{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T23:23:45Z","timestamp":1789255425603,"version":"build-2803163510"},"reference-count":75,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T00:00:00Z","timestamp":1760572800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Biomedical Informatics"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1016\/j.jbi.2025.104928","type":"journal-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T02:34:23Z","timestamp":1762482863000},"page":"104928","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":5,"special_numbering":"C","title":["Attention-based synthetic data generation for calibration-enhanced survival analysis: A case study for chronic kidney disease using electronic health records"],"prefix":"10.1016","volume":"172","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8749-7280","authenticated-orcid":false,"given":"Nicholas I-Hsien","family":"Kuo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Blanca","family":"Gallego","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0390-661X","authenticated-orcid":false,"given":"Louisa","family":"Jorm","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jbi.2025.104928_b0005","doi-asserted-by":"crossref","unstructured":"R. Nosowsky, T. J. Giordano, The Health Insurance Portability and Accountability Act of 1996 (HIPAA) Privacy Rule: Implications for Clinical Research, Annual Review of Medicine 57 (2006) 575\u2013590.","DOI":"10.1146\/annurev.med.57.121304.131257"},{"key":"10.1016\/j.jbi.2025.104928_b0010","doi-asserted-by":"crossref","first-page":"537","DOI":"10.5694\/j.1326-5377.2010.tb04041.x","article-title":"Privacy and the use of health data for research","volume":"193","author":"O\u2019Keefe","year":"2010","journal-title":"Med. J. Aust."},{"key":"10.1016\/j.jbi.2025.104928_b0015","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1038\/s41597-022-01784-7","article-title":"The health gym: synthetic health-related datasets for the development of reinforcement learning algorithms","volume":"9","author":"Kuo","year":"2022","journal-title":"Sci. Data"},{"key":"10.1016\/j.jbi.2025.104928_b0020","doi-asserted-by":"crossref","DOI":"10.2196\/51388","article-title":"Enriching data science and health care education: application and impact of synthetic data sets through the health gym project","volume":"10","author":"Kuo","year":"2024","journal-title":"JMIR Medical Education"},{"key":"10.1016\/j.jbi.2025.104928_b0025","doi-asserted-by":"crossref","first-page":"3396","DOI":"10.1109\/TFUZZ.2024.3372905","article-title":"Canonical fuzzy modeling of disease state","volume":"32","author":"Ingabire","year":"2024","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.jbi.2025.104928_b0030","doi-asserted-by":"crossref","unstructured":"I. Kowsar, S. B. Rabbani, M. D. Samad, Attention-Based Imputation of Missing Values in Electronic Health Records Tabular Data, in: 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI), IEEE, 2024, pp. 177\u2013182. doi:10.1109\/ICHI61247.2024.00030.","DOI":"10.1109\/ICHI61247.2024.00030"},{"key":"10.1016\/j.jbi.2025.104928_b0035","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12874-020-00977-1","article-title":"Generation and evaluation of synthetic patient data","volume":"20","author":"Goncalves","year":"2020","journal-title":"BMC Med. Res. Method."},{"key":"10.1016\/j.jbi.2025.104928_b0040","doi-asserted-by":"crossref","first-page":"12320","DOI":"10.3390\/app122312320","article-title":"Privacy and utility of private synthetic data for medical data analyses","volume":"12","author":"Appenzeller","year":"2022","journal-title":"Appl. Sci."},{"key":"10.1016\/j.jbi.2025.104928_b0045","doi-asserted-by":"crossref","unstructured":"F. Hermsen, A. Mandal, Privacy and Utility Evaluation of Synthetic Tabular Data for Machine Learning, in: Privacy and Identity Management. Sharing in a Digital World, Springer, 2023, pp. 265\u2013281. doi:10.1007\/978-3-031-57978-3_17.","DOI":"10.1007\/978-3-031-57978-3_17"},{"key":"10.1016\/j.jbi.2025.104928_b0050","doi-asserted-by":"crossref","unstructured":"F. J. Sarmin, A. R. Sarkar, Y. Wang, N. Mohammed, Synthetic Data: Revisiting the Privacy-Utility Trade-Off, arXiv preprint arXiv:2407.07926 (2024).","DOI":"10.1007\/s10207-025-01072-6"},{"key":"10.1016\/j.jbi.2025.104928_b0055","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1038\/sj.bjc.6601118","article-title":"Survival analysis Part I: basic concepts and first analyses","volume":"89","author":"Clark","year":"2003","journal-title":"Br. J. Cancer"},{"key":"10.1016\/j.jbi.2025.104928_b0060","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.3390\/ijerph21111424","article-title":"Determinants of length of stay for medical inpatients using survival analysis","volume":"21","author":"Kim","year":"2024","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"10.1016\/j.jbi.2025.104928_b0065","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.clon.2020.07.014","article-title":"Understanding survival analysis in clinical trials","volume":"33","author":"Emmerson","year":"2021","journal-title":"Clin. Oncol."},{"key":"10.1016\/j.jbi.2025.104928_b0070","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1186\/s13014-016-0609-7","article-title":"Personalized Treatment planning with a model of radiation therapy outcomes for use in multiobjective optimization of IMRT plans for prostate cancer","volume":"11","author":"Smith","year":"2016","journal-title":"Radiat. Oncol."},{"key":"10.1016\/j.jbi.2025.104928_b0075","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1001\/jama.2017.12126","article-title":"Discrimination and calibration of clinical prediction models: users\u2019 guides to the medical literature","volume":"318","author":"Alba","year":"2017","journal-title":"JAMA"},{"key":"10.1016\/j.jbi.2025.104928_b0080","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1186\/s12916-019-1466-7","article-title":"Calibration: the achilles heel of predictive analytics","volume":"17","author":"Van Calster","year":"2019","journal-title":"BMC Med."},{"key":"10.1016\/j.jbi.2025.104928_b0085","unstructured":"D. . Kingma, M. Welling, Auto-Encoding Variational Bayes, in: International Conference on Learning Representations, 2014. URL: https:\/\/arxiv.org\/abs\/1312.6114."},{"key":"10.1016\/j.jbi.2025.104928_b0090","unstructured":"I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative Ad- versarial Nets, in: Advances in Neural Information Processing Systems, 2014. URL: https:\/\/papers.nips.cc\/paper\/ 5423-generative-adversarial-nets."},{"key":"10.1016\/j.jbi.2025.104928_b0095","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"10.1016\/j.jbi.2025.104928_b0100","unstructured":"N. I.-H. Kuo, B. Gallego, L. Jorm, CK4Gen: A Knowledge Distillation Framework for Generating High-Utility Synthetic Survival Datasets in Healthcare, arXiv preprint arXiv:2410.16872 (2024)."},{"key":"10.1016\/j.jbi.2025.104928_b0105","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","article-title":"Regression models and life-tables","volume":"34","author":"Cox","year":"1972","journal-title":"J. Roy. Stat. Soc.: Ser. B (Methodol.)"},{"key":"10.1016\/j.jbi.2025.104928_b0110","unstructured":"D. Bahdanau, K. Cho, Y. Bengio, Neural Machine Translation by Jointly Learning to Align and Translate, arXiv preprint arXiv:1409.0473 (2014)."},{"key":"10.1016\/j.jbi.2025.104928_b0115","unstructured":"A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, I. Polosukhin, Attention Is All You Need, in: Advances in Neural Information Processing Systems, volume 30, 2017, pp. 5998\u20136008. doi:10.5555\/3295222.3295349."},{"key":"10.1016\/j.jbi.2025.104928_b0120","unstructured":"J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding, arXiv preprint arXiv:1810.04805 (2018)."},{"key":"10.1016\/j.jbi.2025.104928_b0125","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0199920","article-title":"Chronic kidney disease in patients at high risk of cardiovascular disease in the United Arab Emirates: a population-based Study","volume":"13","author":"Al-Shamsi","year":"2018","journal-title":"PLoS One"},{"key":"10.1016\/j.jbi.2025.104928_b0130","doi-asserted-by":"crossref","unstructured":"G. E. Hinton, A. Krizhevsky, S. D. Wang, Transforming Auto-Encoders, in: Proceedings of the 21st International Conference on Artificial Neural Networks (ICANN 2011), Springer, 2011, pp. 44\u201351. doi:10.1007\/978-3-642-21735-7_6.","DOI":"10.1007\/978-3-642-21735-7_6"},{"key":"10.1016\/j.jbi.2025.104928_b0135","unstructured":"J. Ho, A. Jain, P. Abbeel, Denoising Diffusion Probabilistic Models, in: Advances in Neural Information Processing Systems, volume 33, Curran Associates, Inc., 2020, pp. 6840\u20136851. doi:10.5555\/3495724.3495946."},{"key":"10.1016\/j.jbi.2025.104928_b0140","doi-asserted-by":"crossref","unstructured":"J.-Y. Zhu, T. Park, P. Isola, A. A. Efros, Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2223\u20132232. doi:10.1109\/ICCV.2017.244.","DOI":"10.1109\/ICCV.2017.244"},{"key":"10.1016\/j.jbi.2025.104928_b0145","unstructured":"S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, H. Lee, Generative Adversarial Text to Image Synthesis, in: Proceedings of the 33rd International Conference on Machine Learning (ICML), PMLR, 2016, pp. 1060\u20131069. doi:10.48550\/arXiv.2006.11239."},{"key":"10.1016\/j.jbi.2025.104928_b0150","unstructured":"U. Singer, A. Polyak, T. Hayes, X. Yin, J. An, S. Zhang, Q. Hu, H. Yang, O. Ashual, O. Gafni, et al., Make-A-Video: Text-to-Video Generation without Text-Video Data, arXiv preprint arXiv:2209.14792 (2022)."},{"key":"10.1016\/j.jbi.2025.104928_b0155","doi-asserted-by":"crossref","unstructured":"H. He, Y. Bai, E. A. Garcia, S. Li, ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning, in: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), IEEE, 2008, pp. 1322\u20131328. URL: https: \/\/ieeexplore.ieee.org\/document\/4633969. doi:10.1109\/IJCNN.2008.4633969.","DOI":"10.1109\/IJCNN.2008.4633969"},{"key":"10.1016\/j.jbi.2025.104928_b0160","doi-asserted-by":"crossref","unstructured":"H. Han, W.-Y. Wang, B.-H. Mao, Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning, in: International Conference on Intelligent Computing, Springer, 2005, pp. 878\u2013887. URL: https:\/\/link.springer.com\/chapter\/10.1007\/11538059_91. doi:10.1007\/11538059_91.","DOI":"10.1007\/11538059_91"},{"key":"10.1016\/j.jbi.2025.104928_b0165","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1504\/IJKESDP.2011.039875","article-title":"Borderline over-sampling for imbalanced data classification","volume":"3","author":"Nguyen","year":"2011","journal-title":"Int. Jo. Knowledge Eng. Soft Data Paradigms"},{"key":"10.1016\/j.jbi.2025.104928_b0170","unstructured":"M. Arjovsky, S. Chintala, L. Bottou, Wasserstein Generative Adversarial Networks, in: Proceedings of the 34th International Conference on Machine Learning, PMLR, 2017, pp. 214\u2013223. URL: https:\/\/proceedings.mlr.press\/v70\/arjovsky17a.html."},{"key":"10.1016\/j.jbi.2025.104928_b0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2023.104436","article-title":"Generating synthetic clinical data that capture class imbalanced distributions with generative adversarial networks: example using antiretroviral therapy for HIV","volume":"144","author":"Kuo","year":"2023","journal-title":"J. Biomed. Inform."},{"key":"10.1016\/j.jbi.2025.104928_b0180","unstructured":"N. I.-H. Kuo, L. Jorm, S. Barbieri, Synthetic Health-Related Longitudinal Data with Mixed-Type Variables Generated Using Diffusion Models, arXiv preprint arXiv:2303.12281 (2023)."},{"key":"10.1016\/j.jbi.2025.104928_b0185","unstructured":"U.S. Department of Homeland Security (DHS), S&T Awards Contracts to Four Startups to Develop Privacy-Enhancing Synthetic Data Generation Capabilities, 2024. URL: https:\/\/www.dhs.gov\/science-and-technology\/news\/2024\/10\/08\/ st-awards-contracts-four-startups-develop-privacy-enhancing-synthetic-data-generation-capabilities, accessed: 2024-11-18."},{"key":"10.1016\/j.jbi.2025.104928_b0190","unstructured":"Digital Health Cooperative Research Centre (DHCRC), Synthetic data community of practice (synd), 2023. URL: https:\/\/ digitalhealthcrc.com\/synthetic-data-community-of-practice-synd\/, accessed: 2024-11-18."},{"key":"10.1016\/j.jbi.2025.104928_b0195","unstructured":"Microsoft Startups, South Australian Health: Synthetic Data Enables Safe EHR Data Sharing, 2023. URL: https:\/\/www.microsoft.com\/ en-us\/startups\/blog\/south-australian-health-synthetic-data-safe-ehr-data-sharing\/, accessed: 2024-11-18."},{"key":"10.1016\/j.jbi.2025.104928_b0200","article-title":"Neural Discrete Representation Learning","volume":"30","author":"Van Den Oord","year":"2017","journal-title":"Adv. Neural Inf. Proces. Syst."},{"key":"10.1016\/j.jbi.2025.104928_b0205","unstructured":"T. Che, Y. Li, A. P. Jacob, Y. Bengio, W. Li, Mode Regularized Generative Adversarial Networks, arXiv preprint arXiv:1612.02136 (2016)."},{"key":"10.1016\/j.jbi.2025.104928_b0210","unstructured":"A. Norcliffe, B. Cebere, F. Imrie, P. Lio, M. van der Schaar, SurvivalGAN: Generating Time-to-Event Data for Survival Analysis, in: Proceedings of the 26th International Conference on Artificial Intelligence and Statistics, PMLR, 2023, pp. 10279\u201310304. doi:10.48550\/ arXiv.2302.12749."},{"key":"10.1016\/j.jbi.2025.104928_b0215","unstructured":"N. Micheletti, R. Marchesi, N. I.-H. Kuo, S. Barbieri, G. Jurman, V. Osmani, Generative AI Mitigates Representation Bias Using Synthetic Health Data, medRxiv (2023)."},{"key":"10.1016\/j.jbi.2025.104928_b0220","unstructured":"N. I.-H. Kuo, B. Gallego, L. Jorm, Masked Clinical Modelling: A Framework for Synthetic and Augmented Survival Data Generation, MedInfo 2025 (to appear) (2025)."},{"key":"10.1016\/j.jbi.2025.104928_b0225","unstructured":"G. Hinton, O. Vinyals, J. Dean, Distilling the Knowledge in a Neural Network, arXiv preprint arXiv:1503.02531 (2015)."},{"key":"10.1016\/j.jbi.2025.104928_b0230","unstructured":"C. M. Bishop, Pattern Recognition and Machine Learning, Springer, New York, NY, USA, 2006."},{"key":"10.1016\/j.jbi.2025.104928_b0235","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep Residual Learning for Image Recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770\u2013778. doi:10.1109\/CVPR.2016.90.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.jbi.2025.104928_b0240","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep Residual Learning for Image Recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770\u2013778. doi:10.1109\/CVPR.2016.90.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.jbi.2025.104928_b0245","unstructured":"J. L. Ba, J. R. Kiros, G. E. Hinton, Layer Normalization, arXiv preprint arXiv:1607.06450 (2016)."},{"key":"10.1016\/j.jbi.2025.104928_b0250","unstructured":"D. P. Kingma, J. Ba, Adam: A Method for Stochastic Optimization, arXiv preprint arXiv:1412.6980 (2014)."},{"key":"10.1016\/j.jbi.2025.104928_b0255","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0158765","article-title":"Global prevalence of chronic kidney disease\u2014a systematic review and meta-analysis","volume":"11","author":"Hill","year":"2016","journal-title":"PLoS One"},{"key":"10.1016\/j.jbi.2025.104928_b0260","doi-asserted-by":"crossref","unstructured":"A. S. Levey, K.-U. Eckardt, Y. Tsukamoto, A. Levin, J. Coresh, J. Rossert, D. De Zeeuw, T. H. Hostetter, N. Lameire, G. Eknoyan, Definition and Classification of Chronic Kidney Disease: A Position Statement from Kidney Disease: Improving Global Outcomes (KDIGO), Kidney International 67 (2005) 2089\u20132100.","DOI":"10.1111\/j.1523-1755.2005.00365.x"},{"key":"10.1016\/j.jbi.2025.104928_b0265","unstructured":"World Health Organization, Diabetes, 2024. URL: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/diabetes, accessed: 2024-11-22."},{"key":"10.1016\/j.jbi.2025.104928_b0270","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1038\/s41440-022-01114-9","article-title":"The public health impact of hypertension and diabetes: a powerful tag team for the development of chronic kidney disease","volume":"46","author":"Kohagura","year":"2023","journal-title":"Hypertens. Res."},{"key":"10.1016\/j.jbi.2025.104928_b0275","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1053\/j.ajkd.2018.12.044","article-title":"Hypertension in CKD: core curriculum 2019","volume":"74","author":"Ku","year":"2019","journal-title":"Am. J. Kidney Dis."},{"key":"10.1016\/j.jbi.2025.104928_b0280","doi-asserted-by":"crossref","first-page":"1606","DOI":"10.1161\/CIR.0000000000001184","article-title":"Cardiovascular-kidney-metabolic health: a presidential advisory from the American heart association","volume":"148","author":"Ndumele","year":"2023","journal-title":"Circulation"},{"key":"10.1016\/j.jbi.2025.104928_b0285","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1053\/j.ackd.2015.08.004","article-title":"Structural and functional changes with the aging kidney","volume":"23","author":"Denic","year":"2016","journal-title":"Adv. Chronic Kidney Dis."},{"key":"10.1016\/j.jbi.2025.104928_b0290","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1681\/ASN.2005090986","article-title":"Mortality risk stratification in chronic kidney disease: one size for all Ages?","volume":"17","author":"O\u2019Hare","year":"2006","journal-title":"J. Am. Soc. Nephrol."},{"key":"10.1016\/j.jbi.2025.104928_b0295","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1053\/j.ajkd.2006.05.021","article-title":"Cardiovascular outcomes and all-cause mortality: exploring the interaction between CKD and cardiovascular disease","volume":"48","author":"Weiner","year":"2006","journal-title":"Am. J. Kidney Dis."},{"key":"10.1016\/j.jbi.2025.104928_b0300","doi-asserted-by":"crossref","unstructured":"R. A. Davis, K.-S. Lii, D. N. Politis, Remarks on Some Nonparametric Estimates of a Density Function, in: Selected Works of Murray Rosenblatt, Springer, 2011, pp. 95\u2013100. URL: https:\/\/link.springer.com\/chapter\/10.1007\/978-1-4419-8339-8_13. doi:10.1007\/978-1-4419-8339-8_13.","DOI":"10.1007\/978-1-4419-8339-8_13"},{"key":"10.1016\/j.jbi.2025.104928_b0305","unstructured":"Stats Stack Exchange Community, Why Does Kernel Density of Positive Data Display Negative Values?, 2020. URL: https:\/\/stats. stackexchange.com\/questions\/483203\/why-does-kernel-density-of-positive-data-display-negative-values, accessed: 2024-12-05."},{"key":"10.1016\/j.jbi.2025.104928_b0310","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1080\/01621459.1958.10501452","article-title":"Nonparametric Estimation from Incomplete Observations","volume":"53","author":"Kaplan","year":"1958","journal-title":"Journal of the American Statistical Association"},{"key":"10.1016\/j.jbi.2025.104928_b0315","unstructured":"G. Van Rossum, Python Tutorial, Technical Report, Centrum voor Wiskunde en Informatica (CWI), 1995. URL: https:\/\/ir.cwi.nl\/pub\/ 5007\/05007D.pdf."},{"key":"10.1016\/j.jbi.2025.104928_b0320","doi-asserted-by":"crossref","unstructured":"C. Davidson-Pilon, lifelines: Survival Analysis in Python, Journal of Open Source Software 4 (2019) 1317.","DOI":"10.21105\/joss.01317"},{"key":"10.1016\/j.jbi.2025.104928_b0325","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1162\/089976698300017197","article-title":"Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms","volume":"10","author":"Dietterich","year":"1998","journal-title":"Neural Computation"},{"key":"10.1016\/j.jbi.2025.104928_b0330","doi-asserted-by":"crossref","unstructured":"W. M. Campion, Book review: Multiple imputation for nonresponse in surveys, 1989.","DOI":"10.2307\/3172772"},{"key":"10.1016\/j.jbi.2025.104928_b0335","unstructured":"European Union, General Data Protection Regulation (GDPR) \u2013 Article 17: Right to Erasure (\u2019Right to be Forgotten\u2019), 2016. URL: https: \/\/gdpr-info.eu\/art-17-gdpr\/, Accessed: 17 March 2025."},{"key":"10.1016\/j.jbi.2025.104928_b0340","doi-asserted-by":"crossref","first-page":"125","DOI":"10.23889\/ijpds.v9i5.2609","article-title":"A UK specification for trusted research environments","volume":"9","author":"Sood","year":"2024","journal-title":"International Journal of Population Data Science"},{"key":"10.1016\/j.jbi.2025.104928_b0345","doi-asserted-by":"crossref","unstructured":"C. Dwork, Differential privacy, in: International colloquium on automata, languages, and programming, Springer, 2006, pp. 1\u201312.","DOI":"10.1007\/11787006_1"},{"key":"10.1016\/j.jbi.2025.104928_b0350","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1093\/aje\/kwv254","article-title":"Using big data to emulate a target trial when a randomized trial is not available","volume":"183","author":"Hern\u00e1n","year":"2016","journal-title":"American Journal of Epidemiology"},{"key":"10.1016\/j.jbi.2025.104928_b0355","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1111\/j.1467-6419.2007.00527.x","article-title":"Some practical guidance for the implementation of propensity score matching","volume":"22","author":"Caliendo","year":"2008","journal-title":"J. Econ. Surveys"},{"key":"10.1016\/j.jbi.2025.104928_b0360","doi-asserted-by":"crossref","unstructured":"M. Packer, Data sharing in medical research, 2018.","DOI":"10.1136\/bmj.k510"},{"key":"10.1016\/j.jbi.2025.104928_b0365","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-021-01602-x","article-title":"Privacy-preserving data sharing infrastructures for medical research: systematization and comparison","volume":"21","author":"Wirth","year":"2021","journal-title":"BMC Medical Informatics and Decision Making"},{"key":"10.1016\/j.jbi.2025.104928_b0370","doi-asserted-by":"crossref","first-page":"2446","DOI":"10.1001\/jama.2022.21383","article-title":"Target trial emulation: a framework for causal inference from observational data","volume":"328","author":"Hern\u00e1n","year":"2022","journal-title":"Jama"},{"key":"10.1016\/j.jbi.2025.104928_b0375","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.jacc.2021.11.035","article-title":"Antithrombotic therapy in patients with atrial fibrillation after acute coronary syndromes or percutaneous intervention","volume":"79","author":"Harskamp","year":"2022","journal-title":"J. Am. College Cardiol."}],"container-title":["Journal of Biomedical Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1532046425001571?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1532046425001571?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T11:33:24Z","timestamp":1780918404000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1532046425001571"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":75,"alternative-id":["S1532046425001571"],"URL":"https:\/\/doi.org\/10.1016\/j.jbi.2025.104928","relation":{},"ISSN":["1532-0464"],"issn-type":[{"value":"1532-0464","type":"print"}],"subject":[],"published":{"date-parts":[[2025,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Attention-based synthetic data generation for calibration-enhanced survival analysis: A case study for chronic kidney disease using electronic health records","name":"articletitle","label":"Article Title"},{"value":"Journal of Biomedical Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jbi.2025.104928","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Author(s). Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"104928"}}