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			<depositor_name>Clinical Intelligence Research Press</depositor_name>
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				<full_title>Journal of Health Informatics and Digital Systems</full_title>
				<abbrev_title>J. Health Inform. Digit. Syst.</abbrev_title>
				<issn>3149-8973</issn>
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				<publication_date>
					<year>2021</year>
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					<volume>1</volume>
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				<issue>1</issue>
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					<title>Rule-Augmented Artificial Intelligence Framework for Detecting Clinically Significant Abnormal Laboratory Result Patterns in Hospitalized Adults Using Sequential Blood Chemistry Panels, Vital Sign Trends, and Physician Response Times</title>
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          					<person_name sequence="first" contributor_role="author">
            <given_name>Wei</given_name>
            <surname>Chen</surname>
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          					<person_name sequence="additional" contributor_role="author">
            <given_name>Li</given_name>
            <surname>Zhang</surname>
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								<publication_date>
					<year>2021</year>
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					  <unstructured_citation>Costa MB, Wernsdorfer M, Kehrer A, Voigt M, Cundius C, Federbusch M, et al. The clinical decision support system AMPEL for laboratory diagnostics: implementation and technical evaluation. JMIR Med Inform. 2021;9(6):e20407.</unstructured_citation>
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          					<citation key="rk-10.68159/q702272227-c7822497-ab90-42ab-bd41-31b039dc211e">
					  <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>
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          					<citation key="rk-10.68159/q702272227-b2d981e7-e43f-4e0b-94ba-e3dedf88f5d3">
					  <unstructured_citation>Li R, Wang T, Gong L, Dong J, Xiao N, Guo M, et al. Enhance the effectiveness of clinical laboratory critical values initiative notification by implementing a closed-loop system: a five-year retrospective observational study. J Clin Lab Anal. 2020;34(2):e23038.</unstructured_citation>
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					  <unstructured_citation>Baron JM, Huang R, McEvoy D, Dighe AS. Use of machine learning to predict clinical decision support compliance, reduce alert burden, and evaluate duplicate laboratory test ordering alerts. JAMIA Open. 2021;4(1):ooab006.</unstructured_citation>
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          					<citation key="rk-10.68159/q702272227-b2865207-8510-4cb3-9ddb-3080de69b53d">
					  <unstructured_citation>Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI, et al. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med. 2020;3(1):17.</unstructured_citation>
						 <doi>10.1038/s41746-020-0221-y</doi> 					</citation>
          					<citation key="rk-10.68159/q702272227-534316d3-7bf2-45e2-9c60-dd70045641be">
					  <unstructured_citation>Jackson CR, Cervinski MA. Development and characterization of neural network-based multianalyte delta checks. J Lab Precis Med. 2020;5:12.</unstructured_citation>
						 <doi>10.21037/jlpm.2020.03.03</doi> 					</citation>
          					<citation key="rk-10.68159/q702272227-891e3df9-a9e0-47d9-bf46-6eeb682770e2">
					  <unstructured_citation>Escobar GJ, Liu VX, Schuler A, Lawson B, Greene JD, Kipnis P, et al. Automated identification of adults at risk for in-hospital clinical deterioration. N Engl J Med. 2020;383(20):1951-60.</unstructured_citation>
						 <doi>10.1056/NEJMsa2001090</doi> 					</citation>
          					<citation key="rk-10.68159/q702272227-ffe5a6f1-a764-49f0-b917-082c4f9ae055">
					  <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/q702272227-8af10f29-9adf-4103-938b-9b85efcdc2cc">
					  <unstructured_citation>Ueno R, Xu L, Uegami W, Matsui H, Okui J, Kusunoki Y, et al. Value of laboratory results in addition to vital signs in a machine learning algorithm to predict in-hospital cardiac arrest: a single-center retrospective cohort study. PLoS One. 2020;15(7):e0235835.</unstructured_citation>
						 <doi>10.1371/journal.pone.0235835</doi> 					</citation>
          					<citation key="rk-10.68159/q702272227-56b8bc7a-c977-4b4c-a3c2-f7845ca03f12">
					  <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/q702272227-c2f36859-28be-447d-bc81-bd7261008fb9">
					  <unstructured_citation>Hyland SL, Faltys M, Hüser M, Lyu X, Gumbsch T, Esteban C, et al. Early prediction of circulatory failure in the intensive care unit using machine learning. Nat Med. 2020;26(3):364-373.</unstructured_citation>
						 <doi>10.1038/s41591-020-0789-4</doi> 					</citation>
          					<citation key="rk-10.68159/q702272227-751b89d5-c7d5-4dcc-b0f3-31a53002b77b">
					  <unstructured_citation>Beeler PE, Bates DW, Hug BL. Clinical decision support systems. Swiss Med Wkly. 2014;144:w14073.</unstructured_citation>
						 <doi>10.4414/smw.2014.14073</doi> 					</citation>
          					<citation key="rk-10.68159/q702272227-5c4a50c4-a420-47bf-9649-164f0cfad957">
					  <unstructured_citation>Nemati S, Holder A, Razmi F, Stanley MD, Clifford GD, Buchman TG, et al. An interpretable machine learning model for accurate prediction of sepsis in the ICU. Crit Care Med. 2018;46(4):547-53.</unstructured_citation>
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          					<citation key="rk-10.68159/q702272227-2bca3009-dcbe-46fa-988f-ceb5f0cd11ba">
					  <unstructured_citation>Fu LH, Schwartz J, Moy A, Knaplund C, Kang MJ, Schnock KO, et al. Development and validation of early warning score system: a systematic literature review. J Biomed Inform. 2020;105:103410.</unstructured_citation>
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					  <unstructured_citation>Lauritsen SM, Kristensen M, Olsen MV, Larsen MS, Lauritsen KM, Jørgensen MJ, et al. Explainable artificial intelligence model to predict acute critical illness from electronic health records. Nat Commun. 2020;11(1):3852.</unstructured_citation>
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					  <unstructured_citation>Kawamoto K, Kukhareva PV, Weir C, Flynn MC, Nanjo CJ, Liu M, et al. Establishing a multidisciplinary initiative for interoperable electronic health record innovations at an academic medical center. JAMIA Open. 2021;4(3):ooab041.</unstructured_citation>
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          					<citation key="rk-10.68159/q702272227-f5d07c3f-fcd0-448a-b3b1-9ea94c9f5090">
					  <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/q702272227-b1656b99-d602-407b-8c17-5c1b53b21754">
					  <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>
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          					<citation key="rk-10.68159/q702272227-0bfc57b9-79bc-433c-a2cb-3f6c8e3730c7">
					  <unstructured_citation>Beaulieu-Jones BK, Lavage DR, Snyder JW, Moore JH, Pendergrass SA, Bauer CR, et al. Characterizing and managing missing structured data in electronic health records: data analysis. JMIR Med Inform. 2018;6(1):e11.</unstructured_citation>
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          					<citation key="rk-10.68159/q702272227-5fb6b0fe-2e71-4613-ae02-6a6ecbb3fad2">
					  <unstructured_citation>Lynam AL, Dennis JM, Owen KR, Oram RA, Jones AG, Shields BM, et al. Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: application to the discrimination between type 1 and type 2 diabetes in young adults. Diagn Progn Res. 2020;4(1):6.</unstructured_citation>
						 <doi>10.1186/s41512-020-00075-2</doi> 					</citation>
          					<citation key="rk-10.68159/q702272227-fd7841a1-9071-455c-842f-f573b3cd3c3e">
					  <unstructured_citation>Bennett CE, Wright RS, Jentzer J, Gajic O, Murphree DH, Murphy JG, et al. Severity of illness assessment with application of the APACHE IV predicted mortality and outcome trends analysis in an academic cardiac intensive care unit. J Crit Care. 2019;50:242-6.</unstructured_citation>
						 <doi>10.1016/j.jcrc.2018.12.019</doi> 					</citation>
          					<citation key="rk-10.68159/q702272227-e8e6fa42-a6d0-41a5-b110-6509d6797b74">
					  <unstructured_citation>de Munter L, Polinder S, Lansink KW, Cnossen MC, Steyerberg EW, de Jongh MA, et al. Mortality prediction models in the general trauma population: a systematic review. Injury. 2017;48(2):221-9.</unstructured_citation>
						 <doi>10.1016/j.injury.2016.11.009</doi> 					</citation>
          					<citation key="rk-10.68159/q702272227-5ee30982-a043-4f54-bea3-86cb64ba67ea">
					  <unstructured_citation>Kwon JM, Lee Y, Lee Y, Lee S, Park H, Park J, et al. Validation of deep-learning-based triage and acuity score using a large national dataset. PLoS One. 2018;13(10):e0205836.</unstructured_citation>
						 <doi>10.1371/journal.pone.0205836</doi> 					</citation>
          					<citation key="rk-10.68159/q702272227-4bed44f3-733f-4aff-a27b-5d1a66df2fe8">
					  <unstructured_citation>Bedoya AD, Clement ME, Phelan M, Steorts RC, O’Brien C, Goldstein BA, et al. Minimal impact of implemented early warning score and best practice alert for patient deterioration. Crit Care Med. 2019;47(1):49-55.</unstructured_citation>
						 <doi>10.1097/CCM.0000000000003499</doi> 					</citation>
          					<citation key="rk-10.68159/q702272227-c1855696-c683-4ca3-86ec-0684bd0a5140">
					  <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/q702272227-e1a39335-0c53-45c4-ab49-b813f2d327c9">
					  <unstructured_citation>Islam MM, Nasrin T, Walther BA, Wu CC, Yang HC, Li YC, et al. Prediction of sepsis patients using machine learning approach: a meta-analysis. Comput Methods Programs Biomed. 2019;170:1-9.</unstructured_citation>
						 <doi>10.1016/j.cmpb.2018.12.027</doi> 					</citation>
          					<citation key="rk-10.68159/q702272227-6c9b3f74-9a4b-4efa-910b-c7f6c628ab09">
					  <unstructured_citation>Fleuren LM, Klausch TL, Zwager CL, Schoonmade LJ, Guo T, Roggeveen LF, et al. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Intensive Care Med. 2020;46(3):383-400.</unstructured_citation>
						 <doi>10.1007/s00134-019-05872-y</doi> 					</citation>
          					<citation key="rk-10.68159/q702272227-d63c9958-c279-4b3a-84c2-74f904afd880">
					  <unstructured_citation>Komorowski M, Celi LA, Badawi O, Gordon AC, Faisal AA. The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med. 2018;24(11):1716-20.</unstructured_citation>
						 <doi>10.1038/s41591-018-0213-5</doi> 					</citation>
          					<citation key="rk-10.68159/q702272227-fbd2bf79-d56c-4d7d-a13f-163f7e1bced7">
					  <unstructured_citation>Moor M, Rieck B, Horn M, Jutzeler CR, Borgwardt K. Early prediction of sepsis in the ICU using machine learning: a systematic review. Front Med (Lausanne). 2021;8:607952.</unstructured_citation>
						 <doi>10.3389/fmed.2021.607952</doi> 					</citation>
          					<citation key="rk-10.68159/q702272227-9c417531-da54-40ab-b12f-8af6bfca9d0b">
					  <unstructured_citation>Masino AJ, Harris MC, Forsyth D, Ostapenko S, Srinivasan L, Bonafide CP, et al. Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data. PLoS One. 2019;14(2):e0212665.</unstructured_citation>
						 <doi>10.1371/journal.pone.0212665</doi> 					</citation>
          					<citation key="rk-10.68159/q702272227-deaeaaf6-6edb-4165-aafa-0d7d3d818a7a">
					  <unstructured_citation>Futoma J, Hariharan S, Heller K. Learning to detect sepsis with a multitask Gaussian process RNN classifier. In: Proceedings of the 34th International Conference on Machine Learning. PMLR; 2017. p.1174-82.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q702272227-d993fe4c-1140-4ed3-9a0d-611d9d39cf8c">
					  <unstructured_citation>Kam HJ, Kim HY. Learning representations for the early detection of sepsis with deep neural networks. Comput Biol Med. 2017;89:248-55.</unstructured_citation>
						 <doi>10.1016/j.compbiomed.2017.08.015</doi> 					</citation>
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					  <unstructured_citation>Huff K, Rose RS, Engle WA. Late preterm infants: morbidities, mortality, and management recommendations. Pediatr Clin North Am. 2019;66(2):387-402.</unstructured_citation>
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						 <doi>10.1007/978-3-319-99713-1_10</doi> 					</citation>
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