<?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-FMPr-1790878114-a527202896</doi_batch_id>
		<timestamp>1790878114</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>2026</year>
				</publication_date>
				<journal_volume>
					<volume>5</volume>
				</journal_volume>
				<issue>2</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Digital Twin Framework Integrating Patient-Specific Computational Models and Real-Time Wearable Data for Personalized Management of Chronic Obstructive Pulmonary Disease Exacerbations</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Mohamed</given_name>
            <surname>Salah</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Youssef</given_name>
            <surname>Karim</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Ahmed</given_name>
            <surname>Nabil</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Mahmoud</given_name>
            <surname>Adel</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Karim</given_name>
            <surname>Hassan</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2026</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/a527202896</doi>
					<resource>https://cirpublications.com/pub/journal/1/article/a527202896</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/a527202896-3975b576-d1c6-4b6c-a1b7-8d95a9652dfd">
					  <unstructured_citation>Adibi A, Sin DD, Safari A, Johnson KM, Aaron SD, FitzGerald JM, et al. The acute COPD exacerbation prediction tool (ACCEPT): a modelling study. Lancet Respir Med. 2020;8(10):1013-21.</unstructured_citation>
						 <doi>10.1016/S2213-2600(20)30197-2</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-701279d0-2a16-44ec-ab98-5fb3c7b727b6">
					  <unstructured_citation>Safari A, Adibi A, Sin DD, Lee TY, Ho JK, Sadatsafavi M, et al. ACCEPT 2.0: recalibrating and externally validating the acute COPD exacerbation prediction tool (ACCEPT). EClinicalMedicine. 2022;51:101567.</unstructured_citation>
						 <doi>10.1016/j.eclinm.2022.101567</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-bd91edc2-376c-48be-b52b-4fe3a5b10938">
					  <unstructured_citation>Wu CT, Li GH, Huang CT, Cheng YC, Chen CH, Chien JY, et al. Acute exacerbation of chronic obstructive pulmonary disease prediction system using wearable device data, machine learning, and deep learning: development and cohort study. JMIR Mhealth Uhealth. 2021;9(5):e22591.</unstructured_citation>
						 <doi>10.2196/22591</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-f033c254-9629-463e-a8c7-785da050255a">
					  <unstructured_citation>Chmiel FP, Burns DK, Pickering JB, Blythin A, Wilkinson TM, Boniface MJ, et al. Prediction of chronic obstructive pulmonary disease exacerbation events by using patient self-reported data in a digital health app: statistical evaluation and machine learning approach. JMIR Med Inform. 2022;10(3):e26499.</unstructured_citation>
						 <doi>10.2196/26499</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-6bad60ee-c58f-4629-a5a5-22a35aa631f1">
					  <unstructured_citation>Laubenbacher R, Mehrad B, Shmulevich I, Trayanova N. Digital twins in medicine. Nat Comput Sci. 2024;4(3):184-91.</unstructured_citation>
						 <doi>10.1038/s43588-024-00615-3</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-487ff68f-a5ec-4a8a-8d25-96f79271a523">
					  <unstructured_citation>Masison J, Beezley J, Mei Y, Ribeiro HA, Knapp AC, Sordo Vieira L, et al. A modular computational framework for medical digital twins. Proc Natl Acad Sci U S A. 2021;118(20):e2024287118.</unstructured_citation>
						 <doi>10.1073/pnas.2024287118</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-7ee9b39a-a908-44d9-a63b-5974aabf86ae">
					  <unstructured_citation>Katsoulakis E, Wang Q, Wu H, Shahriyari L, Fletcher R, Liu J, et al. Digital twins for health: a scoping review. NPJ Digit Med. 2024;7(1):77.</unstructured_citation>
						 <doi>10.1038/s41746-024-01073-0</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-8f58a842-e9fb-4b89-90af-348c277d8f64">
					  <unstructured_citation>Gonsard A, Genet M, Drummond D. Digital twins for chronic lung diseases. Eur Respir Rev. 2024;33(174):240105.</unstructured_citation>
						 <doi>10.1183/16000617.0105-2024</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-290fa18f-395c-4ef1-8158-2332ac1b4e87">
					  <unstructured_citation>Sun T, He X, Li Z. Digital twin in healthcare: recent updates and challenges. Digit Health. 2023;9:20552076221149651.</unstructured_citation>
						 <doi>10.1177/20552076221149651</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-9fc6e094-80c0-454f-937c-4142bc9b892a">
					  <unstructured_citation>Roth CJ, Becher T, Frerichs I, Weiler N, Wall WA. Coupling of EIT with computational lung modeling for predicting patient-specific ventilatory responses. J Appl Physiol (1985). 2017;122(4):855-67.</unstructured_citation>
						 <doi>10.1152/japplphysiol.00554.2016</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-c5a38091-6318-4dcd-8ba7-ebe39f6cc4f2">
					  <unstructured_citation>Neelakantan S, Xin Y, Gaver DP, Cereda M, Rizi R, Smith BJ, et al. Computational lung modelling in respiratory medicine. J R Soc Interface. 2022;19(191):20220082.</unstructured_citation>
						 <doi>10.1098/rsif.2022.0082</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-c9b0e839-73a4-499c-99d5-e5fe09ffb9d5">
					  <unstructured_citation>Wu R, Liaqat D, de Lara E, Son T, Rudzicz F, Alshaer H, et al. Feasibility of using a smartwatch to intensively monitor patients with chronic obstructive pulmonary disease: prospective cohort study. JMIR Mhealth Uhealth. 2018;6(6):e10046.</unstructured_citation>
						 <doi>10.2196/10046</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-a5b7719f-aebf-43ee-ba34-2fea55471307">
					  <unstructured_citation>Wu RC, Ginsburg S, Son T, Gershon AS. Using wearables and self-management apps in patients with COPD: a qualitative study. ERJ Open Res. 2019;5(3):00095-2019.</unstructured_citation>
						 <doi>10.1183/23120541.00095-2019</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-81f9e173-3bff-4c8b-b533-bec2b786b8b8">
					  <unstructured_citation>Coutu FA, Iorio OC, Ross BA. Remote patient monitoring strategies and wearable technology in chronic obstructive pulmonary disease. Front Med (Lausanne). 2023;10:1236598.</unstructured_citation>
						 <doi>10.3389/fmed.2023.1236598</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-c74581e2-5aeb-40a2-90b2-81a5e4f8daf6">
					  <unstructured_citation>Iorio OC, Coutu FA, Malaeb D, Ross BA. Feasibility, functionality, and user experience with wearable technologies for acute exacerbation monitoring in patients with severe COPD. Front Signal Process. 2024;4:1362754.</unstructured_citation>
						 <doi>10.3389/frsip.2024.1362754</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-26955159-7109-4e88-a2e9-6b7e6ae4d5c3">
					  <unstructured_citation>Giroux M, Ladjal H, Beuve M, Giraud P, Shariat B. Patient-specific biomechanical modeling of the lung tumor for radiation therapy. Comput Methods Biomech Biomed Engin. 2017;20(sup1):S95-6.</unstructured_citation>
						 <doi>10.1080/10255842.2017.1382882</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-7513a060-38a4-4f37-824e-ce4dac7274d4">
					  <unstructured_citation>Geitner CM, Becher T, Frerichs I, Weiler N, Bates JH, Wall WA, et al. An approach to study recruitment/derecruitment dynamics in a patient-specific computational model of an injured human lung. Int J Numer Method Biomed Eng. 2023;39(9):e3745.</unstructured_citation>
						 <doi>10.1002/cnm.3745</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-c73640b2-0568-400e-9996-2ea78b52d582">
					  <unstructured_citation>Lerios T, Knopp JL, Holder-Pearson L, Guy EF, Chase JG. An identifiable model of lung mechanics to diagnose and monitor COPD. Comput Biol Med. 2023;152:106430.</unstructured_citation>
						 <doi>10.1016/j.compbiomed.2022.106430</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-7b558dd3-33be-4545-9133-1af693ccd3a5">
					  <unstructured_citation>Li X, Loscalzo J, Mahmud AF, Aly DM, Rzhetsky A, Zitnik M, et al. Digital twins as global learning health and disease models for preventive and personalized medicine. Genome Med. 2025;17(1):11.</unstructured_citation>
						 <doi>10.1186/s13073-024-01409-2</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-65fa09b7-721e-4d40-9c71-b50c98b5a665">
					  <unstructured_citation>Cuperus LJ, Bult L, van Zelst CM, van den Brink WJ, Kamstra KR, van den Broek TJ, et al. Wearable technology for detection of COPD exacerbations: feasibility of the Health Patch. ERJ Open Res. 2024;10(6):00396-2024.</unstructured_citation>
						 <doi>10.1183/23120541.00396-2024</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-d76d22ae-aa5e-4174-ace2-03e5b50c8f13">
					  <unstructured_citation>Hawthorne G, Richardson M, Greening NJ, Esliger D, Briggs-Price S, Chaplin EJ, et al. A proof of concept for continuous, non-invasive, free-living vital signs monitoring to predict readmission following an acute exacerbation of COPD: a prospective cohort study. Respir Res. 2022;23(1):102.</unstructured_citation>
						 <doi>10.1186/s12931-022-02006-1</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-73cc26f9-c00c-47d4-ad2e-d2c48b4c1f53">
					  <unstructured_citation>Shah AJ, Althobiani MA, Saigal A, Ogbonnaya CE, Hurst JR, Mandal S, et al. Wearable technology interventions in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis. NPJ Digit Med. 2023;6(1):222.</unstructured_citation>
						 <doi>10.1038/s41746-023-00962-0</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-41b75eee-904a-4f5f-a60f-9d1fc3321118">
					  <unstructured_citation>Glyde HM, Morgan C, Wilkinson TM, Nabney IT, Dodd JW. Remote patient monitoring and machine learning in acute exacerbations of chronic obstructive pulmonary disease: dual systematic literature review and narrative synthesis. J Med Internet Res. 2024;26:e52143.</unstructured_citation>
						 <doi>10.2196/52143</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-b3564812-a403-4c91-a281-a6cd9764b4e9">
					  <unstructured_citation>Cooper CB, Sirichana W, Neufeld EV, Taylor M, Wang X, Dolezal BA, et al. Statistical process control improves the feasibility of remote physiological monitoring in patients with chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis. 2019;14:2485-96.</unstructured_citation>
						 <doi>10.2147/COPD.S222699</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-b4d90f2f-5b97-4544-a413-76271fc2bdeb">
					  <unstructured_citation>Al Rajeh A, Bhogal AS, Zhang Y, Costello JT, Hurst JR, Mani AR, et al. Application of oxygen saturation variability analysis for the detection of exacerbation in individuals with COPD: a proof-of-concept study. Physiol Rep. 2021;9(23):e15132.</unstructured_citation>
						 <doi>10.14814/phy2.15132</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-4641fb33-91ca-4efd-b89a-8f3ec80c5d03">
					  <unstructured_citation>Nguyen HQ, Moy ML, Liu IL, Fan VS, Gould MK, Desai SA, et al. Effect of physical activity coaching on acute care and survival among patients with chronic obstructive pulmonary disease: a pragmatic randomized clinical trial. JAMA Netw Open. 2019;2(8):e199657.</unstructured_citation>
						 <doi>10.1001/jamanetworkopen.2019.9657</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-496bbb60-6f62-47f9-9325-8606d0371a0e">
					  <unstructured_citation>Wan ES, Kantorowski A, Polak M, Kadri R, Richardson CR, Gagnon DR, et al. Long-term effects of web-based pedometer-mediated intervention on COPD exacerbations. Respir Med. 2020;162:105878.</unstructured_citation>
						 <doi>10.1016/j.rmed.2020.105878</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-3670bf12-b6ea-41b8-8663-47fb9aa2d11a">
					  <unstructured_citation>Zeng S, Arjomandi M, Tong Y, Liao ZC, Luo G. Developing a machine learning model to predict severe chronic obstructive pulmonary disease exacerbations: retrospective cohort study. J Med Internet Res. 2022;24(1):e28953.</unstructured_citation>
						 <doi>10.2196/28953</doi> 					</citation>
          					<citation key="rk-10.68159/a527202896-f068e690-7754-4b0b-93f6-524d3a64adec">
					  <unstructured_citation>Jo YS, Han S, Lee D, Min KH, Park SJ, Yoon HK, et al. Development of a daily predictive model for the exacerbation of chronic obstructive pulmonary disease. Sci Rep. 2023;13(1):18669.</unstructured_citation>
						 <doi>10.1038/s41598-023-45710-4</doi> 					</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
