<?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-4Eck-1790899041-f400994390</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>2024</year>
				</publication_date>
				<journal_volume>
					<volume>4</volume>
				</journal_volume>
				<issue>1</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Sequence Learning Model for Predicting Specialist Consultation Completion Delays Using Consultation Type, Patient Location, Specialty Workload, Ordering Service, Communication Logs, and Escalation History</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Maria</given_name>
            <surname>Silva</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Joao</given_name>
            <surname>Pereira</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2024</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/f400994390</doi>
					<resource>https://cirpublications.com/pub/journal/2/article/f400994390</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/f400994390-a1048f24-678d-4f16-910e-5b9e20acd3a2">
					  <unstructured_citation>Kern-Goldberger AS, Dalton EM, Rasooly IR, Congdon M, Gunturi D, Wu L, et al. Factors associated with inpatient subspecialty consultation patterns among pediatric hospitalists. JAMA Netw Open. 2023;6(3):e232648.</unstructured_citation>
						 <doi>10.1001/jamanetworkopen.2023.2648</doi> 					</citation>
          					<citation key="rk-10.68159/f400994390-9c1731ed-307e-4cd8-ac4d-c96f3146a3b0">
					  <unstructured_citation>Kachman M, Carter K, Arora VM, Flores A, Meltzer DO, Martin SK. Describing variability of inpatient consultation practices: physician, patient, and admission factors. J Hosp Med. 2020;15(3):164-8.</unstructured_citation>
						 <doi>10.12788/jhm.3344</doi> 					</citation>
          					<citation key="rk-10.68159/f400994390-294f7263-8a80-4633-b472-ff8f8b55025c">
					  <unstructured_citation>Ataman MG, Sariyer G, Saglam C, Karagoz A, Unluer EE. Factors relating to decision delay in the emergency department: effects of diagnostic tests and consultations. Open Access Emerg Med. 2023;15:119-31.</unstructured_citation>
						 <doi>10.2147/OAEM.S398225</doi> 					</citation>
          					<citation key="rk-10.68159/f400994390-7ee9cead-805b-487e-aa0a-10aaf5da5d81">
					  <unstructured_citation>Pearlmutter MD, Dwyer KH, Burke LG, Rathlev N, Maranda L, Volturo G. Analysis of emergency department length of stay for mental health patients at ten Massachusetts emergency departments. Ann Emerg Med. 2017;70(2):193-202.e16.</unstructured_citation>
						 <doi>10.1016/j.annemergmed.2016.10.002</doi> 					</citation>
          					<citation key="rk-10.68159/f400994390-57341088-fa63-4c54-9bc4-1f463fbba6bb">
					  <unstructured_citation>Pavitt S, Bogetz A, Blankenburg R. What makes the “perfect” inpatient consultation? A qualitative analysis of resident and fellow perspectives. Acad Med. 2020;95(1):104-10.</unstructured_citation>
						 <doi>10.1097/ACM.0000000000002944</doi> 					</citation>
          					<citation key="rk-10.68159/f400994390-0687a091-a4a2-41c9-a5b8-01129b941488">
					  <unstructured_citation>Stevens JP, Hatfield LA, Nyweide DJ, Landon B. Comparison of health outcomes among patients admitted on busy vs less busy days for hospitalists. JAMA Netw Open. 2022;5(1):e2144261.</unstructured_citation>
						 <doi>10.1001/jamanetworkopen.2021.44261</doi> 					</citation>
          					<citation key="rk-10.68159/f400994390-8707ae1d-2bcd-43ca-9898-09ab9f85a7ca">
					  <unstructured_citation>Stevens JP, Nyweide DJ, Maresh S, Hatfield LA, Howell MD, Landon BE. Comparison of hospital resource use and outcomes among hospitalists, primary care physicians, and other generalists. JAMA Intern Med. 2017;177(12):1781-7.</unstructured_citation>
						 <doi>10.1001/jamainternmed.2017.5281</doi> 					</citation>
          					<citation key="rk-10.68159/f400994390-387ec67a-a6ef-4eb2-b6ca-6c7ca4453fcd">
					  <unstructured_citation>Choi E, Schuetz A, Stewart WF, Sun J. Using recurrent neural network models for early detection of heart failure onset. J Am Med Inform Assoc. 2017;24(2):361-70.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f400994390-6d950285-ba2c-454d-84b9-a037f252527a">
					  <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(1):18.</unstructured_citation>
						 <doi>10.1038/s41746-018-0029-1</doi> 					</citation>
          					<citation key="rk-10.68159/f400994390-f19abba7-17c4-49a2-8537-0f7cf9adcb61">
					  <unstructured_citation>Shickel B, Tighe PJ, Bihorac A, Rashidi P. Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE J Biomed Health Inform. 2018;22(5):1589-604.</unstructured_citation>
						 <doi>10.1109/JBHI.2017.2767063</doi> 					</citation>
          					<citation key="rk-10.68159/f400994390-88b78dab-1f33-4a7b-b62a-c535f1df25b0">
					  <unstructured_citation>Xiao C, Choi E, Sun J. Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review. J Am Med Inform Assoc. 2018;25(10):1419-28.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f400994390-8dddc074-fcbb-402f-bcb8-f1749ba863ef">
					  <unstructured_citation>Harutyunyan H, Khachatrian H, Kale DC, Ver Steeg G, Galstyan A. Multitask learning and benchmarking with clinical time series data. Sci Data. 2019;6(1):96.</unstructured_citation>
						 <doi>10.1038/s41597-019-0103-9</doi> 					</citation>
          					<citation key="rk-10.68159/f400994390-680454bd-7338-46d2-8274-77fb3e5a2cf5">
					  <unstructured_citation>Li Y, Rao S, Solares JR, Hassaine A, Ramakrishnan R, Canoy D, et al. BEHRT: transformer for electronic health records. Sci Rep. 2020;10(1):7155.</unstructured_citation>
						 <doi>10.1038/s41598-020-62922-y</doi> 					</citation>
          					<citation key="rk-10.68159/f400994390-8a7402cc-491e-46c0-b385-9d3751eb90f1">
					  <unstructured_citation>Si Y, Du J, Li Z, Jiang X, Miller T, Wang F, et al. Deep representation learning of patient data from electronic health records (EHR): a systematic review. J Biomed Inform. 2021;115:103671.</unstructured_citation>
						 <doi>10.1016/j.jbi.2021.103671</doi> 					</citation>
          					<citation key="rk-10.68159/f400994390-659a3aed-6fc9-4569-a297-1f1f21d239bf">
					  <unstructured_citation>Liu X, Sutton PR, McKenna R, Sinanan MN, Fellner BJ, Leu MG, et al. Evaluation of secure messaging applications for a health care system: a case study. Appl Clin Inform. 2019;10(1):140-50.</unstructured_citation>
						 <doi>10.1055/s-0038-1677016</doi> 					</citation>
          					<citation key="rk-10.68159/f400994390-0388b8b1-69aa-432e-8f86-e87d17606db5">
					  <unstructured_citation>Huang M, Fan J, Prigge J, Shah ND, Costello BA, Yao L. Characterizing patient-clinician communication in secure medical messages: retrospective study. J Med Internet Res. 2022;24(1):e17273.</unstructured_citation>
						 <doi>10.2196/17273</doi> 					</citation>
          					<citation key="rk-10.68159/f400994390-8e61a1ae-4044-4ea7-8122-73ae2b4520b8">
					  <unstructured_citation>Katzman JL, Shaham U, Cloninger A, Bates J, Jiang T, Kluger Y. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med Res Methodol. 2018;18(1):24.</unstructured_citation>
						 <doi>10.1186/s12874-018-0482-1</doi> 					</citation>
          					<citation key="rk-10.68159/f400994390-0597b504-d23a-4123-938a-7b85ec80977f">
					  <unstructured_citation>Lee C, Zame W, Yoon J, Van Der Schaar M. Deephit: a deep learning approach to survival analysis with competing risks. Proc AAAI Conf Artif Intell. 2018;32(1):2314-21.</unstructured_citation>
						 <doi>10.1609/aaai.v32i1.11842</doi> 					</citation>
          					<citation key="rk-10.68159/f400994390-50427bdb-1720-4b2c-a89d-03f0bd9ed5c2">
					  <unstructured_citation>Kvamme H, Borgan Ø, Scheel I. Time-to-event prediction with neural networks and Cox regression. J Mach Learn Res. 2019;20(129):1-30.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/f400994390-42510dc9-34f8-4f20-8a39-dac2dce4a71a">
					  <unstructured_citation>Nagpal C, Li X, Dubrawski A. Deep survival machines: fully parametric survival regression and representation learning for censored data with competing risks. IEEE J Biomed Health Inform. 2021;25(8):3163-75.</unstructured_citation>
						 <doi>10.1109/JBHI.2021.3052441</doi> 					</citation>
          					<citation key="rk-10.68159/f400994390-447869fd-b56b-4ffc-ad1f-ad5f69259483">
					  <unstructured_citation>Ren K, Qin J, Zheng L, Yang Z, Zhang W, Qiu L, et al. Deep recurrent survival analysis. Proc AAAI Conf Artif Intell. 2019;33(1):4798-805.</unstructured_citation>
						 <doi>10.1609/aaai.v33i01.33014798</doi> 					</citation>
          					<citation key="rk-10.68159/f400994390-3c2225c7-ed74-48a0-80ed-49ac10b5b799">
					  <unstructured_citation>Wang P, Li Y, Reddy CK. Machine learning for survival analysis: a survey. ACM Comput Surv. 2019;51(6):1-36.</unstructured_citation>
						 <doi>10.1145/3214306</doi> 					</citation>
          					<citation key="rk-10.68159/f400994390-3664d7c4-b55b-4968-9652-deb48136e745">
					  <unstructured_citation>Giannini HM, Ginestra JC, Chivers C, Draugelis M, Hanish A, Schweickert WD, et al. A machine learning algorithm to predict severe sepsis and septic shock: development, implementation, and impact on clinical practice. Crit Care Med. 2019;47(11):1485-92.</unstructured_citation>
						 <doi>10.1097/CCM.0000000000003891</doi> 					</citation>
          					<citation key="rk-10.68159/f400994390-ac08f46b-f87e-45de-932e-2366ada44e24">
					  <unstructured_citation>Tomašev N, Glorot X, Rae JW, Zielinski M, Askham H, Saraiva A, et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature. 2019;572(7767):116-9.</unstructured_citation>
						 <doi>10.1038/s41586-019-1390-1</doi> 					</citation>
          					<citation key="rk-10.68159/f400994390-78e1e6b5-c51b-426f-bc37-579b9c469e7e">
					  <unstructured_citation>Shamout FE, Zhu T, Sharma P, Watkinson PJ, Clifton DA. Deep interpretable early warning system for the detection of clinical deterioration. IEEE J Biomed Health Inform. 2020;24(2):437-46.</unstructured_citation>
						 <doi>10.1109/JBHI.2019.2938387</doi> 					</citation>
          					<citation key="rk-10.68159/f400994390-ecb49706-a909-46f3-8bfa-27aceebe1e8e">
					  <unstructured_citation>Pungitore S, Subbian V. Assessment of prediction tasks and time window selection in temporal modeling of electronic health record data: a systematic review. J Healthc Inform Res. 2023;7(3):313-31.</unstructured_citation>
						 <doi>10.1007/s41666-023-00139-3</doi> 					</citation>
          					<citation key="rk-10.68159/f400994390-40565544-2fcb-4807-b16a-daf9d5b68bb5">
					  <unstructured_citation>Liu S, Wang X, Xiang Y, Xu H, Wang H, Tang B. Multi-channel fusion LSTM for medical event prediction using EHRs. J Biomed Inform. 2022;127:104011.</unstructured_citation>
						 <doi>10.1016/j.jbi.2022.104011</doi> 					</citation>
          					<citation key="rk-10.68159/f400994390-534250ba-899c-4773-bea5-e2957e88e902">
					  <unstructured_citation>Zhang Z, Yan C, Zhang X, Nyemba SL, Malin BA. Forecasting the future clinical events of a patient through contrastive learning. J Am Med Inform Assoc. 2022;29(9):1584-92.</unstructured_citation>
											</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
