<?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-vMM6-1790880680-z613962324</doi_batch_id>
		<timestamp>1790880680</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>2022</year>
				</publication_date>
				<journal_volume>
					<volume>1</volume>
				</journal_volume>
				<issue>1</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Multimodal Transformer Architecture for ARDS Detection: A Framework Integrating Chest X-Ray, Clinical Notes, and Laboratory Values</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Ravi</given_name>
            <surname>Kumar</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Neha</given_name>
            <surname>Sharma</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Aniket</given_name>
            <surname>Deshmukh</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Arjun</given_name>
            <surname>Nair</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Meera</given_name>
            <surname>Pillai</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2022</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/z613962324</doi>
					<resource>https://cirpublications.com/pub/journal/1/article/z613962324</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/z613962324-99de5294-d331-4b77-92ba-4e07582701a4">
					  <unstructured_citation>Sjoding MW, Hofer TP, Co I, Courey A, Cooke CR, Iwashyna TJ. Interobserver reliability of the Berlin ARDS definition and strategies to improve the reliability of ARDS diagnosis. Chest. 2018;153(2):361-7.</unstructured_citation>
						 <doi>10.1016/j.chest.2017.11.037</doi> 					</citation>
          					<citation key="rk-10.68159/z613962324-07a183cd-ad06-42cd-9a3c-a3da20e8d083">
					  <unstructured_citation>Sayed M, Riaño D, Villar J. Novel criteria to classify ARDS severity using a machine learning approach. Crit Care. 2021;25(1):150.</unstructured_citation>
						 <doi>10.1186/s13054-021-03563-w</doi> 					</citation>
          					<citation key="rk-10.68159/z613962324-ab7ef2bf-1bda-41c4-b0a4-0d18c5d116ad">
					  <unstructured_citation>Li H, Odeyemi YE, Weister TJ, Liu C, Chalmers SJ, Lal A, et al. Rule-based cohort definitions for acute respiratory distress syndrome: a computable phenotyping strategy based on the Berlin definition. Crit Care Explor. 2021;3(6):e0451.</unstructured_citation>
						 <doi>10.1097/CCE.0000000000000451</doi> 					</citation>
          					<citation key="rk-10.68159/z613962324-1a271dfa-65c7-4bcf-898d-28f5c588bcbb">
					  <unstructured_citation>Baltruschat IM, Nickisch H, Grass M, Knopp T, Saalbach A. Comparison of deep learning approaches for multi-label chest X-ray classification. Sci Rep. 2019;9(1):6381.</unstructured_citation>
						 <doi>10.1038/s41598-019-42294-8</doi> 					</citation>
          					<citation key="rk-10.68159/z613962324-da53f88a-dd4b-40d2-b2e3-1719311a3016">
					  <unstructured_citation>Alsentzer E, Murphy J, Boag W, Weng WH, Jindi D, Naumann T, et al. Publicly available clinical BERT embeddings. In: Proc 2nd Clin Nat Lang Process Workshop. Stroudsburg (PA): Association for Computational Linguistics; 2019. p. 72-78.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/z613962324-e9ff6fde-9190-4aca-946d-35a74dfeaea0">
					  <unstructured_citation>Hung CY, Lin CH, Chang CS, Li JL, Lee CC. Predicting gastrointestinal bleeding events from multimodal in-hospital electronic health records using deep fusion networks. In: 2019 41st Annu Int Conf IEEE Eng Med Biol Soc (EMBC). Piscataway (NJ): IEEE; 2019. p. 2447-50.</unstructured_citation>
						 <doi>10.1109/EMBC.2019.8857402</doi> 					</citation>
          					<citation key="rk-10.68159/z613962324-3b122684-9922-49f3-8c12-36683b2cb9b2">
					  <unstructured_citation>Mohsen F, Ali H, El Hajj N, Shah Z. Artificial intelligence-based methods for fusion of electronic health records and imaging data. Sci Rep. 2022;12(1):17981.</unstructured_citation>
						 <doi>10.1038/s41598-022-22514-4</doi> 					</citation>
          					<citation key="rk-10.68159/z613962324-de0b991e-5fca-42bd-8b85-c171aad26728">
					  <unstructured_citation>Tang W, He F, Liu Y, Duan Y. MATR: multimodal medical image fusion via multiscale adaptive transformer. IEEE Trans Image Process. 2022;31:5134-49.</unstructured_citation>
						 <doi>10.1109/TIP.2022.3195278</doi> 					</citation>
          					<citation key="rk-10.68159/z613962324-255765da-8aad-4145-b156-bf4d61afff7e">
					  <unstructured_citation>Song X, Chao H, Xu X, Guo H, Xu S, Turkbey B, et al. Cross-modal attention for multi-modal image registration. Med Image Anal. 2022;82:102612.</unstructured_citation>
						 <doi>10.1016/j.media.2022.102612</doi> 					</citation>
          					<citation key="rk-10.68159/z613962324-5078c829-b34b-4588-a274-f03b41c9ba72">
					  <unstructured_citation>Jana S, Dasgupta T, Dey L. Predicting medical events and ICU requirements using a multimodal multiobjective transformer network. Exp Biol Med (Maywood). 2022;247(22):1988-2002.</unstructured_citation>
						 <doi>10.1177/15353702221121775</doi> 					</citation>
          					<citation key="rk-10.68159/z613962324-a9b240d5-6a39-48b0-9c03-627ccf79bbe5">
					  <unstructured_citation>Majdi MS, Salman KN, Morris MF, Merchant NC, Rodriguez JJ. Deep learning classification of chest X-ray images. In: 2020 IEEE Southwest Symp Image Anal Interpr (SSIAI). Piscataway (NJ): IEEE; 2020. p. 116-9.</unstructured_citation>
						 <doi>10.1109/SSIAI49293.2020.9094615</doi> 					</citation>
          					<citation key="rk-10.68159/z613962324-839dc311-b79f-481c-a081-0a044233fb7c">
					  <unstructured_citation>Khan E, Rehman MZ, Ahmed F, Alfouzan FA, Alzahrani NM, Ahmad J. Chest X-ray classification for the detection of COVID-19 using deep learning techniques. Sensors (Basel). 2022;22(3):1211.</unstructured_citation>
						 <doi>10.3390/s22031211</doi> 					</citation>
          					<citation key="rk-10.68159/z613962324-bc973e7f-bd65-41d7-bf17-c60e346be658">
					  <unstructured_citation>Shelke A, Inamdar M, Shah V, Tiwari A, Hussain A, Chafekar T, et al. Chest X-ray classification using deep learning for automated COVID-19 screening. SN Comput Sci. 2021;2(4):300.</unstructured_citation>
						 <doi>10.1007/s42979-021-00695-8</doi> 					</citation>
          					<citation key="rk-10.68159/z613962324-3b26f9a8-19cc-42ee-aed0-b08ed4d573b5">
					  <unstructured_citation>Hussain E, Hasan M, Rahman MA, Lee I, Tamanna T, Parvez MZ. CoroDet: a deep learning based classification for COVID-19 detection using chest X-ray images. Chaos Solitons Fractals. 2021;142:110495.</unstructured_citation>
						 <doi>10.1016/j.chaos.2020.110495</doi> 					</citation>
          					<citation key="rk-10.68159/z613962324-baf03f26-1a7d-4518-883a-1c1bdeb099f0">
					  <unstructured_citation>Pawar Y, Henriksson A, Hedberg P, Naucler P. Leveraging ClinicalBERT in multimodal mortality prediction models for COVID-19. In: 2022 IEEE 35th Int Symp Comput Based Med Syst (CBMS). Piscataway (NJ): IEEE; 2022. p. 199-204.</unstructured_citation>
						 <doi>10.1109/CBMS55023.2022.00045</doi> 					</citation>
          					<citation key="rk-10.68159/z613962324-462e20b3-a63c-4f83-ad70-1e6656bcd18c">
					  <unstructured_citation>Kalusivalingam AK, Sharma A, Patel N, Singh V. Leveraging BERT and LSTM for enhanced natural language processing in clinical data analysis. Int J AI ML. 2021;2(3):1-9.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/z613962324-fbd3f124-a859-446a-b2c1-e3194190ed7a">
					  <unstructured_citation>Roy A, Pan S. Incorporating medical knowledge in BERT for clinical relation extraction. In: Proc 2021 Conf Empir Methods Nat Lang Process. Stroudsburg (PA): Association for Computational Linguistics; 2021. p. 5357-66.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/z613962324-f537e58a-5c2e-45bc-853c-d1f6b92cd356">
					  <unstructured_citation>Lamproudis A, Henriksson A, Dalianis H. Developing a clinical language model for Swedish: continued pretraining of generic BERT with in-domain data. In: Proc Int Conf Recent Adv Nat Lang Process (RANLP 2021). Stroudsburg (PA): Association for Computational Linguistics; 2021. p. 790-7.</unstructured_citation>
						 <doi>10.26615/978-954-452-072-4_089</doi> 					</citation>
          					<citation key="rk-10.68159/z613962324-2f21a567-8a2a-4760-bb72-44f33df79f80">
					  <unstructured_citation>Zhang C, Chu X, Ma L, Zhu Y, Wang Y, Wang J, et al. M3Care: learning with missing modalities in multimodal healthcare data. In: Proc 28th ACM SIGKDD Conf Knowl Discov Data Min. New York (NY): ACM; 2022. p. 2418-28.</unstructured_citation>
						 <doi>10.1145/3534678.3539397</doi> 					</citation>
          					<citation key="rk-10.68159/z613962324-10e1d524-8fa5-4fd6-bed9-087b1b05e6b9">
					  <unstructured_citation>Lopez K, Fodeh SJ, Allam A, Brandt CA, Krauthammer M. Reducing annotation burden through multimodal learning. Front Big Data. 2020;3:19.</unstructured_citation>
						 <doi>10.3389/fdata.2020.00019</doi> 					</citation>
          					<citation key="rk-10.68159/z613962324-d31ce251-9a23-4874-af98-0a8f7b1f2f24">
					  <unstructured_citation>Sun Q, Fang N, Liu Z, Zhao L, Wen Y, Lin H. HybridCTrm: bridging CNN and transformer for multimodal brain image segmentation. J Healthc Eng. 2021;2021:7467261.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/z613962324-e0748436-9076-49bb-8b5c-aa537022889a">
					  <unstructured_citation>Zhang Y, Deng Y, Zhou Z, Zhang X, Jiao P, Zhao Z. Multimodal learning for fetal distress diagnosis using a multimodal medical information fusion framework. Front Physiol. 2022;13:1021400.</unstructured_citation>
						 <doi>10.3389/fphys.2022.1021400</doi> 					</citation>
          					<citation key="rk-10.68159/z613962324-7e79e315-b480-47c2-9e1e-72e510917c3a">
					  <unstructured_citation>Zhang Y, Ou W, Shi Y, Deng J, You X, Wang A. Deep medical cross-modal attention hashing. World Wide Web. 2022;25(4):1519-36.</unstructured_citation>
						 <doi>10.1007/s11280-021-00973-0</doi> 					</citation>
          					<citation key="rk-10.68159/z613962324-e673d83e-f2ee-4f3a-b804-261243267817">
					  <unstructured_citation>Shi T, Jiang H, Zheng B. C2MA-Net: cross-modal cross-attention network for acute ischemic stroke lesion segmentation based on CT perfusion scans. IEEE Trans Biomed Eng. 2022;69(1):108-18.</unstructured_citation>
						 <doi>10.1109/TBME.2021.3086210</doi> 					</citation>
          					<citation key="rk-10.68159/z613962324-a89818bb-cade-411c-bfcd-3f870abb2c01">
					  <unstructured_citation>Song X, Zhang X, Ji J, Liu Y, Wei P. Cross-modal contrastive attention model for medical report generation. In: Proc 29th Int Conf Comput Linguistics. Stroudsburg (PA): Association for Computational Linguistics; 2022. p. 2388-97.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/z613962324-3a5c6d5f-59b4-4cb4-beac-94815194f017">
					  <unstructured_citation>Zhou Z, Guo X, Yang W, Shi Y, Zhou L, Wang L, et al. Cross-modal attention-guided convolutional network for multi-modal cardiac segmentation. In: Int Workshop Mach Learn Med Imaging. Cham: Springer; 2019. p. 601-10.</unstructured_citation>
						 <doi>10.1007/978-3-030-32692-0_69</doi> 					</citation>
          					<citation key="rk-10.68159/z613962324-35af2612-74b0-460f-b003-c16e7f486612">
					  <unstructured_citation>Yan S, Wang C, Chen W, Lyu J. Swin transformer-based GAN for multi-modal medical image translation. Front Oncol. 2022;12:942511.</unstructured_citation>
						 <doi>10.3389/fonc.2022.942511</doi> 					</citation>
          					<citation key="rk-10.68159/z613962324-05f56e08-f53d-4cc6-8b02-337cf58f6217">
					  <unstructured_citation>Koivunen M, Saranto K. Nursing professionals&#039; experiences of the facilitators and barriers to the use of telehealth applications: a systematic review of qualitative studies. Scand J Caring Sci. 2018;32(1):24-44.</unstructured_citation>
						 <doi>10.1111/scs.12445</doi> 					</citation>
          					<citation key="rk-10.68159/z613962324-7c51f483-d863-4db0-b902-816e533d885b">
					  <unstructured_citation>Markello RD, Shafiei G, Tremblay C, Postuma RB, Dagher A, Misic B. Multimodal phenotypic axes of Parkinson’s disease. npj Parkinsons Dis. 2021;7(1):6.</unstructured_citation>
						 <doi>10.1038/s41531-020-00144-6</doi> 					</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
