<?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-lKKG-1790904314-q291609361</doi_batch_id>
		<timestamp>1790904314</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>Machine Learning for Healthcare Revenue Cycle Analytics: A Systematic Review of Claim Denial Prediction, Coding Automation, Payment Delay Forecasting, and Prior Authorization Support</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Paolo</given_name>
            <surname>Ricci</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Marco</given_name>
            <surname>De Luca</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Giulia</given_name>
            <surname>Ferraro</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Antonio</given_name>
            <surname>Russo</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2024</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/q291609361</doi>
					<resource>https://cirpublications.com/pub/journal/2/article/q291609361</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/q291609361-2297e240-d80d-4a6c-aee1-c769d379b916">
					  <unstructured_citation>Johnson M, Albizri A, Harfouche A. Responsible artificial intelligence in healthcare: predicting and preventing insurance claim denials for economic and social wellbeing. Inf Syst Front. 2023;25(6):2179-95.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-7e85fbdd-5b8c-4efe-9192-d39948392612">
					  <unstructured_citation>Panigrahi S, Palkar B. Comparative analysis on classification algorithms of auto-insurance fraud detection based on feature selection algorithms. Int J Comput Sci Eng. 2018;6(9):72-7.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-38e3f079-79cb-4a2f-ab4f-b90cf72881c6">
					  <unstructured_citation>Chou SC, Gondi S, Baker O, Venkatesh AK, Schuur JD. Analysis of a commercial insurance policy to deny coverage for emergency department visits with nonemergent diagnoses. JAMA Netw Open. 2018;1(6):e183731.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-c35d356e-1b28-4389-9e3f-10ebc53379ad">
					  <unstructured_citation>Kim BH, Sridharan S, Atwal A, Ganapathi V. Deep claim: payer response prediction from claims data with deep learning. arXiv preprint arXiv:2007.06229. 2020.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-41772af9-944e-4c4b-b5cb-336e0c792edf">
					  <unstructured_citation>Mullenbach J, Wiegreffe S, Duke J, Sun J, Eisenstein J. Explainable prediction of medical codes from clinical text. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies; 2018. p. 1101-11.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-244cc5e6-e2e1-4b9a-b1f1-64e82866c1ab">
					  <unstructured_citation>Dong H, Suárez-Paniagua V, Whiteley W, Wu H. Explainable automated coding of clinical notes using hierarchical label-wise attention networks and label embedding initialisation. J Biomed Inform. 2021;116:103728.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-cf3786db-841e-4d57-8b15-a8cb5566c325">
					  <unstructured_citation>Dong H, Falis M, Whiteley W, Alex B, Matterson J, Ji S, et al. Automated clinical coding: what, why, and where we are? NPJ Digit Med. 2022;5(1):159.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-710c7446-07b8-4b5f-b2bd-62ac083f1c5f">
					  <unstructured_citation>Chen PF, Wang SM, Liao WC, Kuo LC, Chen KC, Lin YC, et al. Automatic ICD-10 coding and training system: deep neural network based on supervised learning. JMIR Med Inform. 2021;9(8):e23230.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-99a7c542-368c-4328-b382-35e4a1d9cd59">
					  <unstructured_citation>Junior GV, Vieira JP, de Sales Santos RL, Barbosa JL, dos Santos Neto PD, Moura RS. A study of the influence of textual features in learning medical prior authorization. In: Proc IEEE Int Symp Comput Based Med Syst; 2019. p. 56-61.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-17f03db0-123e-4aba-8160-c6e295dd95a8">
					  <unstructured_citation>Salau A, Nwojo NA, Boukar MM, Usen O. Advancing preauthorization task in healthcare: an application of deep active incremental learning for medical text classification. Eng Technol Appl Sci Res. 2023;13(6):12205-10.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-cbf260a3-274f-47d7-b680-a32df48ebbc7">
					  <unstructured_citation>Lenert LA, Lane S, Wehbe R. Could an artificial intelligence approach to prior authorization be more human? J Am Med Inform Assoc. 2023;30(5):989-94.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-2d7f62c7-3b79-458e-9e3b-50a54ce08662">
					  <unstructured_citation>Young DL, Engels R, Colantuoni E, Friedman LA, Hoyer EH. Machine learning prediction of hospital patient need for post-acute care using an admission mobility measure is robust across patient diagnoses. Health Policy Technol. 2023;12(2):100754.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-f35f49ae-0d60-4e1b-89b7-a869173e5acd">
					  <unstructured_citation>Teng F, Ma Z, Chen J, Xiao M, Huang L. Automatic medical code assignment via deep learning approach for intelligent healthcare. IEEE J Biomed Health Inform. 2020;24(9):2506-15.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-996d2222-6658-4483-aeb9-761462966151">
					  <unstructured_citation>Huang J, Osorio C, Sy LW. An empirical evaluation of deep learning for ICD-9 code assignment using MIMIC-III clinical notes. Comput Methods Programs Biomed. 2019;177:141-53.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-5656ef5e-c488-4b7d-b86d-b1067abe598c">
					  <unstructured_citation>Kaur R, Ginige JA, Obst O. A systematic literature review of automated ICD coding and classification systems using discharge summaries. arXiv preprint arXiv:2107.10652. 2021.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-2d60bd73-4126-4417-8728-cfc4f3421a9d">
					  <unstructured_citation>Catling F, Spithourakis GP, Riedel S. Towards automated clinical coding. Int J Med Inform. 2018;120:50-61.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-8b827f66-ace9-4f38-93d8-eb08d5e1fc72">
					  <unstructured_citation>Atutxa A, de Ilarraza AD, Gojenola K, Oronoz M, Perez-de-Viñaspre O. Interpretable deep learning to map diagnostic texts to ICD-10 codes. Int J Med Inform. 2019;129:49-59.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-eb294878-42dc-4f53-9bdb-be1ee1e83cb7">
					  <unstructured_citation>Miftahutdinov Z, Tutubalina E. Deep learning for ICD coding: looking for medical concepts in clinical documents in English and in French. In: Proc CLEF Conf; 2018. p. 203-15.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-d68c8bd7-0585-448d-bb7c-277b6f135001">
					  <unstructured_citation>Li F, Yu H. ICD coding from clinical text using multi-filter residual convolutional neural network. In: Proc AAAI Conf Artif Intell. 2020;34(5):8180-7.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-14c41d57-f496-4917-afc7-b13718e28ee5">
					  <unstructured_citation>Vu T, Nguyen DQ, Nguyen A. A label attention model for ICD coding from clinical text. arXiv preprint arXiv:2007.06351. 2020.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-b29730fa-ef4b-45c1-823d-3418a805785c">
					  <unstructured_citation>Cao P, Chen Y, Liu K, Zhao J, Liu S, Chong W. HyperCore: hyperbolic and co-graph representation for automatic ICD coding. In: Proc Assoc Comput Linguist; 2020. p. 3105-14.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-94db7239-3099-4cfd-a28e-84a3f3b6e4a3">
					  <unstructured_citation>Feucht M, Wu Z, Althammer S, Tresp V. Description-based label attention classifier for explainable ICD-9 classification. In: Proc W-NUT; 2021. p. 62-6.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-6388693c-c00b-48ee-bb31-0b3b63390d3d">
					  <unstructured_citation>Mahbubani K, Georgiades F, Goh EL, Chidambaram S, Sivakumaran P, Rawson T, et al. Clinician-directed improvement in the accuracy of hospital clinical coding. Future Healthc J. 2018;5(1):47-51.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-bf9f863f-2550-4275-810e-9326366e7b7e">
					  <unstructured_citation>Sun W, Ji S, Cambria E, Marttinen P. Multitask balanced and recalibrated network for medical code prediction. ACM Trans Intell Syst Technol. 2022;14(1):1-20.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-40185794-036c-4944-b02a-a61a8b9f4ecc">
					  <unstructured_citation>Baumel T, Nassour-Kassis J, Cohen R, Elhadad M, Elhadad N. Multi-label classification of patient notes: case study on ICD code assignment. In: AAAI Workshops; 2018. p. 409-16.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-0e09aff9-13c7-4e4e-82d6-af898a9619bc">
					  <unstructured_citation>Shi H, Xie P, Hu Z, Zhang M, Xing EP. Towards automated ICD coding using deep learning. arXiv preprint arXiv:1711.04075. 2017.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-09a4ba14-042c-402f-8418-f22c53d1510d">
					  <unstructured_citation>Rios A, Kavuluru R. Few-shot and zero-shot multi-label learning for structured label spaces. In: Proc Empirical Methods Nat Lang Process; 2018. p. 3132-42.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-17dbdf9c-46b2-49b6-b239-ddc93afdce0a">
					  <unstructured_citation>Afkanpour A, Adeel S, Bassani H, Epshteyn A, Fan H, Jones I, et al. BERT for long documents: a case study of automated ICD coding. In: Proc Health Text Mining Inf Anal; 2022. p. 100-7.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-dcc80126-0a92-496e-9285-90f24e750d29">
					  <unstructured_citation>Searle T, Ibrahim Z, Dobson R. Experimental evaluation and development of a silver-standard for the MIMIC-III clinical coding dataset. In: Proc SIGBioMed Workshop Biomed Lang Process; 2020. p. 76-85.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-60c33317-551d-4def-b051-544ae9d9e19b">
					  <unstructured_citation>Tsai SC, Chang TY, Chen YN. Leveraging hierarchical category knowledge for data-imbalanced multi-label diagnostic text understanding. In: Proc Health Text Mining Inf Anal; 2019. p. 39-43.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/q291609361-df53c3fc-4ca1-4c6b-9200-8f91c057cded">
					  <unstructured_citation>Dreyer KJ, Geis JR. When machines think: radiology’s next frontier. Radiology. 2017;285(3):713-8.</unstructured_citation>
											</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
