<?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-SWCP-1790880687-r535171166</doi_batch_id>
		<timestamp>1790880687</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>2026</year>
				</publication_date>
				<journal_volume>
					<volume>5</volume>
				</journal_volume>
				<issue>1</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>A Variational Recurrent Neural Network with Stochastic Attention for Imputation of Irregularly Sampled ICU Time Series under Non-Random Missingness</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Minh</given_name>
            <surname>Tran</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Duc</given_name>
            <surname>Pham</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2026</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/r535171166</doi>
					<resource>https://cirpublications.com/pub/journal/1/article/r535171166</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/r535171166-ae8b8e86-77e4-4398-b0ef-5b7f07b70184">
					  <unstructured_citation>Che Z, Purushotham S, Cho K, Sontag D, Liu Y. Recurrent neural networks for multivariate time series with missing values. Sci Rep. 2018;8(1):6085.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-d8d31f15-f761-4b5a-a564-46216667ea21">
					  <unstructured_citation>Cao W, Wang D, Li J, Zhou H, Li L, Li Y. BRITS: bidirectional recurrent imputation for time series. Adv Neural Inf Process Syst. 2018;31:6776-86.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-91d6cdb1-fcc4-4497-a259-cc9ed1c1680b">
					  <unstructured_citation>Yoon J, Jordon J, van der Schaar M. GAIN: missing data imputation using generative adversarial nets. In: Proceedings of the 35th International Conference on Machine Learning (ICML); 2018; Stockholm, Sweden. PMLR; 2018. p. 5689-98.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-c828fddf-eb56-4f61-8281-aae6af51184e">
					  <unstructured_citation>Shukla SN, Marlin BM. Interpolation-prediction networks for irregularly sampled time series. arXiv [Preprint]. 2019;arXiv:1909.07782.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-5c90a545-46db-47e6-990f-2226780b1266">
					  <unstructured_citation>Rubanova Y, Chen RTQ, Duvenaud DK. Latent ordinary differential equations for irregularly-sampled time series. Adv Neural Inf Process Syst. 2019;32:5320-30.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-6a06377e-46ed-4ff2-bab4-76125af429ff">
					  <unstructured_citation>De Brouwer E, Simm J, Arany A, Moreau Y. GRU-ODE-Bayes: continuous modeling of sporadically-observed time series. Adv Neural Inf Process Syst. 2019;32:7379-90.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-05f7f4c2-76dd-40c0-b552-fd9d3cc01666">
					  <unstructured_citation>Fortuin V, Baranchuk D, Rätsch G, Mandt S. GP-VAE: deep probabilistic time series imputation. In: Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS); 2020; Palermo, Italy. PMLR; 2020. p. 1651-61.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-24711b5c-4a77-41ba-b673-a8d596014711">
					  <unstructured_citation>Shukla SN, Marlin BM. Multi-time attention networks for irregularly sampled time series. arXiv [Preprint]. 2021;arXiv:2101.10318.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-2f5909de-3ce6-4358-a27d-d8f6a510d317">
					  <unstructured_citation>Tashiro Y, Song J, Song Y, Ermon S. CSDI: conditional score-based diffusion models for probabilistic time series imputation. Adv Neural Inf Process Syst. 2021;34:24804-16.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-8e0a579e-c506-4b8a-b699-4574a92561fe">
					  <unstructured_citation>Zhang X, Zeman M, Tsiligkaridis T, Zitnik M. Graph-guided network for irregularly sampled multivariate time series. arXiv [Preprint]. 2021;arXiv:2110.05357.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-884014fb-1995-48ee-ab36-014856fce3c6">
					  <unstructured_citation>Du W, Côté D, Liu Y. SAITS: self-attention-based imputation for time series. Expert Syst Appl. 2023;219:119619.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-cdef42c6-69e5-48af-8948-e52f2d63c5dd">
					  <unstructured_citation>Biloš M, Sommer J, Rangapuram SS, Januschowski T, Günnemann S. Neural flows: efficient alternative to neural ODEs. Adv Neural Inf Process Syst. 2021;34:21325-37.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-54b86f35-7c2a-48d2-ad42-60f824942ab2">
					  <unstructured_citation>Zargar S. Introduction to sequence learning models: RNN, LSTM, GRU. Dep Mech Aerosp Eng N C State Univ. 2021;37988518.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-583486ed-4e77-4409-9f69-e21b277686c3">
					  <unstructured_citation>Agor J, Özaltın OY, Ivy JS, Capan M, Arnold R, Romero S. The value of missing information in severity of illness score development. J Biomed Inform. 2019;97:103255.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-e11203d9-cbbe-4dc2-8380-5388355280ea">
					  <unstructured_citation>Sharafoddini A, Dubin JA, Maslove DM, Lee J. A new insight into missing data in intensive care unit patient profiles: observational study. JMIR Med Inform. 2019;7(1):e11605.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-1e8118cd-b26d-4cdc-b0d0-94666afe6f81">
					  <unstructured_citation>Brinton DL, Ford DW, Martin RH, Simpson KN, Goodwin AJ, Simpson AN. Missing data methods for intensive care unit SOFA scores in electronic health records studies: results from a Monte Carlo simulation. J Comp Eff Res. 2021;11(1):47-56.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-1a041c62-966d-43b0-82b3-b96e8eecb38c">
					  <unstructured_citation>Sisk R, Sperrin M, Peek N, van Smeden M, Martin GP. Imputation and missing indicators for handling missing data in the development and deployment of clinical prediction models: a simulation study. Stat Methods Med Res. 2023;32(8):1461-77.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-654227e1-b82b-436e-8488-e698d86a25f0">
					  <unstructured_citation>Morid MA, Sheng ORL, Dunbar J. Time series prediction using deep learning methods in healthcare. ACM Trans Manag Inf Syst. 2023;14(1):1-29.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-62957072-aca7-47d5-beb5-368f3c1db0ed">
					  <unstructured_citation>Luo Y, Szolovits P, Dighe AS, Baron JM. 3D-MICE: integration of cross-sectional and longitudinal imputation for multi-analyte longitudinal clinical data. J Am Med Inform Assoc. 2018;25(6):645-53.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-38442979-e1d0-4a2e-8eeb-5e6bd92175c5">
					  <unstructured_citation>Beaulieu-Jones BK, Lavage DR, Snyder JW, Moore JH, Pendergrass SA, Bauer CR. Characterizing and managing missing structured data in electronic health records: data analysis. JMIR Med Inform. 2018;6(1):e11.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-476d8688-e79f-4ae2-89a7-079c5ff6ff12">
					  <unstructured_citation>Johnson AEW, Bulgarelli L, Shen L, Gayles A, Shammout A, et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci Data. 2023;10(1):1.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-810c75fc-9ad4-490b-a7f6-c55f386924ee">
					  <unstructured_citation>Pollard TJ, Johnson AEW, Raffa JD, Celi LA, Mark RG, Badawi O. The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Sci Data. 2018;5(1):180178.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-7fcb5c3f-73a7-4300-b961-2237795a91a4">
					  <unstructured_citation>Hyland SL, Faltys M, Hüser M, Lyu X, Gumbsch T, et al. Early prediction of circulatory failure in the intensive care unit using machine learning. Nat Med. 2020;26(3):364-73.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-df61f8ab-5f58-4200-a445-5b76e10c2bce">
					  <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>
											</citation>
          					<citation key="rk-10.68159/r535171166-c762a902-bda0-4a3f-aad3-937d2750db9c">
					  <unstructured_citation>Sheikhalishahi S, Balaraman V, Osmani V. Benchmarking machine learning models on multi-centre eICU critical care dataset. PLoS One. 2020;15(7):e0235424.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-568bda86-b793-4d63-807f-7fb19c17ccb7">
					  <unstructured_citation>Kowsar I, Rabbani SB, Samad MD. Attention-based imputation of missing values in electronic health records tabular data. In: 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI); 2024; Orlando, FL, USA. IEEE; 2024. p. 177-82.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-e5d27895-15b2-49c0-b72e-8d9477cb5e04">
					  <unstructured_citation>Kazdaghli S, Kerenidis I, Kieckbusch J, Teare P. Improved clinical data imputation via classical and quantum determinantal point processes. eLife. 2024;12:RP89947.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-3bdc4692-47ca-479f-9cf1-4a1db9090407">
					  <unstructured_citation>Digitale J, Franzon D, Pletcher MJ, McCulloch CE, Gennatas ED. Methods for addressing missingness in electronic health record data for clinical prediction models: comparative evaluation. JMIR Med Inform. 2025;13(1):e79307.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/r535171166-6560f187-cf7b-47f8-b88a-0796cc63170b">
					  <unstructured_citation>Qian L, Ellis HL, Wang T, Wang J, Mitra R, Dobson R, et al. How deep is your guess? a fresh perspective on deep learning for medical time-series imputation. IEEE J Biomed Health Inform. 2025.</unstructured_citation>
											</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
