{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T22:54:52Z","timestamp":1761519292370,"version":"build-2065373602"},"reference-count":30,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"vor","delay-in-days":365,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100000925","name":"National Health and Medical Research Council","doi-asserted-by":"publisher","award":["1125414"],"award-info":[{"award-number":["1125414"]}],"id":[{"id":"10.13039\/501100000925","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Biomedical Informatics"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1016\/j.jbi.2022.104119","type":"journal-article","created":{"date-parts":[[2022,6,14]],"date-time":"2022-06-14T13:01:44Z","timestamp":1655211704000},"page":"104119","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["Causal inference for observational longitudinal studies using deep survival models"],"prefix":"10.1016","volume":"131","author":[{"given":"Jie","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Blanca","family":"Gallego","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"6","key":"10.1016\/j.jbi.2022.104119_b0005","doi-asserted-by":"crossref","first-page":"529","DOI":"10.2217\/cer.13.65","article-title":"Role of electronic health records in comparative effectiveness research","volume":"2","author":"Gallego","year":"2013","journal-title":"J. Comparat. Effectiv. Res."},{"issue":"23","key":"10.1016\/j.jbi.2022.104119_b0010","doi-asserted-by":"crossref","first-page":"3309","DOI":"10.1002\/sim.7820","article-title":"Comparing methods for estimation of heterogeneous treatment effects using observational data from health care databases","volume":"37","author":"Wendling","year":"2018","journal-title":"Stat. Med."},{"issue":"4","key":"10.1016\/j.jbi.2022.104119_b0015","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1111\/j.0887-378X.2004.00327.x","article-title":"Evidence-based medicine, heterogeneity of treatment effects, and the trouble with averages","volume":"82","author":"Kravitz","year":"2004","journal-title":"Milbank Quart."},{"key":"10.1016\/j.jbi.2022.104119_b0020","doi-asserted-by":"crossref","unstructured":"Anand Kumar, Daniel Roberts, Kenneth E Wood, Bruce Light, Joseph E Parrillo, Satendra Sharma, Robert Suppes, Daniel Feinstein, Sergio Zanotti, Leo Taiberg, et al. Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. Crit. Care Med., 34(6):1589\u20131596, 2006.","DOI":"10.1097\/01.CCM.0000217961.75225.E9"},{"issue":"4","key":"10.1016\/j.jbi.2022.104119_b0025","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1097\/CCM.0000000000002965","article-title":"A Comparative Analysis of Sepsis Identification Methods in an Electronic Database*","volume":"46","author":"Johnson","year":"2018","journal-title":"Crit. Care Med."},{"issue":"2","key":"10.1016\/j.jbi.2022.104119_b0030","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","article-title":"Regression models and life tables (with discussion)","volume":"34","author":"David","year":"1972","journal-title":"J. Roy. Stat. Soc."},{"key":"10.1016\/j.jbi.2022.104119_b0035","doi-asserted-by":"crossref","DOI":"10.1111\/j.1467-9469.2006.00529.x","article-title":"Dynamic prediction by landmarking in event history analysis","author":"Van Houwelingen","year":"2007","journal-title":"Scand. J. Stat."},{"key":"10.1016\/j.jbi.2022.104119_b0040","doi-asserted-by":"crossref","DOI":"10.1002\/bimj.4710360613","article-title":"Fitting Survival Data to a Piecewise Linear Hazard Rate in the Presence of Covariates","author":"Recknor","year":"1994","journal-title":"Biometr. J."},{"key":"10.1016\/j.jbi.2022.104119_b0045","doi-asserted-by":"crossref","DOI":"10.1093\/biostatistics\/1.4.465","article-title":"Joint modelling of longitudinal measurements and event time data","author":"Henderson","year":"2000","journal-title":"Biostatistics"},{"key":"10.1016\/j.jbi.2022.104119_b0050","doi-asserted-by":"crossref","unstructured":"Joseph G Ibrahim, Haitao Chu, and Liddy M Chen. Basic concepts and methods for joint models of longitudinal and survival data, 2010.","DOI":"10.1200\/JCO.2009.25.0654"},{"key":"10.1016\/j.jbi.2022.104119_b0055","unstructured":"Ioana Bica, Ahmed M. Alaa, James Jordon, Mihaela van der Schaar. Estimating counterfactual treatment outcomes over time through adversarially balanced representations. arXiv preprint arXiv:2002.04083, 2020."},{"issue":"1","key":"10.1016\/j.jbi.2022.104119_b0060","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1109\/TBME.2019.2909027","article-title":"Dynamic-DeepHit: A Deep Learning Approach for Dynamic Survival Analysis With Competing Risks Based on Longitudinal Data","volume":"67","author":"Lee","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.jbi.2022.104119_b0065","series-title":"In COLING\/ACL 2006 - EMNLP 2006: 2006 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference","article-title":"Domain adaptation with structural correspondence learning","author":"Blitzer","year":"2006"},{"key":"10.1016\/j.jbi.2022.104119_b0070","series-title":"Targeted Learning: Causal Inference for Observational and Experimental Data","article-title":"Targeted Learning: Causal Inference for Observational and Experimental Data","author":"Rose","year":"2011"},{"issue":"2","key":"10.1016\/j.jbi.2022.104119_b0075","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1214\/18-AOS1709","article-title":"Generalized random forests","volume":"47","author":"Athey","year":"2019","journal-title":"The Annals of Statistics"},{"key":"10.1016\/j.jbi.2022.104119_b0080","doi-asserted-by":"crossref","unstructured":"Jie Zhu and Blanca Gallego. Targeted Estimation of Heterogeneous Treatment Effect in Observational Survival Analysis. Journal of Biomedical Informatics, page 103474, 2020.","DOI":"10.1016\/j.jbi.2020.103474"},{"key":"10.1016\/j.jbi.2022.104119_b0085","doi-asserted-by":"crossref","DOI":"10.1038\/sdata.2016.35","article-title":"MIMIC-III, a freely accessible critical care database","volume":"3","author":"Johnson","year":"2016","journal-title":"Sci Data"},{"key":"10.1016\/j.jbi.2022.104119_b0090","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1001\/jama.2016.0288","article-title":"Assessment of clinical criteria for sepsis: For the third international consensus definitions for sepsis and septic shock (sepsis-3)","volume":"315","author":"Seymour","year":"2016","journal-title":"J Am Med Assoc"},{"issue":"11","key":"10.1016\/j.jbi.2022.104119_b0095","doi-asserted-by":"crossref","first-page":"1716","DOI":"10.1038\/s41591-018-0213-5","article-title":"The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care","volume":"24","author":"Komorowski","year":"2018","journal-title":"Nat. Med."},{"issue":"1","key":"10.1016\/j.jbi.2022.104119_b0100","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1093\/biomet\/70.1.41","article-title":"The central role of the propensity score in observational studies for causal effects","volume":"70","author":"Rosenbaum","year":"1983","journal-title":"Biometrika"},{"issue":"23","key":"10.1016\/j.jbi.2022.104119_b0105","doi-asserted-by":"crossref","first-page":"4118","DOI":"10.1002\/sim.5823","article-title":"Simulating biologically plausible complex survival data","volume":"32","author":"Crowther","year":"2013","journal-title":"Statistics in medicine"},{"issue":"18","key":"10.1016\/j.jbi.2022.104119_b0110","doi-asserted-by":"crossref","first-page":"2543","DOI":"10.1001\/jama.1982.03320430047030","article-title":"Evaluating the yield of medical tests","volume":"247","author":"Frank","year":"1982","journal-title":"J. Am. Med. Assoc."},{"key":"10.1016\/j.jbi.2022.104119_b0115","unstructured":"Martin Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al. Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467, 2016."},{"key":"10.1016\/j.jbi.2022.104119_b0120","unstructured":"Jie Zhu and Blanca Gallego. Dynamic prediction of time to event with survival curves, 2021."},{"issue":"5","key":"10.1016\/j.jbi.2022.104119_b0125","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1162\/rest_a_00858","article-title":"Quantile treatment effects in the presence of covariates","volume":"102","author":"Powell","year":"2020","journal-title":"Rev. Econ. Stat."},{"issue":"4","key":"10.1016\/j.jbi.2022.104119_b0130","first-page":"1229","article-title":"Nonparametric methods for doubly robust estimation of continuous treatment effects. Journal of the Royal Statistical Society Series B","volume":"79","author":"Kennedy","year":"2017","journal-title":"Statistical Methodology"},{"issue":"1","key":"10.1016\/j.jbi.2022.104119_b0135","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12874-020-0914-6","article-title":"Evaluation of confounding in epidemiologic studies assessing alcohol consumption on the risk of ischemic heart disease","volume":"20","author":"Wallach","year":"2020","journal-title":"BMC medical research methodology"},{"issue":"8","key":"10.1016\/j.jbi.2022.104119_b0140","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 computation"},{"key":"10.1016\/j.jbi.2022.104119_b0145","doi-asserted-by":"crossref","DOI":"10.7717\/peerj.6257","article-title":"A scalable discrete-time survival model for neural networks","volume":"7","author":"Gensheimer","year":"2019","journal-title":"PeerJ"},{"issue":"1","key":"10.1016\/j.jbi.2022.104119_b0150","first-page":"1","article-title":"Recurrent Neural Networks for Multivariate Time Series with Missing Values","volume":"8","author":"Che","year":"2018","journal-title":"Scient. Rep."}],"container-title":["Journal of Biomedical Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1532046422001356?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1532046422001356?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T22:49:50Z","timestamp":1761518990000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1532046422001356"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":30,"alternative-id":["S1532046422001356"],"URL":"https:\/\/doi.org\/10.1016\/j.jbi.2022.104119","relation":{},"ISSN":["1532-0464"],"issn-type":[{"type":"print","value":"1532-0464"}],"subject":[],"published":{"date-parts":[[2022,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Causal inference for observational longitudinal studies using deep survival models","name":"articletitle","label":"Article Title"},{"value":"Journal of Biomedical Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jbi.2022.104119","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"104119"}}