{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T20:08:31Z","timestamp":1777925311854,"version":"3.51.4"},"reference-count":94,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"NIHR Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust"},{"name":"EPSRC Centre for Doctoral Training in Data-Driven Health"},{"name":"Kings-China Scholarship Council PhD Scholarship Programme","award":["CSC202008060096"],"award-info":[{"award-number":["CSC202008060096"]}]},{"name":"Research Fellowship Award from the Department of Medicine at Dalhousie University"},{"name":"NIHR Maudsley Biomedical Research Centre at South London"},{"name":"Institute of Psychiatry, Psychology &amp; Neuroscience"},{"name":"Innovate U.K.","award":["10104845"],"award-info":[{"award-number":["10104845"]}]},{"name":"NIHR Biomedical Research Centre at SLaM"},{"name":"Kings College London, London, U.K."},{"DOI":"10.13039\/501100012317","name":"UCLH Biomedical Research Centre","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012317","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Biomed. Health Inform."],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1109\/jbhi.2025.3568778","type":"journal-article","created":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T14:01:25Z","timestamp":1746799285000},"page":"6814-6829","source":"Crossref","is-referenced-by-count":1,"title":["How Deep is Your Guess? A Fresh Perspective on Deep Learning for Medical Time-Series Imputation"],"prefix":"10.1109","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3930-045X","authenticated-orcid":false,"given":"Linglong","family":"Qian","sequence":"first","affiliation":[{"name":"Department of Biostatistics and Health Informatics, King&#x2019;s College London, London, U.K."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6428-0158","authenticated-orcid":false,"given":"Hugh Logan","family":"Ellis","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Health Informatics, King&#x2019;s College London, London, U.K."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0437-0557","authenticated-orcid":false,"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Health Informatics, King&#x2019;s College London, London, U.K."}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1581-8369","authenticated-orcid":false,"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Warwick, Coventry, U.K."}]},{"given":"Robin","family":"Mitra","sequence":"additional","affiliation":[{"name":"Department of Statistics, University College London, London, U.K."}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4224-9245","authenticated-orcid":false,"given":"Richard","family":"Dobson","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Health Informatics, King&#x2019;s College London, London, U.K."}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6203-2727","authenticated-orcid":false,"given":"Zina","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Health Informatics, King&#x2019;s College London, London, U.K."}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1098\/rsos.170175"},{"key":"ref2","article-title":"DeepMPM: A mortality risk prediction model using longitudinal EHR data","volume-title":"BMC Bioinf.","volume":"23","author":"Yang","year":"2022"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103671"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.3390\/jcm12175658"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2023.104430"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0245157"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.4266\/acc.2018.00290"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-023-00986-6"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-24271-9"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-009-0295-6"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab489"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-021-00518-0"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-2013-002592"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocy106"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocad066"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-022-00596-z"},{"key":"ref17","article-title":"Deep learning for multivariate time series imputation: A survey","author":"Wang","year":"2024"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbx044"},{"key":"ref19","article-title":"Deep imputation of missing values in time series health data: A review with benchmarking","volume-title":"J. Biomed. Inform.","volume":"144","author":"Kazijevs","year":"2023"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2023.102587"},{"key":"ref21","article-title":"TSI-bench: Benchmarking time series imputation","author":"Du","year":"2024"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.2021.0068"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1038\/nrg3208"},{"issue":"4","key":"ref24","first-page":"H627","article-title":"Continuous vital sign analysis for predicting and preventing noncardiac complications after major surgery","volume":"312","author":"Moss","year":"2017","journal-title":"Amer. J. Physiol.-Heart Circulatory Physiol."},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2015.0417"},{"key":"ref26","first-page":"452","article-title":"Medical temporal-knowledge discovery via temporal abstraction","volume-title":"Proc. AMIA Annu. Symp. Proc.","volume":"2009","author":"Moskovitch","year":"2009"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2014.03.016"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehy462"},{"issue":"64\/67","key":"ref29","first-page":"2","article-title":"Recurrent neural networks","volume":"5","author":"Medsker","year":"2001","journal-title":"Des. Appl."},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"ref32","first-page":"133","article-title":"Introduction to Gaussian processes","volume":"168","author":"MacKay","year":"1998","journal-title":"NATO ASI Ser. F Comput. Syst. Sci."},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2765202"},{"key":"ref34","first-page":"21371","article-title":"Uqgan: A unified model for uncertainty quantification of deep classifiers trained via conditional GANs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Oberdiek","year":"2022"},{"key":"ref35","first-page":"6572","article-title":"Neural ordinary differential equations","volume-title":"Proc. 32nd Int. Conf. Neural Inf. Process. Syst.","author":"Chen","year":"2018"},{"key":"ref36","first-page":"19388","article-title":"Modeling irregular time series with continuous recurrent units","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Schirmer","year":"2022"},{"key":"ref37","article-title":"Neural Markov controlled SDE: Stochastic optimization for continuous-time data","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Park","year":"2021"},{"key":"ref38","first-page":"1434","article-title":"Latent ordinary differential equations for irregularly-sampled time series","volume-title":"Proc. 33rd Int. Conf. Neural Inf. Process. Syst.","author":"Rubanova","year":"2019"},{"key":"ref39","first-page":"5321","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Ho","year":"2020"},{"key":"ref40","article-title":"Score-based generative modeling through stochastic differential equations","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Song","year":"2020"},{"key":"ref41","article-title":"Pypots: A python toolbox for data mining on partially-observed time series","author":"Du","year":"2023"},{"key":"ref42","first-page":"6776","article-title":"BRITS: Bidirectional recurrent imputation for time series","volume-title":"Proc. 32nd Int. Conf. Neural Inf. Process. Syst.","author":"Cao","year":"2018"},{"key":"ref43","article-title":"Knowledge enhanced conditional imputation for healthcare time-series","author":"Qian","year":"2024"},{"key":"ref44","article-title":"Multi-directional recurrent neural networks: A novel method for estimating missing data","author":"Yoon","year":"2024"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"ref46","article-title":"Tsmixer: An all-MLP architecture for time series forecasting","author":"Chen","year":"2023"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.12.118"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/BigData50022.2020.9378408"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2022.3224880"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119619"},{"key":"ref51","first-page":"3939","article-title":"Imaging time-series to improve classification and imputation","volume-title":"Proc. Proc. 24th Int. Conf. Artif. Intell.","author":"Wang","year":"2015"},{"key":"ref52","article-title":"TimesNet: Temporal 2D-variation modeling for general time series analysis","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Wu","year":"2023"},{"issue":"4","key":"ref53","first-page":"1723","article-title":"Deep learning using 2D convolutional neural networks for multivariate clinical time series: A review and comparison","volume":"27","author":"Zhao","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref54","article-title":"Rethinking 1D-CNN for time series classification: A stronger baseline","author":"Tang","year":"2020"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.3389\/fdata.2021.693869"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2024.3443141"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403118"},{"key":"ref58","first-page":"4413","article-title":"Miwae: Deep generative modelling and imputation of incomplete data sets","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Mattei","year":"2019"},{"key":"ref59","first-page":"1651","article-title":"GP-VAE: Deep probabilistic time series imputation","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Fortuin","year":"2020"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107501"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3053599"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2021.3123839"},{"key":"ref63","first-page":"16654","article-title":"Probabilistic imputation for time-series classification with missing data","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kim","year":"2023"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM55620.2022.9995587"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2017.8257992"},{"key":"ref66","first-page":"1603","article-title":"Multivariate time series imputation with generative adversarial networks","volume-title":"Proc. 32nd Int. Conf. Neural Inf. Process. Syst.","author":"Luo","year":"2018"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/429"},{"key":"ref68","first-page":"11238","article-title":"Naomi: Non-autoregressive multiresolution sequence imputation","volume-title":"Proc. 33rd Int. Conf. Neural Inf. Process. Syst.","author":"Liu","year":"2019"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17086"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-022-01718-0"},{"key":"ref71","first-page":"24804","article-title":"CSDI: Conditional score-based diffusion models for probabilistic time series imputation","volume":"34","author":"Tashiro","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref72","article-title":"Diffusion-based time series imputation and forecasting with structured state space models","author":"Alcaraz","year":"2022","journal-title":"Trans. Mach. Learn. Res."},{"key":"ref73","first-page":"4485","article-title":"Provably convergent Schrdinger bridge with applications to probabilistic time series imputation","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"2023","author":"Chen","year":"2023"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3614840"},{"key":"ref75","article-title":"A comprehensive survey on deep learning for missing data imputation: Taxonomy, challenges, and future directions","volume":"93","author":"Zhang","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref76","article-title":"On mixture density networks: A survey","author":"Zhuang","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00597"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1016\/0370-1573(76)90029-6"},{"key":"ref79","volume-title":"Dynamic Programming and Markov Processes","author":"Howard","year":"1960"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi5020013"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2022.103826"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86362-3_20"},{"key":"ref83","article-title":"Yet another ICU benchmark: A flexible multi-center framework for clinical ml","author":"Water","year":"2023"},{"key":"ref84","article-title":"Unveiling the secrets: How masking strategies shape time series imputation","volume-title":"Proc. 12th Int. Conf. Learn. Representations","author":"Qian","year":"2024"},{"key":"ref85","article-title":"Addressing class imbalance in electronic health records data imputation","volume-title":"Proc. 6th Int. Workshop Knowl. Discov. Healthcare Data Co-Located 32nd Int. Joint Conf. Artif. Intell.","volume":"3479","author":"Qian","year":"2023"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3315343"},{"key":"ref87","first-page":"245","article-title":"Predicting in-hospital mortality of ICU patients: The physionet\/computing in cardiology challenge 2012","volume":"39","author":"Silva","year":"2012","journal-title":"Comput. Cardiol."},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.35"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1136\/bmjgh-2018-000798"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1111\/add.16133"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1177\/1751143718774713"},{"key":"ref92","article-title":"Uncertainty-aware deep attention recurrent neural network for heterogeneous time series imputation","author":"Qian","year":"2024"},{"key":"ref93","first-page":"6216","article-title":"Conformal time-series forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Stankeviciute","year":"2021"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM54844.2022.00072"}],"container-title":["IEEE Journal of Biomedical and Health Informatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6221020\/11152528\/10994403.pdf?arnumber=10994403","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T17:47:22Z","timestamp":1757353642000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10994403\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":94,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/jbhi.2025.3568778","relation":{},"ISSN":["2168-2194","2168-2208"],"issn-type":[{"value":"2168-2194","type":"print"},{"value":"2168-2208","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9]]}}}