<?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-cXlM-1790883434-q083800313</doi_batch_id>
		<timestamp>1790883434</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 Health Informatics and Digital Systems</full_title>
				<abbrev_title>J. Health Inform. Digit. Syst.</abbrev_title>
				<issn>3149-8973</issn>
			</journal_metadata>
			<journal_issue>
				<publication_date>
					<year>2024</year>
				</publication_date>
				<journal_volume>
					<volume>4</volume>
				</journal_volume>
				<issue>1</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Predictive Analytics for Hospital Resource Allocation: A Review of Machine Learning Models for Bed Management, Staffing Demand, Equipment Use, Diagnostic Capacity, and Patient Throughput</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Sven</given_name>
            <surname>Larsson</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Erik</given_name>
            <surname>Johansson</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Anna</given_name>
            <surname>Nilsson</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2024</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/q083800313</doi>
					<resource>https://cirpublications.com/pub/journal/2/article/q083800313</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/q083800313-50202014-8dfc-4e75-b28b-b73c2fc3057c">
					  <unstructured_citation>Kutafina E, Bechtold I, Kabino K, Jonas SM. Recursive neural networks in hospital bed occupancy forecasting. BMC Med Inform Decis Mak. 2019;19(1):39.</unstructured_citation>
						 <doi>10.1186/s12911-019-0742-7</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-a8c819b4-55ea-402c-a6df-cd2d2ec49e4e">
					  <unstructured_citation>Tello M, Reich ES, Puckey J, Maff R, Garcia-Arce A, Feijoo F, 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/s12911-022-01788-6</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-69288c4b-6095-41db-84d5-63697a20186e">
					  <unstructured_citation>Hong WS, Haimovich AD, Taylor RA. Predicting hospital admission at emergency department triage using machine learning. PLoS One. 2018;13(7):e0201016.</unstructured_citation>
						 <doi>10.1371/journal.pone.0201016</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-ee64a80e-f59b-4265-99d1-b2af4588e34d">
					  <unstructured_citation>Graham B, Bond R, Quinn M, Mulvenna M. Using data mining to predict hospital admissions from the emergency department. IEEE Access. 2018;6:10458-69.</unstructured_citation>
						 <doi>10.1109/ACCESS.2018.2804791</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-f858dfe6-4570-49fa-bab1-03b7ef951106">
					  <unstructured_citation>Heins J, Schoenfelder J, Heider S, Heller AR, Brunner JO. A scalable forecasting framework to predict COVID-19 hospital bed occupancy. INFORMS J Appl Anal. 2022;52(6):508-23.</unstructured_citation>
						 <doi>10.1287/inte.2022.1123</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-3d653426-bb16-4948-a1e8-526b002c7109">
					  <unstructured_citation>Dijkstra S, Baas S, Braaksma A, Boucherie RJ. Dynamic fair balancing of COVID-19 patients over hospitals based on forecasts of bed occupancy. Omega. 2023;116:102801.</unstructured_citation>
						 <doi>10.1016/j.omega.2022.102801</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-ec508112-57bc-4bf8-b6bf-d0d1ba52b6b6">
					  <unstructured_citation>Fenn A, Davis C, Buckland DM, Kapadia N, Nichols M, et al. Development and validation of machine learning models to predict admission from emergency department to inpatient and intensive care units. Ann Emerg Med. 2021;78(2):290-302.</unstructured_citation>
						 <doi>10.1016/j.annemergmed.2021.02.014</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-c121207a-a785-40e4-b760-651adc7ea12c">
					  <unstructured_citation>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.</unstructured_citation>
						 <doi>10.3390/jcm11236888</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-a6aca2d2-ee62-477e-ac81-84fcce79a473">
					  <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):785-802.</unstructured_citation>
						 <doi>10.1007/s10729-022-09612-7</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-c117a1b1-c5b6-4e4a-b958-3763ed10c9de">
					  <unstructured_citation>Turgeman L, May JH, Sciulli R. Insights from a machine learning model for predicting the hospital length of stay at the time of admission. Expert Syst Appl. 2017;78:376-85.</unstructured_citation>
						 <doi>10.1016/j.eswa.2017.02.030</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-071faad3-b0b2-4a7b-b452-17831459cd31">
					  <unstructured_citation>Harrou F, Dairi A, Kadri F, Sun Y. Effective forecasting of key features in hospital emergency department: hybrid deep learning-driven methods. Mach Learn Appl. 2022;7:100200.</unstructured_citation>
						 <doi>10.1016/j.mlwa.2021.100200</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-b8fb9bfd-1595-4df4-b32e-1df162d1f433">
					  <unstructured_citation>Daghistani TA, Elshawi R, Sakr S, Ahmed AM, Al-Thwayee A, Al-Mallah MH. Predictors of in-hospital length of stay among cardiac patients: a machine learning approach. Int J Cardiol. 2019;288:140-7.</unstructured_citation>
						 <doi>10.1016/j.ijcard.2019.04.067</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-0acc067d-b000-4624-bcd1-f7d960dfb643">
					  <unstructured_citation>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.</unstructured_citation>
						 <doi>10.1177/1460458219892402</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-b528b788-9f82-4b83-ad6d-d312cd04c281">
					  <unstructured_citation>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.</unstructured_citation>
						 <doi>10.1007/s10278-021-00551-3</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-d783b5e1-0931-44ac-a69d-bd6adaa6e813">
					  <unstructured_citation>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.</unstructured_citation>
						 <doi>10.1016/j.health.2023.100245</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-1fd51dfe-f6cd-4289-b811-e84ebaffd81e">
					  <unstructured_citation>Li N, Arnold DM, Down DG, Barty R, Blake J, Chiang F, et al. From demand forecasting to inventory ordering decisions for red blood cells through integrating machine learning, statistical modeling, and inventory optimization. Transfusion. 2022;62(1):87-99.</unstructured_citation>
						 <doi>10.1111/trf.16769</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-b69423c8-3c54-4952-bb14-5db0d9e42f5e">
					  <unstructured_citation>Li N, Pham T, Cheng C, McElfresh DC, Metcalf RA, Russell WA, et al. Blood demand forecasting and supply management: an analytical assessment of key studies utilizing novel computational techniques. Transfus Med Rev. 2023;37(4):150768.</unstructured_citation>
						 <doi>10.1016/j.tmrv.2023.150768</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-1a8b118a-d77b-4fa5-86b5-87206be0a0c8">
					  <unstructured_citation>Moreno-Fergusson ME, Guerrero Rueda WJ, Ortiz Basto GA, Arevalo Sandoval IA, Sanchez-Herrera B. Analytics and lean health care to address nurse care management challenges for inpatients in emerging economies. J Nurs Scholarsh. 2021;53(6):803-14.</unstructured_citation>
						 <doi>10.1111/jnu.12700</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-ff0bb813-4d10-44b0-bf0a-426e353cdd56">
					  <unstructured_citation>Chrusciel J, Girardon F, Roquette L, Laplanche D, Duclos A, Sanchez S. The prediction of hospital length of stay using unstructured data. BMC Med Inform Decis Mak. 2021;21(1):351.</unstructured_citation>
						 <doi>10.1186/s12911-021-01656-x</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-1488fca2-ccd0-4a4e-bdf1-c4de019d9374">
					  <unstructured_citation>Klumpp M, Hintze M, Immonen M, Ródenas-Rigla F, Pilati F, Aparicio-Martínez F, et al. Artificial intelligence for hospital health care: application cases and answers to challenges in European hospitals. Healthcare. 2021;9(8):961.</unstructured_citation>
						 <doi>10.3390/healthcare9080961</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-78dd37f9-7bcc-4ef3-a5bc-34cfc75c1a5d">
					  <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/q083800313-025926b4-8fca-4ba4-8bd0-3e1f97429603">
					  <unstructured_citation>Abuhay TM, Robinson S, Mamuye A, Kovalchuk SV. Machine learning integrated patient flow simulation: why and how? J Simul. 2023;17(5):580-93.</unstructured_citation>
						 <doi>10.1080/17477778.2022.2091624</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-0b77909a-5276-47b0-81ea-5b75dba28fe7">
					  <unstructured_citation>Shbool MA, Arabeyyat O, Al-Bazi A, Al-Hyari A, Salem A, Abu-Hmaid T, et al. Machine learning approaches to predict patient’s length of stay in emergency department. Appl Comput Intell Soft Comput. 2023;2023:8063846.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q083800313-d06fdecc-2ce1-4166-b9de-f35823c325fc">
					  <unstructured_citation>Alsinglawi B, Alshari O, Alorjani M, Mubin O, Alnajjar F, Novoa M, et al. An explainable machine learning framework for lung cancer hospital length of stay prediction. Sci Rep. 2022;12(1):607.</unstructured_citation>
						 <doi>10.1038/s41598-021-04623-3</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-6331b2b5-da82-4cc8-87da-651e3f796f66">
					  <unstructured_citation>Zeleke AJ, Palumbo P, Tubertini P, Miglio R, Chiari L. Machine learning-based prediction of hospital prolonged length of stay admission at emergency department: a gradient boosting algorithm analysis. Front Artif Intell. 2023;6:1179226.</unstructured_citation>
						 <doi>10.3389/frai.2023.1179226</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-b9aad6bb-e19c-4d5e-9102-3cb83a94970c">
					  <unstructured_citation>Strum RP, Mowbray FI, Zargoush M, Jones AP. Prehospital prediction of hospital admission for emergent acuity patients transported by paramedics: a population-based cohort study using machine learning. PLoS One. 2023;18(8):e0289429.</unstructured_citation>
						 <doi>10.1371/journal.pone.0289429</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-9fa25f47-286e-41b2-ab61-f17cf9abe325">
					  <unstructured_citation>Tully JL, Zhong W, Simpson S, Curran BP, Macias AA, Waterman RS, et al. Machine learning prediction models to reduce length of stay at ambulatory surgery centers through case resequencing. J Med Syst. 2023;47(1):71.</unstructured_citation>
						 <doi>10.1007/s10916-023-01969-2</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-af0ff0a7-b844-4d40-963f-aad6655befd8">
					  <unstructured_citation>Tuominen J, Lomio F, Oksala N, Palomäki A, Peltonen J, Huttunen H, et al. Forecasting daily emergency department arrivals using high-dimensional multivariate data: a feature selection approach. BMC Med Inform Decis Mak. 2022;22(1):134.</unstructured_citation>
						 <doi>10.1186/s12911-022-01904-9</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-99945558-2ee0-41e9-afcc-3fa72d4167c8">
					  <unstructured_citation>Larburu N, Azkue L, Kerexeta J. Predicting hospital ward admission from the emergency department: a systematic review. J Pers Med. 2023;13(5):849.</unstructured_citation>
						 <doi>10.3390/jpm13050849</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-be272369-0d1e-4dbf-af70-11dc9a4671cb">
					  <unstructured_citation>Kishore K, Braitberg G, Holmes NE, Bellomo R. Early prediction of hospital admission of emergency department patients. Emerg Med Australas. 2023;35(4):572-88.</unstructured_citation>
						 <doi>10.1111/1742-6723.14169</doi> 					</citation>
          					<citation key="rk-10.68159/q083800313-abe4e2ed-227e-46ea-add4-03e65fe29c24">
					  <unstructured_citation>Raita Y, Goto T, Faridi MK, Brown DF, Camargo CA Jr, Hasegawa K. Emergency department triage prediction of clinical outcomes using machine learning models. Crit Care. 2019;23(1):64.</unstructured_citation>
						 <doi>10.1186/s13054-019-2351-7</doi> 					</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
