{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T02:47:07Z","timestamp":1788403627002,"version":"build-2803163510"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T00:00:00Z","timestamp":1739491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T00:00:00Z","timestamp":1739491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation, USA","doi-asserted-by":"crossref","award":["SCH-2205289"],"award-info":[{"award-number":["SCH-2205289"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation, USA","doi-asserted-by":"crossref","award":["SCH-2205289"],"award-info":[{"award-number":["SCH-2205289"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Applying machine learning to clinical outcome prediction is challenging due to imbalanced datasets and sensitive tasks that contain rare yet critical outcomes and where equitable treatment across diverse patient groups is essential. Despite attempts, biases in predictions persist, driven by disparities in representation and exacerbated by the scarcity of positive labels, perpetuating health inequities. This paper introduces , a synthetic data generation approach leveraging large language models, to address these issues.  enhances algorithmic performance and reduces bias by creating realistic, anonymous synthetic patient data that improves representation and augments dataset patterns while preserving privacy. Through experiments on multiple datasets, we demonstrate that  boosts mortality prediction performance across diverse subgroups, achieving up to a 21% improvement in F1 Score without requiring additional data or altering downstream training pipelines. Furthermore,  consistently reduces subgroup performance gaps, as shown by universal improvements in performance and fairness metrics across four experimental setups.<\/jats:p>","DOI":"10.1038\/s41746-025-01438-z","type":"journal-article","created":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T12:31:42Z","timestamp":1739536302000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Improving medical machine learning models with generative balancing for equity and excellence"],"prefix":"10.1038","volume":"8","author":[{"given":"Brandon","family":"Theodorou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin","family":"Danek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Venkat","family":"Tummala","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shivam Pankaj","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bradley","family":"Malin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jimeng","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,14]]},"reference":[{"key":"1438_CR1","doi-asserted-by":"publisher","first-page":"299","DOI":"10.9745\/GHSP-D-16-00286","volume":"5","author":"CA Umeh","year":"2017","unstructured":"Umeh, C. A. & Feeley, F. G. Inequitable access to health care by the poor in community-based health insurance programs: a review of studies from low-and middle-income countries. Glob. Health Sci. Pract. 5, 299\u2013314 (2017).","journal-title":"Glob. Health Sci. Pract."},{"key":"1438_CR2","doi-asserted-by":"crossref","unstructured":"Turner, B. E., Steinberg, J. R., Weeks, B. T., Rodriguez, F. & Cullen, M. R. Race\/ethnicity reporting and representation in US clinical trials: a cohort study. Lancet Reg. Health Am. 11, 100252 (2022).","DOI":"10.1016\/j.lana.2022.100252"},{"key":"1438_CR3","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1177\/1077558703257169","volume":"60","author":"JA Macinko","year":"2003","unstructured":"Macinko, J. A., Shi, L., Starfield, B. & Wulu Jr, J. T. Income inequality and health: a critical review of the literature. Med. Care Res. Rev. 60, 407\u2013452 (2003).","journal-title":"Med. Care Res. Rev."},{"key":"1438_CR4","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1037\/cap0000272","volume":"62","author":"B Gibson","year":"2021","unstructured":"Gibson, B., Schneider, J., Talamonti, D. & Forshaw, M. The impact of inequality on mental health outcomes during the COVID-19 pandemic: a systematic review. Can. Psychol. 62, 101 (2021).","journal-title":"Can. Psychol."},{"key":"1438_CR5","doi-asserted-by":"crossref","unstructured":"Cai, L., Gao, J. & Zhao, D. A review of the application of deep learning in medical image classification and segmentation. Ann. Transl. Med. 8, 713 (2020).","DOI":"10.21037\/atm.2020.02.44"},{"key":"1438_CR6","doi-asserted-by":"crossref","unstructured":"Li, Q. et al. Medical image classification with convolutional neural network. In Proc. 13th International Conference on Control Automation Robotics & Vision (ICARCV) 844\u2013848 (IEEE, 2014).","DOI":"10.1109\/ICARCV.2014.7064414"},{"key":"1438_CR7","doi-asserted-by":"crossref","unstructured":"Esteva, A. et al. Deep learning-enabled medical computer vision. npj Digit. Med. 4, 5. https:\/\/www.nature.com\/articles\/s41746-020-00376-2 (2021).","DOI":"10.1038\/s41746-020-00376-2"},{"key":"1438_CR8","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1146\/annurev.bioeng.8.061505.095802","volume":"8","author":"P Sajda","year":"2006","unstructured":"Sajda, P. Machine learning for detection and diagnosis of disease. Annu. Rev. Biomed. Eng. 8, 537\u2013565 (2006).","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"1438_CR9","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/S0933-3657(01)00077-X","volume":"23","author":"I Kononenko","year":"2001","unstructured":"Kononenko, I. Machine learning for medical diagnosis: history, state of the art and perspective. Artif. Intell. Med. 23, 89\u2013109 (2001).","journal-title":"Artif. Intell. Med."},{"key":"1438_CR10","doi-asserted-by":"crossref","unstructured":"Wang, Z. & Sun, J. Survtrace: transformers for survival analysis with competing events. In Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 1\u20139 (Association for Computing Machinery, New York, NY, USA, 2022).","DOI":"10.1145\/3535508.3545521"},{"key":"1438_CR11","doi-asserted-by":"publisher","first-page":"846","DOI":"10.1513\/AnnalsATS.201710-787OC","volume":"15","author":"JC Rojas","year":"2018","unstructured":"Rojas, J. C. et al. Predicting intensive care unit readmission with machine learning using electronic health record data. Ann. Am. Thorac. Soc. 15, 846\u2013853 (2018).","journal-title":"Ann. Am. Thorac. Soc."},{"key":"1438_CR12","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1148\/radiol.2020192224","volume":"295","author":"MJ Willemink","year":"2020","unstructured":"Willemink, M. J. et al. Preparing medical imaging data for machine learning. Radiology 295, 4\u201315 (2020).","journal-title":"Radiology"},{"key":"1438_CR13","doi-asserted-by":"publisher","first-page":"2970","DOI":"10.3390\/math9222970","volume":"9","author":"AM Rahmani","year":"2021","unstructured":"Rahmani, A. M. et al. Machine learning (ML) in medicine: review, applications, and challenges. Mathematics 9, 2970 (2021).","journal-title":"Mathematics"},{"key":"1438_CR14","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01076-x","volume":"7","author":"J-N Eckardt","year":"2024","unstructured":"Eckardt, J.-N. et al. Mimicking clinical trials with synthetic acute myeloid leukemia patients using generative artificial intelligence. npj Digit. Med. 7, 76 (2024).","journal-title":"npj Digit. Med."},{"key":"1438_CR15","unstructured":"Farahani, A., Voghoei, S., Rasheed, K. & Arabnia, H. R. A brief review of domain adaptation. http:\/\/arxiv.org\/abs\/2010.03978 (2020)."},{"key":"1438_CR16","unstructured":"Wang, J. et al. Generalizing to unseen domains: a survey on domain generalization. http:\/\/arxiv.org\/abs\/2103.03097 (2022)."},{"key":"1438_CR17","unstructured":"Schrouff, J. et al. Diagnosing failures of fairness transfer across distribution shift in real-world medical settings. http:\/\/arxiv.org\/abs\/2202.01034 (2023)."},{"key":"1438_CR18","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1038\/s41591-021-01614-0","volume":"28","author":"P Rajpurkar","year":"2022","unstructured":"Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. AI in health and medicine. Nat. Med. 28, 31\u201338 (2022).","journal-title":"Nat. Med."},{"key":"1438_CR19","doi-asserted-by":"crossref","unstructured":"Norori, N., Hu, Q., Aellen, F. M., Faraci, F. D. & Tzovara, A. Addressing bias in big data and AI for health care: a call for open science. Patterns 2, 100347 (2021).","DOI":"10.1016\/j.patter.2021.100347"},{"key":"1438_CR20","doi-asserted-by":"publisher","first-page":"753","DOI":"10.4314\/ahs.v23i2.86","volume":"23","author":"L Kamulegeya","year":"2023","unstructured":"Kamulegeya, L. et al. Using artificial intelligence on dermatology conditions in Uganda: a case for diversity in training data sets for machine learning. Afr. Health Sci. 23, 753\u201363 (2023).","journal-title":"Afr. Health Sci."},{"key":"1438_CR21","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1002\/clc.21972","volume":"35","author":"NK Wenger","year":"2012","unstructured":"Wenger, N. K. Cardiovascular disease: the female heart is vulnerable: a call to action from the 10Q report. Clin. Cardiol. 35, 134\u2013135 (2012).","journal-title":"Clin. Cardiol."},{"key":"1438_CR22","doi-asserted-by":"publisher","first-page":"3529","DOI":"10.1093\/eurheartj\/ehz592","volume":"40","author":"EK Oikonomou","year":"2019","unstructured":"Oikonomou, E. K. et al. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary ct angiography. Eur. Heart J. 40, 3529\u20133543 (2019).","journal-title":"Eur. Heart J."},{"key":"1438_CR23","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-021-01110-7","volume":"9","author":"E R\u00f6\u00f6sli","year":"2022","unstructured":"R\u00f6\u00f6sli, E., Bozkurt, S. & Hernandez-Boussard, T. Peeking into a black box, the fairness and generalizability of a mimic-iii benchmarking model. Sci. Data 9, 24 (2022).","journal-title":"Sci. Data"},{"key":"1438_CR24","doi-asserted-by":"publisher","unstructured":"Cock, J. M., Bilal, M., Davis, R., Marras, M. & Kaser, T. Protected attributes tell us who, behavior tells us how: a comparison of demographic and behavioral oversampling for fair student success modeling. In LAK23: 13th International Learning Analytics and Knowledge Conference. (ACM, New York, NY, USA, 2023). https:\/\/doi.org\/10.1145\/3576050.3576149.","DOI":"10.1145\/3576050.3576149"},{"key":"1438_CR25","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.neucom.2014.08.091","volume":"163","author":"F Charte","year":"2015","unstructured":"Charte, F., Rivera, A. J., del Jesus, M. J. & Herrera, F. Addressing imbalance in multilabel classification: measures and random resampling algorithms. Neurocomputing 163, 3\u201316 (2015).","journal-title":"Neurocomputing"},{"key":"1438_CR26","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N. V., Bowyer, K. W., Hall, L. O. & Kegelmeyer, W. P. Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002).","journal-title":"J. Artif. Intell. Res."},{"key":"1438_CR27","unstructured":"Behal, K., Chen, J., Fikes, C. & Xiao, S. Mcrage: synthetic healthcare data for fairness. arXiv preprint arXiv:2310.18430 (2023)."},{"key":"1438_CR28","doi-asserted-by":"crossref","unstructured":"Juwara, L., El-Hussuna, A. & El Emam, K. An evaluation of synthetic data augmentation for mitigating covariate bias in health data. Patterns 5, 100946 (2024).","DOI":"10.1016\/j.patter.2024.100946"},{"key":"1438_CR29","doi-asserted-by":"publisher","first-page":"3738","DOI":"10.1016\/j.patcog.2012.03.014","volume":"45","author":"MA Tahir","year":"2012","unstructured":"Tahir, M. A., Kittler, J. & Yan, F. Inverse random under sampling for class imbalance problem and its application to multi-label classification. Pattern Recognit. 45, 3738\u20133750 (2012).","journal-title":"Pattern Recognit."},{"key":"1438_CR30","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1162\/evco.2009.17.3.275","volume":"17","author":"S Garc\u00eda","year":"2009","unstructured":"Garc\u00eda, S. & Herrera, F. Evolutionary undersampling for classification with imbalanced datasets: proposals and taxonomy. Evolut. Comput. 17, 275\u2013306 (2009).","journal-title":"Evolut. Comput."},{"key":"1438_CR31","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1377\/hlthaff.2021.01287","volume":"41","author":"SS Gervasi","year":"2022","unstructured":"Gervasi, S. S. et al. The potential for bias in machine learning and opportunities for health insurers to address it: article examines the potential for bias in machine learning and opportunities for health insurers to address it. Health Aff. 41, 212\u2013218 (2022).","journal-title":"Health Aff."},{"key":"1438_CR32","unstructured":"Zafar, M. B., Valera, I., Rogriguez, M. G. & Gummadi, K. P. Fairness constraints: mechanisms for fair classification. In Artificial Intelligence and Statistics 962\u2013970 (PMLR, 2017)."},{"key":"1438_CR33","unstructured":"Berk, R. et al. A convex framework for fair regression. arXiv preprint arXiv:1706.02409 (2017)."},{"key":"1438_CR34","doi-asserted-by":"crossref","unstructured":"Kamishima, T., Akaho, S., Asoh, H. & Sakuma, J. Fairness-aware classifier with prejudice remover regularizer. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases 35\u201350 (Springer, 2012).","DOI":"10.1007\/978-3-642-33486-3_3"},{"key":"1438_CR35","doi-asserted-by":"publisher","unstructured":"Yongkai, W., Zhang, L. & Wu, X. On Convexity and Bounds of Fairness-Aware Classification. In The World Wide Web Conference. (ACM, New York, NY, USA, 2019). https:\/\/doi.org\/10.1145\/3308558.3313723.","DOI":"10.1145\/3308558.3313723"},{"key":"1438_CR36","doi-asserted-by":"publisher","unstructured":"Zhang, B. H., Lemoine, B. & Mitchell, M. Mitigating unwanted biases with adversarial learning. In Proceedings of the 2018 AAAI\/ACM Conference on AI, Ethics, and Society. (ACM, New York, NY, USA, 2018). https:\/\/doi.org\/10.1145\/3278721.3278779.","DOI":"10.1145\/3278721.3278779"},{"key":"1438_CR37","doi-asserted-by":"crossref","unstructured":"Theodorou, B., Glass, L., Xiao, C. & Sun, J. FRAMM: fair ranking with missing modalities for clinical trial site selection. Patterns 5, 100944 (2024).","DOI":"10.1016\/j.patter.2024.100944"},{"key":"1438_CR38","unstructured":"Dutta, S. et al. Is there a trade-off between fairness and accuracy? a perspective using mismatched hypothesis testing. In International conference on machine learning 2803\u20132813 (PMLR, 2020)."},{"key":"1438_CR39","doi-asserted-by":"publisher","unstructured":"Wang, Y. et al. Understanding and improving fairness-accuracy trade-offs in multi-task learning. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. (ACM, New York, NY, USA, 2021). https:\/\/doi.org\/10.1145\/3447548.3467326.","DOI":"10.1145\/3447548.3467326"},{"key":"1438_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.178","volume":"5","author":"TJ Pollard","year":"2018","unstructured":"Pollard, T. J. et al. The eicu collaborative research database, a freely available multi-center database for critical care research. Sci. Data 5, 1\u201313 (2018).","journal-title":"Sci. Data"},{"key":"1438_CR41","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01899-x","volume":"10","author":"AE Johnson","year":"2023","unstructured":"Johnson, A. E. et al. Mimic-iv, a freely accessible electronic health record dataset. Sci. data 10, 1 (2023).","journal-title":"Sci. data"},{"key":"1438_CR42","unstructured":"Chouldechova, A. Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. http:\/\/arxiv.org\/abs\/1610.07524 (2016)."},{"key":"1438_CR43","doi-asserted-by":"crossref","unstructured":"Conceicao, P. N. & Ferreira, P. M. The Young Person\u2019s Guide to the Theil Index: suggesting intuitive interpretations and exploring analytical applications. SSRN Electron. J. http:\/\/www.ssrn.com\/abstract=228703 (2000).","DOI":"10.2139\/ssrn.228703"},{"key":"1438_CR44","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-41093-0","volume":"14","author":"B Theodorou","year":"2023","unstructured":"Theodorou, B., Xiao, C. & Sun, J. Synthesize high-dimensional longitudinal electronic health records via hierarchical autoregressive language model. Nat. Commun. 14, 5305 (2023).","journal-title":"Nat. Commun."},{"key":"1438_CR45","doi-asserted-by":"publisher","unstructured":"Jon, K., Mullainathan, S. & Raghavan, M. \u201cInherent Trade-Offs in the Fair Determination of Risk Scores.\u201d 8th Innovations in Theoretical Computer Science Conference (ITCS 2017). (Schloss Dagstuhl - Leibniz-Zentrum f\u00fcr Informatik, 2017). https:\/\/doi.org\/10.4230\/LIPICS.ITCS.2017.43.","DOI":"10.4230\/LIPICS.ITCS.2017.43"},{"key":"1438_CR46","doi-asserted-by":"crossref","unstructured":"Li, B. et al. Enhancing fairness in disease prediction by optimizing multiple domain adversarial networks. BioRxiv (2023).","DOI":"10.1101\/2023.08.04.551906"},{"key":"1438_CR47","doi-asserted-by":"crossref","unstructured":"Obermeyer, Z., Powers, B., Vogeli, C. & Mullainathan, S. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 447\u2013453 (2019).","DOI":"10.1126\/science.aax2342"},{"key":"1438_CR48","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1167\/tvst.10.2.13","volume":"10","author":"P Burlina","year":"2021","unstructured":"Burlina, P., Joshi, N., Paul, W., Pacheco, K. D. & Bressler, N. M. Addressing artificial intelligence bias in retinal diagnostics. Transl. Vis. Sci. Technol. 10, 13 (2021).","journal-title":"Transl. Vis. Sci. Technol."},{"key":"1438_CR49","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1162\/neco_a_01468","volume":"34","author":"W Paul","year":"2022","unstructured":"Paul, W., Hadzic, A., Joshi, N., Alajaji, F. & Burlina, P. TARA: training and representation alteration for AI fairness and domain generalization. Neural Comput. 34, 716\u2013753 (2022).","journal-title":"Neural Comput."},{"key":"1438_CR50","unstructured":"Chouldechova, A. & Roth, A. The frontiers of fairness in machine learning. arXiv preprint arXiv:1810.08810 (2018)."},{"key":"1438_CR51","doi-asserted-by":"crossref","unstructured":"Castelnovo, A. et al. The zoo of fairness metrics in machine learning (2021).","DOI":"10.21203\/rs.3.rs-1162350\/v1"},{"key":"1438_CR52","doi-asserted-by":"publisher","unstructured":"Beutel, A. et al. Putting fairness principles into practice: challenges, metrics, and improvements. In Proceedings of the 2019 AAAI\/ACM Conference on AI, Ethics, and Society. (ACM, New York, NY, USA, 2019). https:\/\/doi.org\/10.1145\/3306618.3314234.","DOI":"10.1145\/3306618.3314234"},{"key":"1438_CR53","doi-asserted-by":"crossref","unstructured":"Leben, D. Normative principles for evaluating fairness in machine learning. In Proceedings of the AAAI\/ACM Conference on AI, Ethics, and Society 86\u201392 (2020).","DOI":"10.1145\/3375627.3375808"},{"key":"1438_CR54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3457607","volume":"54","author":"N Mehrabi","year":"2021","unstructured":"Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K. & Galstyan, A. A survey on bias and fairness in machine learning. ACM Comput. Surv. 54, 1\u201335 (2021).","journal-title":"ACM Comput. Surv."},{"key":"1438_CR55","first-page":"2017","volume":"1","author":"S Barocas","year":"2017","unstructured":"Barocas, S., Hardt, M. & Narayanan, A. Fairness in machine learning. Nips Tutor. 1, 2017 (2017).","journal-title":"Nips Tutor."},{"key":"1438_CR56","doi-asserted-by":"crossref","unstructured":"Caton, S. & Haas, C. Fairness in machine learning: a survey. ACM Comput. Surv. 56, 1\u201338 (2024).","DOI":"10.1145\/3616865"},{"key":"1438_CR57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3494672","volume":"55","author":"D Pessach","year":"2022","unstructured":"Pessach, D. & Shmueli, E. A review on fairness in machine learning. ACM Comput. Surv. 55, 1\u201344 (2022).","journal-title":"ACM Comput. Surv."},{"key":"1438_CR58","doi-asserted-by":"publisher","unstructured":"Mehrabi, N., Morstatter, F., Peng, N. & Galstyan, A. Debiasing community detection: the importance of lowly connected nodes. In Proceedings of the 2019 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining. (ACM, New York, NY, USA, 2019). https:\/\/doi.org\/10.1145\/3341161.3342915.","DOI":"10.1145\/3341161.3342915"},{"key":"1438_CR59","unstructured":"Calmon, F., Wei, D., Vinzamuri, B., Natesan Ramamurthy, K. & Varshney, K. R. Optimized pre-processing for discrimination prevention. Advances in Neural Information Processing Systems 30 (2017)."},{"key":"1438_CR60","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10115-011-0463-8","volume":"33","author":"F Kamiran","year":"2012","unstructured":"Kamiran, F. & Calders, T. Data preprocessing techniques for classification without discrimination. Knowl. Inf. Syst. 33, 1\u201333 (2012).","journal-title":"Knowl. Inf. Syst."},{"key":"1438_CR61","unstructured":"Hardt, M., Price, E. & Srebro, N. Equality of opportunity in supervised learning. Advances in Neural Information Processing Systems 29 (2016)."},{"key":"1438_CR62","unstructured":"Pleiss, G., Raghavan, M., Wu, F., Kleinberg, J. & Weinberger, K. Q. On fairness and calibration. Advances in Neural Information Processing Systems 30 (2017)."},{"key":"1438_CR63","doi-asserted-by":"crossref","unstructured":"Yang, C. et al. Pyhealth: A deep learning toolkit for healthcare applications. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 5788\u20135789 (2023).","DOI":"10.1145\/3580305.3599178"}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01438-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01438-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01438-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T12:32:00Z","timestamp":1739536320000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01438-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,14]]},"references-count":63,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1438"],"URL":"https:\/\/doi.org\/10.1038\/s41746-025-01438-z","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-5252769\/v1","asserted-by":"object"}]},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,14]]},"assertion":[{"value":"12 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"100"}}