{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T19:47:35Z","timestamp":1790452055103,"version":"4.1.0"},"reference-count":57,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Biomed. Health Inform."],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1109\/jbhi.2025.3577328","type":"journal-article","created":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T13:42:54Z","timestamp":1749217374000},"page":"9170-9180","source":"Crossref","is-referenced-by-count":8,"title":["TimEHR: Image-Based Time Series Generation for Electronic Health Records"],"prefix":"10.1109","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9902-0233","authenticated-orcid":false,"given":"Hojjat","family":"Karami","sequence":"first","affiliation":[{"name":"Signal Processing Laboratory 5 (LTS5), EPFL, Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8826-3870","authenticated-orcid":false,"given":"Mary-Anne","family":"Hartley","sequence":"additional","affiliation":[{"name":"Laboratory for Intelligent Global Health &amp; Humanitarian Response Technologies (LiGHT), EPFL, Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9536-4947","authenticated-orcid":false,"given":"David","family":"Atienza","sequence":"additional","affiliation":[{"name":"Embedded Systems Laboratory (ESL), EPFL, Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2341-4383","authenticated-orcid":false,"given":"Anisoara","family":"Ionescu","sequence":"additional","affiliation":[{"name":"Signal Processing Laboratory 5 (LTS5), EPFL, Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1002\/wics.1549"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eij.2020.07.003"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1097\/MLR.0b013e3182585355"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.08.363"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3390\/app122312320"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-023-00927-3"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-9735-0_5"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging9030069"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-018-0070-0"},{"key":"ref11","article-title":"ClinicalBERT: Modeling clinical notes and predicting hospital readmission","author":"Huang","year":"2020"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocy142"},{"key":"ref13","article-title":"CorGAN: Correlation-capturing convolutional generative adversarial networks for generating synthetic healthcare records","author":"Torfi","year":"2020"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-023-00888-7"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocae229"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2023.104306"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2022.104269"},{"key":"ref18","first-page":"301","article-title":"Doctor AI: Predicting clinical events via recurrent neural networks","volume-title":"Proc. 1st Mach. Learn. Healthcare Conf.","author":"Choi","year":"2016"},{"key":"ref19","article-title":"RETAIN: An interpretable predictive model for healthcare using reverse time attention mechanism","author":"Choi","year":"2017"},{"key":"ref20","first-page":"4353","article-title":"Set functions for time series","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Horn","year":"2020"},{"key":"ref21","article-title":"Graph-guided network for irregularly sampled multivariate time series","author":"Zhang","year":"2022"},{"key":"ref22","article-title":"T-CGAN: Conditional generative adversarial network for data augmentation in noisy time series with irregular sampling","author":"Ramponi","year":"2018"},{"key":"ref23","first-page":"5937","article-title":"Learning from irregularly-sampled time series: A missing data perspective","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Li","year":"2020"},{"key":"ref24","article-title":"Time-parameterized convolutional neural networks for irregularly sampled time series","author":"Kosma","year":"2023"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3490354.3494387"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2022.10.006"},{"key":"ref27","article-title":"Imaging time-series to improve classification and imputation","author":"Wang","year":"2015"},{"key":"ref28","article-title":"Classification of time-series images using deep convolutional neural networks","author":"Hatami","year":"2017"},{"key":"ref29","article-title":"Multivariate time series classification using dilated convolutional neural network","author":"Yazdanbakhsh","year":"2019"},{"key":"ref30","article-title":"Time series anomaly detection using convolutional neural networks and transfer learning","author":"Wen","year":"2019"},{"key":"ref31","article-title":"Time series as images: Vision transformer for irregularly sampled time series","author":"Li","year":"2023"},{"key":"ref32","article-title":"Improved training of wasserstein GANss","author":"Gulrajani","year":"2017"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref34","article-title":"Time-series generative adversarial networks","volume":"32","author":"Yoon","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3419394.3423643"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM51629.2021.00058"},{"key":"ref37","article-title":"GT-GAN: General purpose time series synthesis with generative adversarial networks","author":"Jeon","year":"2022"},{"key":"ref38","article-title":"TS-diffusion: Generating highly complex time series with diffusion models","author":"Li","year":"2023"},{"key":"ref39","article-title":"Quick and easy time series generation with established image-based GANs","author":"Brophy","year":"2019"},{"key":"ref40","article-title":"Conditional GAN for timeseries generation","author":"Smith","year":"2020"},{"key":"ref41","article-title":"A spectral enabled GAN for time series data generation","author":"Smith","year":"2021"},{"key":"ref42","article-title":"Real-valued (Medical) time series generation with recurrent conditional GANs","author":"Esteban","year":"2017"},{"key":"ref43","article-title":"Deep multi-scale video prediction beyond mean square error","author":"Mathieu","year":"2016"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.278"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref46","article-title":"Modeling tabular data using conditional GAN","author":"Xu","year":"2019"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.35"},{"key":"ref48","article-title":"Predicting in-hospital mortality of icu patients: The physionet\/computing in cardiology challenge","author":"Silva","year":"2012","journal-title":"Comput. Cardiology"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1097\/CCM.0000000000004145"},{"key":"ref50","article-title":"Reliable fidelity and diversity metrics for generative models","author":"Naeem","year":"2020"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.52202\/079017-1400"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.12.136"},{"key":"ref53","first-page":"4905","article-title":"Understanding the effective receptive field in deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Luo","year":"2016"},{"key":"ref54","article-title":"Differentially private generative adversarial network","author":"Xie","year":"2018"},{"key":"ref55","first-page":"361","article-title":"Reproducibility in critical care: A mortality prediction case study","volume-title":"Proc. 2nd Mach. Learn. Healthcare Conf.","author":"Johnson","year":"2017"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330701"},{"key":"ref57","article-title":"Improved precision and recall metric for assessing generative models","author":"Kynknniemi","year":"2019"}],"container-title":["IEEE Journal of Biomedical and Health Informatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6221020\/11284538\/11027528.pdf?arnumber=11027528","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T18:34:50Z","timestamp":1765305290000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11027528\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":57,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/jbhi.2025.3577328","relation":{},"ISSN":["2168-2194","2168-2208"],"issn-type":[{"value":"2168-2194","type":"print"},{"value":"2168-2208","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12]]}}}