{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T15:59:02Z","timestamp":1785340742519,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":24,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Fondazione Cariplo","award":["2024-0173"],"award-info":[{"award-number":["2024-0173"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,6,30]]},"DOI":"10.1145\/3807503.3819459","type":"proceedings-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T02:55:27Z","timestamp":1785293727000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Assessing Subgroup Fairness in Clinical Missing Data Imputation: A Case Study Using MIMIC-IV"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9651-7156","authenticated-orcid":false,"given":"Aldo","family":"Marzullo","sequence":"first","affiliation":[{"name":"Department of Electronics Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0216-6402","authenticated-orcid":false,"given":"Abdelrahman Ali Mohamed","family":"Dafalla","sequence":"additional","affiliation":[{"name":"Department of Electronics Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8819-2734","authenticated-orcid":false,"given":"Elena","family":"De Momi","sequence":"additional","affiliation":[{"name":"Department of Electronics Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,28]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1142\/9789813207813_0021"},{"key":"e_1_3_3_1_3_2","unstructured":"Cynthia Dwork Moritz Hardt Toniann Pitassi Omer Reingold and Rich Zemel. 2011. Fairness Through Awareness. arxiv:https:\/\/arXiv.org\/abs\/1104.3913\u00a0[cs.CC] https:\/\/arxiv.org\/abs\/1104.3913"},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"crossref","unstructured":"Aya El\u00a0Mir Eric\u00a0Bezerra de Sousa Ignacio Mesina-Estarr\u00f3n Leo\u00a0Anthony Celi Moad Hani Mohammed Benjelloun Neha Nageswaran Sa\u00efd Mahmoudi Shaheen Siddiqui Sreeram Sadasivam et\u00a0al. 2026. Moving beyond the empty cell: The threat of decontextualized healthcare data. PLOS Digital Health 5 1 (2026) e0001194.","DOI":"10.1371\/journal.pdig.0001194"},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"publisher","unstructured":"Camila Guerreiro F\u00e1tima Leal and Micaela Pinho. 2025. Synthetic Data Generation for Binary and Multi-Class Classification in the Health Domain. Information 16 11 (Nov. 2025) 986. 10.3390\/info16110986","DOI":"10.3390\/info16110986"},{"key":"e_1_3_3_1_6_2","unstructured":"Moritz Hardt Eric Price and Nathan Srebro. 2016. Equality of Opportunity in Supervised Learning. arxiv:https:\/\/arXiv.org\/abs\/1610.02413\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/1610.02413"},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"publisher","unstructured":"PhD Irene Y.\u00a0Chen Peter\u00a0Szolovits and PhD Marzyeh\u00a0Ghassemi. 2019. Can AI Help Reduce Disparities in General Medical and Mental Health Care? AMA Journal of Ethics 21 2 (Feb. 2019) E167\u2013179. 10.1001\/amajethics.2019.167","DOI":"10.1001\/amajethics.2019.167"},{"key":"e_1_3_3_1_8_2","unstructured":"Daniel Jarrett Bogdan Cebere Tennison Liu Alicia Curth and Mihaela van\u00a0der Schaar. 2022. HyperImpute: Generalized Iterative Imputation with Automatic Model Selection. arxiv:https:\/\/arXiv.org\/abs\/2206.07769\u00a0[stat.ML] https:\/\/arxiv.org\/abs\/2206.07769"},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"publisher","unstructured":"Alistair E.\u00a0W. Johnson Lucas Bulgarelli Lu Shen Alvin Gayles Ayad Shammout Steven Horng Tom\u00a0J. Pollard Sicheng Hao Benjamin Moody Brian Gow Li-wei\u00a0H. Lehman Leo\u00a0A. Celi and Roger\u00a0G. Mark. 2023. MIMIC-IV a freely accessible electronic health record dataset. Sci Data 10 1 (Jan. 2023) 1. 10.1038\/s41597-022-01899-x","DOI":"10.1038\/s41597-022-01899-x"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"publisher","unstructured":"Justin Kauffman Riccardo Miotto Eyal Klang Anthony Costa Beau Norgeot Marinka Zitnik Shameer Khader Fei Wang Girish\u00a0N. Nadkarni and Benjamin\u00a0S. Glicksberg. 2025. Embedding Methods for Electronic Health Record Research. Annual Review of Biomedical Data Science 8 Volume 8 2025 (2025) 563\u2013590. 10.1146\/annurev-biodatasci-103123-094729","DOI":"10.1146\/annurev-biodatasci-103123-094729"},{"key":"e_1_3_3_1_11_2","unstructured":"Zachary\u00a0C. Lipton David\u00a0C. Kale and Randall Wetzel. 2016. Modeling Missing Data in Clinical Time Series with RNNs. arxiv:https:\/\/arXiv.org\/abs\/1606.04130\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/1606.04130"},{"key":"e_1_3_3_1_12_2","volume-title":"Statistical analysis with missing data","author":"Little Roderick\u00a0JA","year":"2019","unstructured":"Roderick\u00a0JA Little and Donald\u00a0B Rubin. 2019. Statistical analysis with missing data. John Wiley & Sons."},{"key":"e_1_3_3_1_13_2","unstructured":"Rahul Mazumder Trevor Hastie and Robert Tibshirani. 2010. Spectral Regularization Algorithms for Learning Large Incomplete Matrices. Journal of Machine Learning Research 11 80 (2010) 2287\u20132322. http:\/\/jmlr.org\/papers\/v11\/mazumder10a.html"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"publisher","unstructured":"Stephen\u00a0R. Pfohl Agata Foryciarz and Nigam\u00a0H. Shah. 2021. An empirical characterization of fair machine learning for clinical risk prediction. Journal of Biomedical Informatics 113 (2021) 103621. 10.1016\/j.jbi.2020.103621","DOI":"10.1016\/j.jbi.2020.103621"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"publisher","unstructured":"Lisa Pilgram Samer El\u00a0Kababji Dan Liu and Khaled El\u00a0Emam. 2025. Should we synthesize more than we need: impact of synthetic data generation for high-dimensional cross-sectional medical data. Journal of the American Medical Informatics Association 32 12 (Dec. 2025) 1843\u20131854. 10.1093\/jamia\/ocaf169","DOI":"10.1093\/jamia\/ocaf169"},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"publisher","unstructured":"Mohamed\u00a0Ashik Shahul\u00a0Hameed Asifa\u00a0Mehmood Qureshi and Abhishek Kaushik. 2024. Bias Mitigation via Synthetic Data Generation: A Review. Electronics 13 19 (Oct. 2024) 3909. 10.3390\/electronics13193909","DOI":"10.3390\/electronics13193909"},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"publisher","unstructured":"Daniel\u00a0J. Stekhoven and Peter B\u00fchlmann. 2011. MissForest\u2014non-parametric missing value imputation for mixed-type data. Bioinformatics 28 1 (10 2011) 112\u2013118. arXiv:https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/28\/1\/112\/50568519\/bioinformatics_28_1_112.pdf10.1093\/bioinformatics\/btr597","DOI":"10.1093\/bioinformatics\/btr597"},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"crossref","unstructured":"Yige Sun Jing Li Yifan Xu Tingting Zhang and Xiaofeng Wang. 2023. Deep learning versus conventional methods for missing data imputation: A review and comparative study. Expert Systems with Applications 227 (2023) 120201.","DOI":"10.1016\/j.eswa.2023.120201"},{"key":"e_1_3_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-71050-9"},{"key":"e_1_3_3_1_20_2","doi-asserted-by":"publisher","unstructured":"Xiao Xiang David Restrepo Hyewon Jeong Yugang Jia and Leo\u00a0Anthony Celi. 2026. Learning Representations from Incomplete EHR Data with Dual-Masked Autoencoding. 10.48550\/ARXIV.2602.15159Version Number: 1.","DOI":"10.48550\/ARXIV.2602.15159"},{"key":"e_1_3_3_1_21_2","doi-asserted-by":"publisher","unstructured":"Lei Xu Maria Skoularidou Alfredo Cuesta-Infante and Kalyan Veeramachaneni. 2019. Modeling Tabular data using Conditional GAN. 10.48550\/ARXIV.1907.00503Version Number: 2.","DOI":"10.48550\/ARXIV.1907.00503"},{"key":"e_1_3_3_1_22_2","unstructured":"Jinsung Yoon James Jordon and Mihaela van\u00a0der Schaar. 2018. GAIN: Missing Data Imputation using Generative Adversarial Nets. arxiv:https:\/\/arXiv.org\/abs\/1806.02920\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/1806.02920"},{"key":"e_1_3_3_1_23_2","unstructured":"Yiliang Zhang and Qi Long. 2021. Assessing fairness in the presence of missing data. Advances in neural information processing systems 34 (2021) 16007\u201316019."},{"key":"e_1_3_3_1_24_2","doi-asserted-by":"publisher","unstructured":"Yiliang Zhang and Qi Long. 2021. Fairness in Missing Data Imputation. 10.48550\/ARXIV.2110.12002Version Number: 1.","DOI":"10.48550\/ARXIV.2110.12002"},{"key":"e_1_3_3_1_25_2","volume-title":"Workshop on Trustworthy and Socially Responsible Machine Learning, NeurIPS 2022","author":"Zhang Yiliang","year":"2022","unstructured":"Yiliang Zhang and Qi Long. 2022. Fairness-aware missing data imputation. In Workshop on Trustworthy and Socially Responsible Machine Learning, NeurIPS 2022."}],"event":{"name":"BCB '26: 17th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","location":"Rende (CS) Italy","acronym":"BCB '26","sponsor":["SIGBio ACM Special Interest Group on Bioinformatics"]},"container-title":["Proceedings of the 17th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3807503.3819459","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T15:11:35Z","timestamp":1785337895000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3807503.3819459"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,30]]},"references-count":24,"alternative-id":["10.1145\/3807503.3819459","10.1145\/3807503"],"URL":"https:\/\/doi.org\/10.1145\/3807503.3819459","relation":{},"subject":[],"published":{"date-parts":[[2026,6,30]]},"assertion":[{"value":"2026-07-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}