{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T01:21:56Z","timestamp":1773019316289,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T00:00:00Z","timestamp":1751414400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T00:00:00Z","timestamp":1751414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01EB031030"],"award-info":[{"award-number":["R01EB031030"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01EB031030"],"award-info":[{"award-number":["R01EB031030"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01EB031030"],"award-info":[{"award-number":["R01EB031030"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-04069-2","type":"journal-article","created":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T04:38:45Z","timestamp":1751431125000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["False-positive tolerant model misconduct mitigation in distributed federated learning on electronic health record data across clinical institutions"],"prefix":"10.1038","volume":"15","author":[{"given":"Maxim","family":"Edelson","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anh","family":"Pham","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tsung-Ting","family":"Kuo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,2]]},"reference":[{"key":"4069_CR1","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.csbj.2014.11.005","volume":"13","author":"K Kourou","year":"2015","unstructured":"Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V. & Fotiadis D. I. Machine learning applications in cancer prognosis and prediction. Comput. Struct. Biotechnol. J. 13, 8\u201317 (2015).","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"4069_CR2","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1186\/s13195-022-01047-y","volume":"14","author":"VS Diogo","year":"2022","unstructured":"Diogo, V. S., Ferreira, H. A., Prata, D. & Initiative, A. s. D. N. Early diagnosis of Alzheimer\u2019s disease using machine learning: a multi-diagnostic, generalizable approach. Alzheimers Res. Ther. 14, 107 (2022).","journal-title":"Alzheimers Res. Ther."},{"key":"4069_CR3","doi-asserted-by":"publisher","first-page":"2078","DOI":"10.1038\/s41467-021-22265-2","volume":"12","author":"A Eshaghi","year":"2021","unstructured":"Eshaghi, A. et al. Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data. Nat. Commun. 12, 2078 (2021).","journal-title":"Nat. Commun."},{"key":"4069_CR4","doi-asserted-by":"publisher","first-page":"147635","DOI":"10.1109\/ACCESS.2020.3016062","volume":"8","author":"W Wang","year":"2020","unstructured":"Wang, W., Lee, J., Harrou, F. & Sun, Y. Early detection of Parkinson\u2019s disease using deep learning and machine learning. IEEE Access. 8, 147635\u2013147646 (2020).","journal-title":"IEEE Access."},{"key":"4069_CR5","doi-asserted-by":"publisher","first-page":"ooac036","DOI":"10.1093\/jamiaopen\/ooac036","volume":"5","author":"M Edelson","year":"2022","unstructured":"Edelson, M. & Kuo, T. T. Generalizable prediction of COVID-19 mortality on worldwide patient data. JAMIA Open. 5, ooac036 (2022).","journal-title":"JAMIA Open."},{"key":"4069_CR6","doi-asserted-by":"crossref","unstructured":"Wu, X. et al. A novel centralized federated deep fuzzy neural network with multi-objectives neural architecture search for epistatic detection. IEEE Trans. Fuzzy Syst. (2024).","DOI":"10.1109\/TFUZZ.2024.3369944"},{"key":"4069_CR7","doi-asserted-by":"publisher","first-page":"e26","DOI":"10.1016\/j.ijmedinf.2010.10.001","volume":"80","author":"S Haas","year":"2011","unstructured":"Haas, S., Wohlgemuth, S., Echizen, I., Sonehara, N. & M\u00fcller, G. Aspects of privacy for electronic health records. Int. J. Med. Informatics. 80, e26\u2013e31 (2011).","journal-title":"Int. J. Med. Informatics"},{"key":"4069_CR8","doi-asserted-by":"publisher","first-page":"101","DOI":"10.3390\/math11010101","volume":"11","author":"W Deng","year":"2022","unstructured":"Deng, W., Huang, T. & Wang, H. A review of the key technology in a blockchain Building decentralized trust platform. Mathematics 11, 101 (2022).","journal-title":"Mathematics"},{"key":"4069_CR9","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1109\/TCBB.2022.3155774","volume":"20","author":"H Wang","year":"2022","unstructured":"Wang, H. & Wu, X. I. P. P. An intelligent privacy-preserving scheme for detecting interactions in genome association studies. IEEE\/ACM Trans. Comput. Biol. Bioinf. 20, 455\u2013464 (2022).","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"key":"4069_CR10","doi-asserted-by":"crossref","unstructured":"Drainakis, G., Katsaros, K. V., Pantazopoulos, P., Sourlas, V. & Amditis, A. in 2020 IEEE 19th International Symposium on Network Computing and Applications (NCA). 1\u20138 (IEEE).","DOI":"10.1109\/NCA51143.2020.9306745"},{"key":"4069_CR11","first-page":"346","volume":"31","author":"I Lee","year":"2022","unstructured":"Lee, I. An analysis of data breaches in the US healthcare industry: diversity, trends, and risk profiling. Inform. Secur. Journal: Global Perspective. 31, 346\u2013358 (2022).","journal-title":"Inform. Secur. Journal: Global Perspective"},{"key":"4069_CR12","doi-asserted-by":"publisher","first-page":"152076","DOI":"10.1109\/ACCESS.2019.2947261","volume":"7","author":"X Wu","year":"2019","unstructured":"Wu, X., Wang, H., Shi, M., Wang, A. & Xia, K. DNA motif finding method without protection can leak user privacy. IEEE Access. 7, 152076\u2013152087 (2019).","journal-title":"IEEE Access."},{"key":"4069_CR13","unstructured":"Nakamoto, S. & Bitcoin whitepaper URL: https:\/\/bitcoin.org\/bitcoin. pdf-(: 17.07. 2019) 9, 15 (2008)."},{"key":"4069_CR14","doi-asserted-by":"publisher","first-page":"762","DOI":"10.1109\/TSC.2018.2853167","volume":"12","author":"R Li","year":"2018","unstructured":"Li, R. et al. Blockchain for large-scale internet of things data storage and protection. IEEE Trans. Serv. Comput. 12, 762\u2013771 (2018).","journal-title":"IEEE Trans. Serv. Comput."},{"key":"4069_CR15","unstructured":"Kuo, T. T., Ohno-Machado, L. & Modelchain Decentralized privacy-preserving healthcare predictive modeling framework on private blockchain networks. Preprint at https:\/\/arXiv.org\/abs\/1802.01746 (2018)."},{"key":"4069_CR16","doi-asserted-by":"publisher","first-page":"6543","DOI":"10.1109\/TII.2020.2966069","volume":"16","author":"W Liang","year":"2020","unstructured":"Liang, W., Fan, Y., Li, K. C., Zhang, D. & Gaudiot, J. L. Secure data storage and recovery in industrial blockchain network environments. IEEE Trans. Industr. Inf. 16, 6543\u20136552 (2020).","journal-title":"IEEE Trans. Industr. Inf."},{"key":"4069_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-018-1144-x","volume":"43","author":"H Tian","year":"2019","unstructured":"Tian, H., He, J. & Ding, Y. Medical data management on blockchain with privacy. J. Med. Syst. 43, 1\u20136 (2019).","journal-title":"J. Med. Syst."},{"key":"4069_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-017-0110-7","volume":"5","author":"K Abouelmehdi","year":"2018","unstructured":"Abouelmehdi, K., Beni-Hessane, A. & Khaloufi, H. Big healthcare data: preserving security and privacy. J. Big Data. 5, 1\u201318 (2018).","journal-title":"J. Big Data"},{"key":"4069_CR19","doi-asserted-by":"publisher","first-page":"104658","DOI":"10.1016\/j.ijmedinf.2021.104658","volume":"158","author":"TT Kuo","year":"2022","unstructured":"Kuo, T. T. & Pham, A. Detecting model misconducts in decentralized healthcare federated learning. Int. J. Med. Informatics. 158, 104658 (2022).","journal-title":"Int. J. Med. Informatics"},{"key":"4069_CR20","unstructured":"Seh, A. H. et al. in Healthcare. 133 (MDPI)."},{"key":"4069_CR21","doi-asserted-by":"crossref","unstructured":"Al Kinoon, M., Omar, M., Mohaisen, M. & Mohaisen, D. in Computational Data and Social Networks: 10th International Conference, CSoNet Virtual Event,  Proceedings 10. 171\u2013183 (Springer, 2021).","DOI":"10.1007\/978-3-030-91434-9_16"},{"key":"4069_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-018-1123-2","volume":"43","author":"M Chernyshev","year":"2019","unstructured":"Chernyshev, M., Zeadally, S. & Baig, Z. Healthcare data breaches: implications for digital forensic readiness. J. Med. Syst. 43, 1\u201312 (2019).","journal-title":"J. Med. Syst."},{"key":"4069_CR23","first-page":"78","volume":"24","author":"MH Gabriel","year":"2018","unstructured":"Gabriel, M. H., Noblin, A., Rutherford, A., Walden, A. & Cortelyou-Ward, K. Data breach locations, types, and associated characteristics among US hospitals. Am. J. Manag Care. 24, 78\u201384 (2018).","journal-title":"Am. J. Manag Care"},{"key":"4069_CR24","doi-asserted-by":"publisher","first-page":"175","DOI":"10.3390\/appliedmath3010011","volume":"3","author":"W Raghupathi","year":"2023","unstructured":"Raghupathi, W., Raghupathi, V. & Saharia, A. Analyzing health data breaches: a visual analytics approach. AppliedMath 3, 175\u2013199 (2023).","journal-title":"AppliedMath"},{"key":"4069_CR25","unstructured":"Xiao, H., Xiao, H. & Eckert, C. In ECAI 2012 870\u2013875 (IOS, 2012)."},{"key":"4069_CR26","unstructured":"Steinhardt, J., Koh, P. W. W. & Liang, P. S. Certified defenses for data poisoning attacks. Advances Neural Inform. Process. Systems 30 (2017)."},{"key":"4069_CR27","unstructured":"Fang, M., Cao, X., Jia, J. & Gong, N. in 29th USENIX security symposium (USENIX Security 20). 1605\u20131622."},{"key":"4069_CR28","doi-asserted-by":"publisher","first-page":"16301","DOI":"10.1109\/JSEN.2021.3076767","volume":"21","author":"R Kumar","year":"2021","unstructured":"Kumar, R. et al. Blockchain-federated-learning and deep learning models for covid-19 detection using Ct imaging. IEEE Sens. J. 21, 16301\u201316314 (2021).","journal-title":"IEEE Sens. J."},{"key":"4069_CR29","unstructured":"Li, X. Improved Model Poisoning Attacks and Defenses in Federated Learning with Clustering (University of Waterloo, 2022)."},{"key":"4069_CR30","doi-asserted-by":"crossref","unstructured":"Tolpegin, V., Truex, S., Gursoy, M. E. & Liu, L. in Computer Security\u2013ESORICS 2020: 25th European Symposium on Research in Computer Security, ESORICS 2020, Guildford, UK, ,  25. 480\u2013501 (Springer, 2020).","DOI":"10.1007\/978-3-030-58951-6_24"},{"key":"4069_CR31","unstructured":"Cao, D., Chang, S., Lin, Z., Liu, G. & Sun, D. in IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS). 233\u2013239 (IEEE). (2019)."},{"key":"4069_CR32","doi-asserted-by":"crossref","unstructured":"Kalapaaking, A. P., Khalil, I. & Yi, X. Blockchain-based federated learning with SMPC model verification against poisoning attack for healthcare systems. IEEE Trans. Emerg. Top. Computing (2023).","DOI":"10.1109\/TETC.2023.3268186"},{"key":"4069_CR33","doi-asserted-by":"publisher","first-page":"1639","DOI":"10.1109\/TIFS.2022.3169918","volume":"17","author":"Z Ma","year":"2022","unstructured":"Ma, Z., Ma, J., Miao, Y., Li, Y. & Deng, R. H. ShieldFL: mitigating model poisoning attacks in privacy-preserving federated learning. IEEE Trans. Inf. Forensics Secur. 17, 1639\u20131654 (2022).","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"4069_CR34","unstructured":"Baracaldo, N., Chen, B., Ludwig, H. & Safavi, J. A. in Proceedings of the 10th ACM workshop on artificial intelligence and security. 103\u2013110."},{"key":"4069_CR35","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1109\/MNET.011.2000263","volume":"35","author":"Y Li","year":"2020","unstructured":"Li, Y. et al. A blockchain-based decentralized federated learning framework with committee consensus. IEEE Netw. 35, 234\u2013241 (2020).","journal-title":"IEEE Netw."},{"key":"4069_CR36","doi-asserted-by":"publisher","first-page":"1513","DOI":"10.1109\/TPDS.2020.3044223","volume":"32","author":"M Shayan","year":"2020","unstructured":"Shayan, M., Fung, C., Yoon, C. J., Beschastnikh, I. & Biscotti A blockchain system for private and secure federated learning. IEEE Trans. Parallel Distrib. Syst. 32, 1513\u20131525 (2020).","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"4069_CR37","doi-asserted-by":"publisher","first-page":"103694","DOI":"10.1016\/j.csi.2022.103694","volume":"84","author":"H Wang","year":"2023","unstructured":"Wang, H., Zhang, X., Xia, Y. & Wu, X. An intelligent blockchain-based access control framework with federated learning for genome-wide association studies. Comput. Stand. Interfaces. 84, 103694 (2023).","journal-title":"Comput. Stand. Interfaces"},{"key":"4069_CR38","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1093\/jamia\/ocy180","volume":"26","author":"TT Kuo","year":"2019","unstructured":"Kuo, T. T., Gabriel, R. A. & Ohno-Machado, L. Fair compute loads enabled by blockchain: sharing models by alternating client and server roles. J. Am. Med. Inform. Assoc. 26, 392\u2013403 (2019).","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"4069_CR39","unstructured":"Zou, K. H., Liu, A., Bandos, A. I., Ohno-Machado, L. & Rockette, H. E. Statistical Evaluation of Diagnostic Performance: Topics in ROC Analysis (CRC, 2011)."},{"key":"4069_CR40","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1093\/oxfordjournals.eurheartj.a015035","volume":"17","author":"R Kennedy","year":"1996","unstructured":"Kennedy, R., Fraser, H., McStay, L. & Harrison, R. Early diagnosis of acute myocardial infarction using clinical and electrocardiographic data at presentation: derivation and evaluation of logistic regression models. Eur. Heart J. 17, 1181\u20131191 (1996).","journal-title":"Eur. Heart J."},{"key":"4069_CR41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-019-1002-x","volume":"20","author":"D Chicco","year":"2020","unstructured":"Chicco, D. & Jurman, G. Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Med. Inf. Decis. Mak. 20, 1\u201316 (2020).","journal-title":"BMC Med. Inf. Decis. Mak."},{"key":"4069_CR42","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011).","journal-title":"J. Mach. Learn. Res."},{"key":"4069_CR43","doi-asserted-by":"publisher","first-page":"104924","DOI":"10.1016\/j.ijmedinf.2022.104924","volume":"169","author":"TT Kuo","year":"2023","unstructured":"Kuo, T. T. & Pham, A. Quorum-based model learning on a blockchain hierarchical clinical research network using smart contracts. Int. J. Med. Informatics. 169, 104924 (2023).","journal-title":"Int. J. Med. Informatics"},{"key":"4069_CR44","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1093\/jamia\/ocz214","volume":"27","author":"TT Kuo","year":"2020","unstructured":"Kuo, T. T., Kim, J. & Gabriel, R. A. Privacy-preserving model learning on a blockchain network-of-networks. J. Am. Med. Inform. Assoc. 27, 343\u2013354 (2020).","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"4069_CR45","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1093\/jamiaopen\/ooaa017","volume":"3","author":"TT Kuo","year":"2020","unstructured":"Kuo, T. T. The anatomy of a distributed predictive modeling framework: online learning, blockchain network, and consensus algorithm. JAMIA Open. 3, 201\u2013208. https:\/\/doi.org\/10.1093\/jamiaopen\/ooaa017 (2020).","journal-title":"JAMIA Open."},{"key":"4069_CR46","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1093\/jamia\/ocaa023","volume":"27","author":"TT Kuo","year":"2020","unstructured":"Kuo, T. T., Gabriel, R. A., Cidambi, K. R. & Ohno-Machado, L. EXpectation propagation logistic regression on permissioned blockchain (ExplorerChain): decentralized online healthcare\/genomics predictive model learning. J. Am. Med. Inform. Assoc. 27, 747\u2013756. https:\/\/doi.org\/10.1093\/jamia\/ocaa023 (2020).","journal-title":"J. Am. Med. Inform. Assoc."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-04069-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-04069-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-04069-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T04:38:52Z","timestamp":1751431132000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-04069-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,2]]},"references-count":46,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["4069"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-04069-2","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,2]]},"assertion":[{"value":"26 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"23310"}}