<?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-Om0I-1790883433-c689657840</doi_batch_id>
		<timestamp>1790883433</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>2025</year>
				</publication_date>
				<journal_volume>
					<volume>5</volume>
				</journal_volume>
				<issue>1</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Machine Learning for Patient Transfer and Discharge Planning: A Review of Predictive Models for Placement Delay, Discharge Readiness, Follow-Up Completion, and Post-Discharge Risk</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Hassan</given_name>
            <surname>Ali</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Mariam</given_name>
            <surname>Farooq</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Usman</given_name>
            <surname>Shah</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2025</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/c689657840</doi>
					<resource>https://cirpublications.com/pub/journal/2/article/c689657840</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/c689657840-7311f265-7d38-4957-a2f9-89bf5b289161">
					  <unstructured_citation>Pahlevani M, Taghavi M, Vanberkel P. A systematic literature review of predicting patient discharges using statistical methods and machine learning. Health Care Manag Sci. 2024;27(3):458-65.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-64079e15-bbf8-499c-9346-511d3d0cdb18">
					  <unstructured_citation>Levin S, Barnes S, Toerper M, Debraine A, DeAngelo A, Hamrock E, et al. Machine-learning-based hospital discharge predictions can support multidisciplinary rounds and decrease hospital length-of-stay. BMJ Innov. 2021;7(2):388-95.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-655c2c30-de84-4fd4-81b1-5172955d2fe7">
					  <unstructured_citation>Bertsimas D, Pauphilet J, Stevens J, Tandon M. Predicting inpatient flow at a major hospital using interpretable analytics. Manuf Serv Oper Manag. 2022;24(6):2809-24.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-8d8d079f-0141-4d9d-8b7b-8a53e69c79eb">
					  <unstructured_citation>Mickle CF, Deb D. Early prediction of patient discharge disposition in acute neurological care using machine learning. BMC Health Serv Res. 2022;22(1):1281.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-95c62987-f9f4-4699-a9e2-2c5b535da315">
					  <unstructured_citation>Mahmoudi E, Kamdar N, Kim N, Gonzales G, Singh K, Waljee AK. Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review. BMJ. 2020;369:m958.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-cad2b6b8-237d-4a5b-a35d-9af8c4024a78">
					  <unstructured_citation>Golas SB, Shibahara T, Agboola S, Otaki H, Sato J, Nakae T, et al. A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data. BMC Med Inform Decis Mak. 2018;18(1):44.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-9b4663b3-257f-43da-ac74-c28daa379c08">
					  <unstructured_citation>Eckert C, Nieves-Robbins N, Spieker E, Louwers T, Hazel D, Marquardt J, et al. Development and prospective validation of a machine learning-based risk of readmission model in a large military hospital. Appl Clin Inform. 2019;10(2):316-25.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-214f50b8-cec0-41b8-af12-ae9d24679073">
					  <unstructured_citation>Huang Y, Talwar A, Lin Y, Aparasu RR. Machine learning methods to predict 30-day hospital readmission outcome among US adults with pneumonia: analysis of the national readmission database. BMC Med Inform Decis Mak. 2022;22(1):288.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-35e24ff8-e593-4701-8451-a831469071dc">
					  <unstructured_citation>Pishgar M, Theis J, Del Rios M, Ardati A, Anahideh H, Darabi H. Prediction of unplanned 30-day readmission for ICU patients with heart failure. BMC Med Inform Decis Mak. 2022;22(1):117.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-ea9a1f3c-093e-418e-b737-05180a7b371b">
					  <unstructured_citation>Michailidis P, Dimitriadou A, Papadimitriou T, Gogas P. Forecasting hospital readmissions with machine learning. Healthcare (Basel). 2022;10(6):981.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-0bb6b93b-6168-4e68-80d2-5ebd0246fb6c">
					  <unstructured_citation>Alvarez-Romero C, Martinez-Garcia A, Vega JT, Díaz-Jimènez P, Jimènez-Juan C, Nieto-Martín MD, et al. Predicting 30-day readmission risk for patients with chronic obstructive pulmonary disease through a federated machine learning architecture on FAIR data: development and validation study. JMIR Med Inform. 2022;10(6):e35307.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-578ee935-830b-454d-b821-b95636b44acf">
					  <unstructured_citation>Safavi KC, Khaniyev T, Copenhaver M, Seelen M, Zenteno Langle AC, Zanger J, et al. Development and validation of a machine learning model to aid discharge processes for inpatient surgical care. JAMA Netw Open. 2019;2(12):e1917221.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-3690efca-06cd-4803-960d-16d5a8a2ae48">
					  <unstructured_citation>Ahn I, Gwon H, Kang H, Kim Y, Seo H, Choi H, et al. Machine learning-based hospital discharge prediction for patients with cardiovascular diseases: development and usability study. JMIR Med Inform. 2021;9(11):e32662.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-837c1dd2-cde0-49ec-81fe-6d33baec4407">
					  <unstructured_citation>Zhang X, Yan C, Malin BA, Patel MB, Chen Y. Predicting next-day discharge via electronic health record access logs. J Am Med Inform Assoc. 2021;28(12):2670-80.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-36148b0a-2250-4738-b80c-54482f9c8555">
					  <unstructured_citation>Bishop JA, Javed HA, El-Bouri R, Zhu T, Taylor T, Peto T, et al. Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge. PLoS One. 2021;16(11):e0260476.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-37bff10e-fc4a-4fb5-b6c2-272ce9d430e9">
					  <unstructured_citation>Wei J, Zhou J, Zhang Z, Yuan K, Gu Q, Luk A, et al. Predicting individual patient and hospital-level discharge using machine learning. Commun Med (Lond). 2024;4(1):236.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-51233630-fe09-425f-9dc2-6abd2f085ee7">
					  <unstructured_citation>Mahyoub MA, Dougherty K, Yadav RR, Berio-Dorta R, Shukla A. Development and validation of a machine learning model integrated with the clinical workflow for inpatient discharge date prediction. Front Digit Health. 2024;6:1455446.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-57022bc4-991f-4601-9cee-499c7216c4ad">
					  <unstructured_citation>Lee SY, Eagleson RM, Hearld LR, Gibson MJ, Hearld KR, Hall AG, et al. Leveraging machine learning to enhance appointment adherence at a novel post-discharge care transition clinic. JAMIA Open. 2024;7(4):ooae086.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-5e4ddc71-e270-4a85-adca-4af5dcd2c9b0">
					  <unstructured_citation>Coppa K, Kim EJ, Oppenheim MI, Bock KR, Conigliaro J, Hirsch JS. Examination of post-discharge follow-up appointment status and 30-day readmission. J Gen Intern Med. 2021;36(5):1214-21.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-56815aa5-602b-4603-964b-285a7466567f">
					  <unstructured_citation>Yu MY, Son YJ. Machine learning-based 30-day readmission prediction models for patients with heart failure: a systematic review. Eur J Cardiovasc Nurs. 2024;23(7):711-9.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-6678a980-3efb-4412-b524-7cb9fc583bee">
					  <unstructured_citation>Verma VK, Lin WY. Machine learning-based 30-day hospital readmission predictions for COPD patients using physical activity data of daily living with accelerometer-based device. Biosensors (Basel). 2022;12(8):605.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-f93a9035-9855-42c5-97f7-a835c1560d50">
					  <unstructured_citation>Tschoellitsch T, Maletzky A, Moser P, Seidl P, Böck C, Mahečić TT, et al. Machine learning prediction of unexpected readmission or death after discharge from intensive care: a retrospective cohort study. J Clin Anesth. 2024;99:111654.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-cc28f426-da64-49e7-a4a6-c5507986242e">
					  <unstructured_citation>Xie F, Liu N, Yan L, Ning Y, Lim KK, Gong C, et al. Development and validation of an interpretable machine learning scoring tool for estimating time to emergency readmissions. EClinicalMedicine. 2022;45:101308.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-328861a6-5b77-403e-960b-1ee8407bab70">
					  <unstructured_citation>Zhang Y, Xiang T, Wang Y, Shu T, Yin C, Li H, et al. Explainable machine learning for predicting 30-day readmission in acute heart failure patients. iScience. 2024;27(7):110154.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-b8a60844-c317-41ff-b9e4-fb015acd703c">
					  <unstructured_citation>Jamei M, Nisnevich A, Wetchler E, Sudat S, Liu E. Predicting all-cause risk of 30-day hospital readmission using artificial neural networks. PLoS One. 2017;12(7):e0181173.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-e9911150-8f30-4cf9-b032-3d50067241f7">
					  <unstructured_citation>Li L, Wang L, Lu L, Zhu T. Machine learning prediction of postoperative unplanned 30-day hospital readmission in older adult. Front Mol Biosci. 2022;9:910688.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-5387003e-0d56-4f7f-a0c8-67a94cbf24a7">
					  <unstructured_citation>Zahid S, Agrawal A, Salman F, Khan MZ, Ullah W, Teebi A, et al. Development and validation of a machine learning risk-prediction model for 30-day readmission for heart failure following transcatheter aortic valve replacement (TAVR-HF score). Curr Probl Cardiol. 2024;49(2):102143.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-c76417aa-1dc0-4513-8155-1ee4f3e64e88">
					  <unstructured_citation>Liu Y, Du L, Li L, Xiong L, Luo H, Kwaku E, et al. Development and validation of a machine learning-based readmission risk prediction model for non-ST elevation myocardial infarction patients after percutaneous coronary intervention. Sci Rep. 2024;14(1):13393.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-79de75e3-9926-4d10-b950-733a69375f4f">
					  <unstructured_citation>Pham MK, Mai TT, Crane M, Ebiele M, Brennan R, Ward ME, et al. Forecasting patient early readmission from Irish hospital discharge records using conventional machine learning models. Diagnostics (Basel). 2024;14(21):2405.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-f58dc204-1c9e-4701-bfd6-3f26b9a10f3d">
					  <unstructured_citation>Jahangiri S, Abdollahi M, Rashedi E, Azadeh-Fard N. A machine learning model to predict heart failure readmission: toward optimal feature set. Front Artif Intell. 2024;7:1363226.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/c689657840-234dd2aa-846b-4f7c-acd1-de90e52337ca">
					  <unstructured_citation>Bopche R, Gustad LT, Afset JE, Ehrnström B, Damås JK, Nytrø Ø. In-hospital mortality, readmission, and prolonged length of stay risk prediction leveraging historical electronic patient records. JAMIA Open. 2024;7(3):ooae074</unstructured_citation>
											</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
