<?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-krai-1790899041-d049294389</doi_batch_id>
		<timestamp>1790899041</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>2021</year>
				</publication_date>
				<journal_volume>
					<volume>1</volume>
				</journal_volume>
				<issue>1</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Temporal Episode Modeling in Inpatient Care: A Formal Representation Standard for Longitudinal Trajectory Analytics</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Hiroshi</given_name>
            <surname>Tanaka</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Yuki</given_name>
            <surname>Sato</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Kenji</given_name>
            <surname>Mori</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Rina</given_name>
            <surname>Okabe</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Takashi</given_name>
            <surname>Ito</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2021</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/d049294389</doi>
					<resource>https://cirpublications.com/pub/journal/2/article/d049294389</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/d049294389-0b6f5539-31e9-408e-b2bd-48e77c039aee">
					  <unstructured_citation>Pham T, Tran T, Phung D, Venkatesh S. Predicting healthcare trajectories from medical records: A deep learning approach. J Biomed Inform. 2017;69:218-29.</unstructured_citation>
						 <doi>10.1016/j.jbi.2017.04.001</doi> 					</citation>
          					<citation key="rk-10.68159/d049294389-bd08aedd-2e6f-458f-9ccf-44869650fd9f">
					  <unstructured_citation>Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, et al. Scalable and accurate deep learning with electronic health records. npj Digit Med. 2018;1:18.</unstructured_citation>
						 <doi>10.1038/s41746-018-0029-1</doi> 					</citation>
          					<citation key="rk-10.68159/d049294389-09e6af08-fa34-4a21-a7f7-27d12dcd3f21">
					  <unstructured_citation>Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56.</unstructured_citation>
						 <doi>10.1038/s41591-018-0300-7</doi> 					</citation>
          					<citation key="rk-10.68159/d049294389-77770401-fe0d-4eba-87e1-d743e9b450be">
					  <unstructured_citation>Wiens J, Saria S, Sendak M, Ghassemi M, Liu VX, Doshi-Velez F, et al. Do no harm: a roadmap for responsible machine learning for health care. Nat Med. 2019;25(9):1337-40.</unstructured_citation>
						 <doi>10.1038/s41591-019-0548-6</doi> 					</citation>
          					<citation key="rk-10.68159/d049294389-fc42f8ea-4862-4e0d-99a5-a60256b87dfb">
					  <unstructured_citation>Ghassemi M, Naumann T, Schulam P, Beam AL, Chen IY, Ranganath R. Practical guidance on artificial intelligence for health-care data. Lancet Digit Health. 2019;1(4):e157-e159.</unstructured_citation>
						 <doi>10.1016/S2589-7500(19)30023-0</doi> 					</citation>
          					<citation key="rk-10.68159/d049294389-c7c04e8e-5b26-4051-868f-0826cc084a42">
					  <unstructured_citation>Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. npj Digit Med. 2018;1:39.</unstructured_citation>
						 <doi>10.1038/s41746-018-0040-6</doi> 					</citation>
          					<citation key="rk-10.68159/d049294389-b758a166-549b-40c2-b7e6-9c470710ad0a">
					  <unstructured_citation>Reddy S, Allan S, Coghlan S, Cooper P. A governance model for the application of AI in health care. J Am Med Inform Assoc. 2020;27(3):491-7.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d049294389-86b07add-6803-4d14-b50c-e585ae27f029">
					  <unstructured_citation>Hernandez-Boussard T, Bozkurt S, Ioannidis JPA, Shah NH. MINIMAR (MINImum information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care. J Am Med Inform Assoc. 2020;27(12):2011-5.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d049294389-938e2b93-1705-4f95-8d38-3e9d734ae063">
					  <unstructured_citation>Norgeot B, Quer G, Beaulieu-Jones B, Torkamani A, Dias R, Gianfrancesco M, et al. Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist. Nat Med. 2020;26(9):1320-4.</unstructured_citation>
						 <doi>10.1038/s41591-020-1041-y</doi> 					</citation>
          					<citation key="rk-10.68159/d049294389-bfb8c0f2-4339-4598-9407-f20fe9528d15">
					  <unstructured_citation>Sounderajah V, Ashrafian H, Aggarwal R, De Fauw J, Denniston AK, Greaves F, et al. Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: The STARD-AI Steering Group. Nat Med. 2020;26(6):807-8.</unstructured_citation>
						 <doi>10.1038/s41591-020-0941-1</doi> 					</citation>
          					<citation key="rk-10.68159/d049294389-15f09ba5-5583-499c-9221-6093a87fd254">
					  <unstructured_citation>Liu X, Rivera SC, Moher D, Calvert MJ, Denniston AK. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med. 2020;26(9):1364-74.</unstructured_citation>
						 <doi>10.1038/s41591-020-1034-x</doi> 					</citation>
          					<citation key="rk-10.68159/d049294389-68fdcf6e-36b6-43d5-b26c-208fe02674f5">
					  <unstructured_citation>Rivera SC, Liu X, Chan AW, Denniston AK, Calvert MJ. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med. 2020;26(9):1351-63.</unstructured_citation>
						 <doi>10.1038/s41591-020-1037-7</doi> 					</citation>
          					<citation key="rk-10.68159/d049294389-6f6e09d6-e992-4777-8395-69ccb5b66829">
					  <unstructured_citation>McCradden MD, Joshi S, Mazwi M, Anderson JA. Ethical limitations of algorithmic fairness solutions in health care machine learning. Lancet Digit Health. 2020;2(5):e221-e223.</unstructured_citation>
						 <doi>10.1016/S2589-7500(20)30065-0</doi> 					</citation>
          					<citation key="rk-10.68159/d049294389-597aba35-550a-42f2-8f42-1ca843c257a3">
					  <unstructured_citation>Sendak MP, Gao M, Brajer N, Balu S. Presenting machine learning model information to clinical end users with model facts labels. npj Digit Med. 2020;3:41.</unstructured_citation>
						 <doi>10.1038/s41746-020-0253-3</doi> 					</citation>
          					<citation key="rk-10.68159/d049294389-ccaf3c36-8e91-4c1d-bac8-7525cb16ff33">
					  <unstructured_citation>Huang SC, Pareek A, Seyyedi S, Banerjee I, Lungren MP. Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines. npj Digit Med. 2020;3:136.</unstructured_citation>
						 <doi>10.1038/s41746-020-00341-z</doi> 					</citation>
          					<citation key="rk-10.68159/d049294389-7fb7babf-e442-4f8e-bdb8-ced74a49f0fd">
					  <unstructured_citation>Zhou Y, Wang F, Tang J, Nussinov R, Cheng F. Artificial intelligence in COVID-19 drug repurposing. Lancet Digit Health. 2020;2(12):e667-e676.</unstructured_citation>
						 <doi>10.1016/S2589-7500(20)30192-8</doi> 					</citation>
          					<citation key="rk-10.68159/d049294389-3a68bd51-417c-4ee8-a3e5-1c9146675656">
					  <unstructured_citation>Wiens J, Price WN, Sjoding MW. Diagnosing bias in data-driven algorithms for healthcare. Nat Med. 2020;26(1):25-6.</unstructured_citation>
						 <doi>10.1038/s41591-019-0722-x</doi> 					</citation>
          					<citation key="rk-10.68159/d049294389-bfdc64e3-570a-4c99-b40c-253ae9db25b8">
					  <unstructured_citation>Collins GS, Moons KGM. Reporting of artificial intelligence prediction models. Lancet. 2019;393(10181):1577-9.</unstructured_citation>
						 <doi>10.1016/S0140-6736(19)30037-6</doi> 					</citation>
          					<citation key="rk-10.68159/d049294389-fdfc7c8f-8230-4c20-8f05-b5f86ef6fb41">
					  <unstructured_citation>Vasey B, Ursprung S, Stockl E, von Ende T, Vogt JE, McCulloch P, et al. Association of clinician diagnostic performance with machine learning-based decision support systems: a systematic review. JAMA Netw Open. 2021;4(3):e211276.</unstructured_citation>
						 <doi>10.1001/jamanetworkopen.2021.1276</doi> 					</citation>
          					<citation key="rk-10.68159/d049294389-0388ccce-66cb-4ab5-876a-908f25d228c1">
					  <unstructured_citation>Nagendran M, Chen Y, Lovejoy CA, Gordon AC, Komorowski M, Harvey H, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ. 2020;369:m689.</unstructured_citation>
						 <doi>10.1136/bmj.m689</doi> 					</citation>
          					<citation key="rk-10.68159/d049294389-3b9a2519-35e8-434a-8787-11734413313f">
					  <unstructured_citation>Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health. 2019;1(6):e271-e297.</unstructured_citation>
						 <doi>10.1016/S2589-7500(19)30123-2</doi> 					</citation>
          					<citation key="rk-10.68159/d049294389-b27073d8-65ab-4d24-958e-39eef77aef74">
					  <unstructured_citation>Keane PA, Topol EJ. AI-facilitated health care: key ethical issues and policy considerations for society. Lancet Digit Health. 2021;3(2):e69-e70.</unstructured_citation>
						 <doi>10.1016/S2589-7500(20)30292-2</doi> 					</citation>
          					<citation key="rk-10.68159/d049294389-3b78f628-f070-434d-899d-46adbec8510b">
					  <unstructured_citation>Rodriguez-Ruiz A, Lång K, Gubern-Merida A, Broeders M, Gennaro G, Clauser P, et al. Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. J Natl Cancer Inst. 2019;111(9):916-22.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d049294389-b1e3295b-3939-4a7b-9f5d-abebb5203414">
					  <unstructured_citation>Challen R, Denny J, Pitt M, Gompels L, Edwards T, Tsaneva-Atanasova K. Artificial intelligence, bias and clinical safety. BMJ Qual Saf. 2019;28(3):231-7.</unstructured_citation>
						 <doi>10.1136/bmjqs-2018-008370</doi> 					</citation>
          					<citation key="rk-10.68159/d049294389-4134ea2d-59ba-4bb9-a0f5-6dd55e83b423">
					  <unstructured_citation>Vayena E, Blasimme A, Cohen IG. Machine learning in medicine: Addressing ethical challenges. PLoS Med. 2018;15(11):e1002689.</unstructured_citation>
						 <doi>10.1371/journal.pmed.1002689</doi> 					</citation>
          					<citation key="rk-10.68159/d049294389-5f17b8ad-3a3c-4715-8550-ff07385d8a0a">
					  <unstructured_citation>Sendak M, Elish MC, Gao M, Futoma J, LeBlanc M, Bedoya A, et al. “The human body is a black box”: supporting clinical decision-making with deep learning. Proc ACM Conf Fairness Account Transpar. 2020:99-109.</unstructured_citation>
						 <doi>10.1145/3351095.3372827</doi> 					</citation>
          					<citation key="rk-10.68159/d049294389-4fafad77-d163-44ce-9f5b-2b7e35d34f2a">
					  <unstructured_citation>Obermeyer Z, Nissan R, Stern M, Eaneff S, Bembeneck EJ, Mullainathan S, et al. Algorithmic fairness in health care: A path forward. Lancet Digit Health. 2021;3(7):e412-e413.</unstructured_citation>
						 <doi>10.1016/S2589-7500(21)00116-9</doi> 					</citation>
          					<citation key="rk-10.68159/d049294389-f23efb40-0a9a-4be4-9e5a-d9501c628146">
					  <unstructured_citation>Li RC, Asch SM, Shah NH. Developing a delivery science for artificial intelligence in healthcare. npj Digit Med. 2020;3:107.</unstructured_citation>
						 <doi>10.1038/s41746-020-00318-y</doi> 					</citation>
          					<citation key="rk-10.68159/d049294389-03b20173-7765-494a-9ad0-9b4a4df9096c">
					  <unstructured_citation>Li Y, Rao S, Solares JRA, Hassaine A, Ramakrishnan R, Canoy D, et al. BEHRT: Transformer for electronic health records. Sci Rep. 2020;10(1):6905.</unstructured_citation>
						 <doi>10.1038/s41598-020-62922-y</doi> 					</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
