{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T22:12:10Z","timestamp":1785881530432,"version":"3.56.0"},"reference-count":52,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,11,24]],"date-time":"2024-11-24T00:00:00Z","timestamp":1732406400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013365","name":"West China Hospital of Sichuan University","doi-asserted-by":"publisher","award":["ZYAI24070"],"award-info":[{"award-number":["ZYAI24070"]}],"id":[{"id":"10.13039\/501100013365","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013365","name":"West China Hospital of Sichuan University","doi-asserted-by":"publisher","award":["139170052"],"award-info":[{"award-number":["139170052"]}],"id":[{"id":"10.13039\/501100013365","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Provincial Science and Technology Support Program","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004912","name":"Sichuan University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004912","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018525","name":"Key Research and Development Program of Sichuan Province","doi-asserted-by":"publisher","award":["2023YFS0200"],"award-info":[{"award-number":["2023YFS0200"]}],"id":[{"id":"10.13039\/501100018525","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018525","name":"Key Research and Development Program of Sichuan Province","doi-asserted-by":"publisher","award":["2021YFS0091"],"award-info":[{"award-number":["2021YFS0091"]}],"id":[{"id":"10.13039\/501100018525","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72204169"],"award-info":[{"award-number":["72204169"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Methods and Programs in Biomedicine"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1016\/j.cmpb.2024.108521","type":"journal-article","created":{"date-parts":[[2024,11,24]],"date-time":"2024-11-24T02:10:23Z","timestamp":1732414223000},"page":"108521","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":4,"special_numbering":"C","title":["EMR-LIP: A lightweight framework for standardizing the preprocessing of longitudinal irregular data in electronic medical records"],"prefix":"10.1016","volume":"259","author":[{"given":"Jiawei","family":"Luo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shixin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lan","family":"Lan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shu","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingqian","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajun","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingqiang","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaobo","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.cmpb.2024.108521_bib0001","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1038\/s41746-020-00346-8","article-title":"Deep-learning-based real-time prediction of acute kidney injury outperforms human predictive performance","volume":"3","author":"Rank","year":"2020","journal-title":"NPJ Digital Med."},{"issue":"1","key":"10.1016\/j.cmpb.2024.108521_bib0002","doi-asserted-by":"crossref","first-page":"11760","DOI":"10.1038\/s41598-023-38858-4","article-title":"Predicting sepsis onset using a machine learned causal probabilistic network algorithm based on electronic health records data","volume":"13","author":"Valik","year":"2023","journal-title":"Sci. Rep."},{"issue":"4","key":"10.1016\/j.cmpb.2024.108521_bib0003","doi-asserted-by":"crossref","first-page":"e179","DOI":"10.1016\/S2589-7500(20)30018-2","article-title":"Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records","volume":"2","author":"Thorsen-Meyer","year":"2020","journal-title":"The Lancet Digital Health"},{"issue":"1","key":"10.1016\/j.cmpb.2024.108521_bib0004","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1038\/s41746-020-0221-y","article-title":"An overview of clinical decision support systems: benefits, risks, and strategies for success","volume":"3","author":"Sutton","year":"2020","journal-title":"NPJ Digital Med."},{"issue":"1","key":"10.1016\/j.cmpb.2024.108521_bib0005","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-022-00689-4","article-title":"Integrated multimodal artificial intelligence framework for healthcare applications","volume":"5","author":"Soenksen","year":"2022","journal-title":"NPJ Digital Med."},{"issue":"1","key":"10.1016\/j.cmpb.2024.108521_bib0006","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-020-17431-x","article-title":"Explainable artificial intelligence model to predict acute critical illness from electronic health records","volume":"11","author":"Lauritsen","year":"2020","journal-title":"Nat. Commun."},{"issue":"1","key":"10.1016\/j.cmpb.2024.108521_bib0007","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-24271-9","article-title":"Recurrent neural networks for multivariate time series with missing values","volume":"8","author":"Che","year":"2018","journal-title":"Sci. Rep."},{"key":"10.1016\/j.cmpb.2024.108521_bib0008","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2023.104310","article-title":"Real-time prediction of organ failures in patients with acute pancreatitis using longitudinal irregular data","volume":"139","author":"LUO","year":"2023","journal-title":"J. Biomed. Inform."},{"issue":"6","key":"10.1016\/j.cmpb.2024.108521_bib0009","doi-asserted-by":"crossref","first-page":"2992","DOI":"10.1177\/0962280214536537","article-title":"Longitudinal data subject to irregular observation: A review of methods with a focus on visit processes, assumptions, and study design","volume":"25","author":"Pullenayegum","year":"2016","journal-title":"Stat. Methods Med. Res."},{"issue":"2","key":"10.1016\/j.cmpb.2024.108521_bib0010","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1093\/jamiaopen\/ooaa006","article-title":"Machine learning for early detection of sepsis: an internal and temporal validation study","volume":"3","author":"Bedoya","year":"2020","journal-title":"JAMIA Open"},{"issue":"1","key":"10.1016\/j.cmpb.2024.108521_bib0011","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1038\/s41746-018-0029-1","article-title":"Scalable and accurate deep learning with electronic health records","volume":"1","author":"Rajkomar","year":"2018","journal-title":"NPJ Digital Med."},{"issue":"1","key":"10.1016\/j.cmpb.2024.108521_bib0012","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1038\/s41746-020-0301-z","article-title":"Deep representation learning of electronic health records to unlock patient stratification at scale","volume":"3","author":"Landi","year":"2020","journal-title":"NPJ Digital Med."},{"issue":"5","key":"10.1016\/j.cmpb.2024.108521_bib0013","doi-asserted-by":"crossref","first-page":"1589","DOI":"10.1109\/JBHI.2017.2767063","article-title":"Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis","volume":"22","author":"Shickel","year":"2017","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"10.1016\/j.cmpb.2024.108521_bib0014","series-title":"proceedings of the Proceedings of the AAAI Conference on Artificial Intelligence, F","article-title":"Data-gru: Dual-attention time-aware gated recurrent unit for irregular multivariate time series","author":"Tan","year":"2020"},{"issue":"6","key":"10.1016\/j.cmpb.2024.108521_bib0015","doi-asserted-by":"crossref","first-page":"2765","DOI":"10.1038\/s41596-021-00513-5","article-title":"Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records","volume":"16","author":"Toma\u0161ev","year":"2021","journal-title":"Nat. Protoc."},{"key":"10.1016\/j.cmpb.2024.108521_bib0016","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.neucom.2021.02.046","article-title":"A review of irregular time series data handling with gated recurrent neural networks","volume":"441","author":"Weerakody","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.cmpb.2024.108521_bib0017","article-title":"A review of deep learning methods for irregularly sampled medical time series data","author":"Sun","year":"2020","journal-title":"arXiv preprint arXiv:201012493"},{"issue":"1","key":"10.1016\/j.cmpb.2024.108521_bib0018","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1038\/s41746-022-00679-6","article-title":"Discrete-time survival analysis in the critically ill: a deep learning approach using heterogeneous data","volume":"5","author":"Thorsen-Meyer","year":"2022","journal-title":"NPJ Digital Med."},{"key":"10.1016\/j.cmpb.2024.108521_bib0019","first-page":"15","article-title":"Cleaning big data: Most time-consuming, least enjoyable data science task, survey says","volume":"23","author":"Press","year":"2016","journal-title":"Forbes"},{"issue":"1","key":"10.1016\/j.cmpb.2024.108521_bib0020","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1111\/bjet.12595","article-title":"Big Data and data science: A critical review of issues for educational research","volume":"50","author":"Daniel","year":"2019","journal-title":"British Journal of Educational Technology"},{"key":"10.1016\/j.cmpb.2024.108521_bib0021","series-title":"proceedings of the Proceedings of the AAAI Conference on Artificial Intelligence, F","article-title":"Learning the joint representation of heterogeneous temporal events for clinical endpoint prediction","author":"Liu","year":"2018"},{"key":"10.1016\/j.cmpb.2024.108521_bib0022","article-title":"PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Series","author":"Beebe-Wang","year":"2023","journal-title":"arXiv preprint arXiv:230813703"},{"key":"10.1016\/j.cmpb.2024.108521_bib0023","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.jbi.2018.04.007","article-title":"Benchmarking deep learning models on large healthcare datasets","volume":"83","author":"Purushotham","year":"2018","journal-title":"J. Biomed. Inform."},{"issue":"1","key":"10.1016\/j.cmpb.2024.108521_bib0024","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-019-0103-9","article-title":"Multitask learning and benchmarking with clinical time series data","volume":"6","author":"Harutyunyan","year":"2019","journal-title":"Sci. Data"},{"issue":"1","key":"10.1016\/j.cmpb.2024.108521_bib0025","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1093\/jamia\/ocx084","article-title":"The MIMIC Code Repository: enabling reproducibility in critical care research","volume":"25","author":"Johnson","year":"2018","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.cmpb.2024.108521_bib0026","series-title":"proceedings of the Proceedings of the ACM conference on health, inference, and learning, F[C]","article-title":"Mimic-extract: A data extraction, preprocessing, and representation pipeline for mimic-iii","author":"Wang","year":"2020"},{"issue":"12","key":"10.1016\/j.cmpb.2024.108521_bib0027","doi-asserted-by":"crossref","first-page":"1921","DOI":"10.1093\/jamia\/ocaa139","article-title":"Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data","volume":"27","author":"Tang","year":"2020","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.cmpb.2024.108521_bib0028","unstructured":"Shukla S N, Marlin B M. Modeling irregularly sampled clinical time series. arXiv preprint arXiv:181200531, 2018."},{"issue":"3","key":"10.1016\/j.cmpb.2024.108521_bib0029","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1038\/s41591-020-0789-4","article-title":"Early prediction of circulatory failure in the intensive care unit using machine learning","volume":"26","author":"Hyland","year":"2020","journal-title":"Nat. Med."},{"issue":"8","key":"10.1016\/j.cmpb.2024.108521_bib0030","doi-asserted-by":"crossref","first-page":"4258","DOI":"10.1109\/JBHI.2022.3171673","article-title":"Dynamic sepsis prediction for intensive care unit patients using XGBoost-based model with novel time-dependent features","volume":"26","author":"Liu","year":"2022","journal-title":"IEEE J. Biomed. Health Inf."},{"issue":"1","key":"10.1016\/j.cmpb.2024.108521_bib0031","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1038\/s41746-022-00689-4","article-title":"Integrated multimodal artificial intelligence framework for healthcare applications","volume":"5","author":"Soenksen L R","year":"2022","journal-title":"NPJ Digital Med."},{"issue":"1","key":"10.1016\/j.cmpb.2024.108521_bib0032","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-022-01899-x","article-title":"MIMIC-IV, a freely accessible electronic health record dataset","volume":"10","author":"Johnson","year":"2023","journal-title":"Sci. Data"},{"issue":"1","key":"10.1016\/j.cmpb.2024.108521_bib0033","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2018.178","article-title":"The eICU Collaborative Research Database, a freely available multi-center database for critical care research","volume":"5","author":"Pollard","year":"2018","journal-title":"Sci. Data"},{"key":"10.1016\/j.cmpb.2024.108521_bib0034","unstructured":"(MIT-LCP) M L F C P. eicu-code: Code and website related to the eICU Collaborative Research Database. 2018."},{"key":"10.1016\/j.cmpb.2024.108521_bib0035","series-title":"challenge 2012; proceedings of the 2012 Computing in Cardiology, F","article-title":"Predicting in-hospital mortality of icu patients: The physionet\/computing in cardiology","author":"Silva","year":"2012"},{"issue":"4","key":"10.1016\/j.cmpb.2024.108521_bib0036","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1097\/CCM.0000000000000038","article-title":"Using electronic health record data to develop and validate a prediction model for adverse outcomes on the wards","volume":"42","author":"Churpek","year":"2014","journal-title":"Crit. Care Med."},{"issue":"6","key":"10.1016\/j.cmpb.2024.108521_bib0037","doi-asserted-by":"crossref","first-page":"e481","DOI":"10.1097\/CCM.0000000000003011","article-title":"Development and evaluation of an automated machine learning algorithm for in-hospital mortality risk adjustment among critical care patients","volume":"46","author":"Delahanty","year":"2018","journal-title":"Crit. Care Med."},{"issue":"8","key":"10.1016\/j.cmpb.2024.108521_bib0038","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"10.1016\/j.cmpb.2024.108521_bib0039","unstructured":"Chung J, Gulcehre C, Cho K, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:14123555, 2014."},{"key":"10.1016\/j.cmpb.2024.108521_bib0040","series-title":"Time matters: time-aware LSTMs for predictive business process monitoring; proceedings of the Process Mining Workshops: ICPM 2020 International Workshops, Padua, Italy, October 5\u20138, 2020, Revised Selected Papers 2, F","author":"Nguyen","year":"2021"},{"key":"10.1016\/j.cmpb.2024.108521_bib0041","series-title":"proceedings of the International conference on machine learning, F","article-title":"Axiomatic attribution for deep networks","author":"Sundararajan","year":"2017"},{"issue":"1","key":"10.1016\/j.cmpb.2024.108521_bib0042","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1038\/s41746-021-00529-x","article-title":"The Framing of machine learning risk prediction models illustrated by evaluation of sepsis in general wards","volume":"4","author":"Lauritsen","year":"2021","journal-title":"NPJ Digital Med."},{"key":"10.1016\/j.cmpb.2024.108521_bib0043","series-title":"proceedings of the AMIA Annual Symposium Proceedings, F","article-title":"Real-time mortality prediction in the Intensive Care Unit","author":"Johnson","year":"2017"},{"issue":"e1","key":"10.1016\/j.cmpb.2024.108521_bib0044","doi-asserted-by":"crossref","first-page":"e2","DOI":"10.1093\/jamia\/ocv106","article-title":"Real-time prediction of inpatient length of stay for discharge prioritization","volume":"23","author":"Barnes","year":"2016","journal-title":"J. Am. Med. Inform. Assoc."},{"issue":"299","key":"10.1016\/j.cmpb.2024.108521_bib0045","doi-asserted-by":"crossref","first-page":"299ra122","DOI":"10.1126\/scitranslmed.aab3719","article-title":"A targeted real-time early warning score (TREWScore) for septic shock","volume":"7","author":"Henry","year":"2015","journal-title":"Sci. Transl. Med."},{"issue":"1","key":"10.1016\/j.cmpb.2024.108521_bib0046","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1038\/s41597-022-01782-9","article-title":"Benchmarking emergency department prediction models with machine learning and public electronic health records","volume":"9","author":"Xie","year":"2022","journal-title":"Sci. Data"},{"key":"10.1016\/j.cmpb.2024.108521_bib0047","series-title":"IEEE transactions on pattern analysis and machine intelligence","first-page":"301","article-title":"Unsupervised feature selection using feature similarity","volume":"24","author":"Mitra","year":"2002"},{"issue":"1","key":"10.1016\/j.cmpb.2024.108521_bib0048","doi-asserted-by":"crossref","first-page":"7155","DOI":"10.1038\/s41598-020-62922-y","article-title":"BEHRT: transformer for electronic health records","volume":"10","author":"Li","year":"2020","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.cmpb.2024.108521_bib0049","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1038\/s41746-021-00455-y","article-title":"Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction","volume":"4","author":"Rasmy","year":"2021","journal-title":"NPJ Digital Med."},{"issue":"11","key":"10.1016\/j.cmpb.2024.108521_bib0050","doi-asserted-by":"crossref","first-page":"1858","DOI":"10.1093\/jamia\/ocad131","article-title":"FHIR-up! Advancing knowledge from clinical data through application of standardized nursing terminologies within HL7\u00ae FHIR\u00ae","volume":"30","author":"Monsen","year":"2023","journal-title":"J. Am. Med. Inform. Assoc."},{"issue":"3","key":"10.1016\/j.cmpb.2024.108521_bib0051","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1093\/jamia\/ocac246","article-title":"Introducing HL7 FHIR Genomics Operations: a developer-friendly approach to genomics-EHR integration","volume":"30","author":"Dolin","year":"2023","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.cmpb.2024.108521_bib0052","doi-asserted-by":"crossref","DOI":"10.1016\/j.physd.2019.132306","article-title":"Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network","volume":"404","author":"Sherstinsky","year":"2020","journal-title":"Physica D"}],"container-title":["Computer Methods and Programs in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260724005145?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260724005145?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T23:42:36Z","timestamp":1734392556000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169260724005145"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2]]},"references-count":52,"alternative-id":["S0169260724005145"],"URL":"https:\/\/doi.org\/10.1016\/j.cmpb.2024.108521","relation":{},"ISSN":["0169-2607"],"issn-type":[{"value":"0169-2607","type":"print"}],"subject":[],"published":{"date-parts":[[2025,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"EMR-LIP: A lightweight framework for standardizing the preprocessing of longitudinal irregular data in electronic medical records","name":"articletitle","label":"Article Title"},{"value":"Computer Methods and Programs in Biomedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cmpb.2024.108521","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"108521"}}