi>10.1016/j.health.2023.100197</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-8ba12201-47d2-4272-ab45-0d33236301fe">
					  <unstructured_citation>Burdett RL, Corry P, Cook D, Yarlagadda P. Analytical techniques for supporting hospital case mix planning encompassing forced adjustments, comparisons, and scoring. Healthcare (Basel). 2025;13(1):47.</unstructured_citation>
						 <doi>10.3390/healthcare13010047</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-aab87f04-17ec-4fb1-81c3-72f9028f144a">
					  <unstructured_citation>Wang ST, Weng SJ, Yeh TY, Chen CH, Tsai YT. Optimizing emergency department patient flow through bed allocation strategies: a discrete-event simulation study. Inquiry. 2025;62:00469580251335799.</unstructured_citation>
						 <doi>10.1177/00469580251335799</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-d72c0e93-b6e5-4789-9654-efacd03e04b1">
					  <unstructured_citation>Hunstein D, Fiebig M. Staff management with AI: predicting the nursing workload. In: Nursing Informatics 2024. Amsterdam: IOS Press; 2024. p. 231-5.</unstructured_citation>
						 <doi>10.3233/SHTI240390</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-41b61a3c-0360-437a-b94d-45b8e9f3dbce">
					  <unstructured_citation>Aslan M, Toros E. Machine learning in optimising nursing care delivery models: an empirical analysis of hospital wards. J Eval Clin Pract. 2025;31(1):e70001.</unstructured_citation>
						 <doi>10.1111/jep.70001</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-39aa7b60-700f-4d8b-aacd-c520754f355c">
					  <unstructured_citation>Renggli FJ, Gerlach M, Bieri JS, Golz C, Sariyar M. Integrating nurse preferences into AI-based scheduling systems: qualitative study. JMIR Form Res. 2025;9(1):e67747.</unstructured_citation>
						 <doi>10.2196/67747</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-34d76dc3-3781-4fb5-a78d-3d9fd12e890c">
					  <unstructured_citation>McMahon M, Plate S, Herz T, Brenner G, Kleinknecht-Dolf M, Krauthammer M. Development of a data-based method for predicting nursing workload in an acute care hospital: methodological study. J Med Internet Res. 2025;27:e66667.</unstructured_citation>
						 <doi>10.2196/66667</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-46248dcf-bd86-4434-87ec-0fada1e6fecc">
					  <unstructured_citation>Tyler S, Olis M, Aust N, Patel L, Simon L, Triantafyllidis C, et al. Use of artificial intelligence in triage in hospital emergency departments: a scoping review. Cureus. 2024;16(5):e59808.</unstructured_citation>
						 <doi>10.7759/cureus.59808</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-ca399ed5-46d9-4563-9e67-84e5dde89737">
					  <unstructured_citation>Da’Costa A, Teke J, Origbo JE, Osonuga A, Egbon E, Olawade DB. AI-driven triage in emergency departments: a review of benefits, challenges, and future directions. Int J Med Inform. 2025;197:105838.</unstructured_citation>
						 <doi>10.1016/j.ijmedinf.2025.105838</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-125533ac-03e1-4146-958b-4e179baf1f7c">
					  <unstructured_citation>El Arab RA, Al Moosa OA. The role of AI in emergency department triage: an integrative systematic review. Intensive Crit Care Nurs. 2025;89:104058.</unstructured_citation>
						 <doi>10.1016/j.iccn.2025.104058</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-c1c8ba54-c806-4bbb-b011-9e8689c6b385">
					  <unstructured_citation>Seo H, Ahn I, Gwon H, Kang H, Kim Y, Choi H, et al. Forecasting hospital room and ward occupancy using static and dynamic information concurrently: retrospective single-center cohort study. JMIR Med Inform. 2024;12:e53400.</unstructured_citation>
						 <doi>10.2196/53400</doi> 					</citation>
          					<citation key="rk-10.68159/e265368208-f7956829-37e1-4fb5-a9c4-f2797d15f32c">
					  <unstructured_citation>Prakash MK, Kaushal S, Bhattacharya S, Chandran A, Kumar A, Ansumali S. A minimal and adaptive prediction strategy for critical resource planning in a pandemic. medRxiv. 2020;2020.04.10.20061247.</unstructured_citation>
						 <doi>10.1101/2020.04.10.20061247</doi> 					</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
