{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T21:02:47Z","timestamp":1777496567146,"version":"3.51.4"},"publisher-location":"Cham","reference-count":50,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031781971","type":"print"},{"value":"9783031781988","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-78198-8_6","type":"book-chapter","created":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T09:05:58Z","timestamp":1733216758000},"page":"75-89","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Privacy-Preserving Tabular Data Generation: Application to Sepsis Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8275-5294","authenticated-orcid":false,"given":"Eric","family":"Macias-Fassio","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7268-4785","authenticated-orcid":false,"given":"Aythami","family":"Morales","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8565-4507","authenticated-orcid":false,"given":"Cristina","family":"Pruenza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6343-5656","authenticated-orcid":false,"given":"Julian","family":"Fierrez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,4]]},"reference":[{"issue":"2","key":"6_CR1","doi-asserted-by":"publisher","DOI":"10.2196\/41003","volume":"7","author":"A Acien","year":"2022","unstructured":"Acien, A., Morales, A., Vera-Rodriguez, R., Fierrez, J., Mondesire-Crump, I., Arroyo-Gallego, T., et al.: Detection of mental fatigue in the general population: Feasibility study of keystroke dynamics as a real-world biomarker. JMIR Biomedical Engineering 7(2), e41003 (2022)","journal-title":"JMIR Biomedical Engineering"},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: International Conference on Knowledge Discovery and Data Mining (2019)","DOI":"10.1145\/3292500.3330701"},{"issue":"7","key":"6_CR3","doi-asserted-by":"publisher","first-page":"1276","DOI":"10.3390\/medicina59071276","volume":"59","author":"A Alanazi","year":"2023","unstructured":"Alanazi, A., Aldakhil, L., Aldhoayan, M., Aldosari, B.: Machine learning for early prediction of sepsis in Intensive Care Unit (ICU) patients. Medicina 59(7), 1276 (2023)","journal-title":"Medicina"},{"key":"6_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2023.104688","volume":"135","author":"F Boutros","year":"2023","unstructured":"Boutros, F., Struc, V., Fierrez, J., Damer, N.: Synthetic data for face recognition: Current state and future prospects. Image Vis. Comput. 135, 104688 (2023)","journal-title":"Image Vis. Comput."},{"key":"6_CR5","unstructured":"Busch, C., et\u00a0al.: Privacy and Security Matters in Biometric Technologies. Springer (2024)"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Camacho-Cogollo, J.E., Bonet, I., Gil, B., Iadanza, E.: Machine learning models for early prediction of sepsis on large healthcare datasets. Electronics 11(9) (2022)","DOI":"10.3390\/electronics11091507"},{"key":"6_CR7","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. Journal of Artificial Intelligence Research 16, 321\u2013357 (2002)","journal-title":"Journal of Artificial Intelligence Research"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Che, Z., Cheng, Y., Zhai, S., Sun, Z., Liu, Y.: Boosting deep learning risk prediction with generative adversarial networks for electronic health records. In: IEEE International Conference on Data Mining (ICDM). pp. 787\u2013792 (2017)","DOI":"10.1109\/ICDM.2017.93"},{"key":"6_CR9","unstructured":"Choi, E., Biswal, S., Malin, B., Duke, J., Stewart, W.F., Sun, J.: Generating multi-label discrete patient records using generative adversarial networks. In: Machine learning for healthcare conference. pp. 286\u2013305 (2017)"},{"key":"6_CR10","doi-asserted-by":"crossref","unstructured":"Chong, K.M.: Privacy-preserving healthcare informatics: A review. In: Web of Conferences. vol.\u00a036, p. 04005 (2021)","DOI":"10.1051\/itmconf\/20213604005"},{"issue":"4","key":"6_CR11","doi-asserted-by":"publisher","first-page":"274","DOI":"10.3109\/07853890009011772","volume":"32","author":"TW Du Clos","year":"2000","unstructured":"Du Clos, T.W.: Function of c-reactive protein. Ann. Med. 32(4), 274\u2013278 (2000)","journal-title":"Ann. Med."},{"issue":"6","key":"6_CR12","first-page":"1","volume":"19","author":"K El Emam","year":"2019","unstructured":"El Emam, K., Hoptroff, R.: The synthetic data paradigm for using and sharing data. Cutter Executive Update 19(6), 1\u201312 (2019)","journal-title":"Cutter Executive Update"},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"Fierrez-Aguilar, J., Ortega-Garcia, J., Gonzalez-Rodriguez, J.: Target dependent score normalization techniques and their application to signature verification. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 35(3), 418\u2013425 (2005)","DOI":"10.1109\/TSMCC.2005.848181"},{"issue":"11","key":"6_CR14","doi-asserted-by":"publisher","first-page":"1485","DOI":"10.1097\/CCM.0000000000003891","volume":"47","author":"HM Giannini","year":"2019","unstructured":"Giannini, H.M., Ginestra, J.C., Chivers, C., Draugelis, M., Hanish, A., Schweickert, W.D., Fuchs, B.D., Meadows, L., Lynch, M., Donnelly, P.J., et al.: A machine learning algorithm to predict severe sepsis and septic shock: Development, implementation and impact on clinical practice. Crit. Care Med. 47(11), 1485 (2019)","journal-title":"Crit. Care Med."},{"issue":"2","key":"6_CR15","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0281248","volume":"18","author":"LF Gomez","year":"2023","unstructured":"Gomez, L.F., Morales, A., Fierrez, J., Orozco-Arroyave, J.R.: Exploring facial expressions and action unit domains for Parkinson detection. PLoS ONE 18(2), e0281248 (2023)","journal-title":"PLoS ONE"},{"key":"6_CR16","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Nets. Advances in Neural Information Processing Systems 27 (2014)"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Guan, J., Li, R., Yu, S., Zhang, X.: Generation of synthetic electronic medical record text. In: IEEE International Conference on Bioinformatics and Biomedicine. pp. 374\u2013380 (2018)","DOI":"10.1109\/BIBM.2018.8621223"},{"key":"6_CR18","unstructured":"Hafen, B.B., Sharma, S.: Oxygen saturation. StatPearls Publishing (2018)"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Han, C., Hayashi, H., Rundo, L., Araki, R., Shimoda, W., Muramatsu, S., Furukawa, Y., Mauri, G., Nakayama, H.: GAN-based synthetic brain MR image generation. In: IEEE International Symposium on Biomedical Imaging. pp. 734\u2013738 (2018)","DOI":"10.1109\/ISBI.2018.8363678"},{"issue":"12","key":"6_CR20","doi-asserted-by":"publisher","first-page":"441","DOI":"10.3390\/biology9120441","volume":"9","author":"D Hazra","year":"2020","unstructured":"Hazra, D., Byun, Y.C.: SynSigGAN: Generative Adversarial Networks for synthetic biomedical signal generation. Biology 9(12), 441 (2020)","journal-title":"Biology"},{"key":"6_CR21","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1055\/s-0042-1760247","volume":"62","author":"M Hernadez","year":"2023","unstructured":"Hernadez, M., Epelde, G., Alberdi, A., Cilla, R., Rankin, D.: Synthetic tabular data evaluation in the health domain covering resemblance, utility, and privacy dimensions. Methods Inf. Med. 62, 19\u201338 (2023)","journal-title":"Methods Inf. Med."},{"issue":"4","key":"6_CR22","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0174708","volume":"12","author":"S Horng","year":"2017","unstructured":"Horng, S., Sontag, D.A., Halpern, Y., Jernite, Y., Shapiro, N.I., Nathanson, L.A.: Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. PLoS ONE 12(4), e0174708 (2017)","journal-title":"PLoS ONE"},{"key":"6_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cmpb.2018.12.027","volume":"170","author":"MM Islam","year":"2019","unstructured":"Islam, M.M., Nasrin, T., Walther, B.A., Wu, C.C., Yang, H.C., Li, Y.C.: Prediction of sepsis patients using machine learning approach: a meta-analysis. Comput. Methods Programs Biomed. 170, 1\u20139 (2019)","journal-title":"Comput. Methods Programs Biomed."},{"key":"6_CR24","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.ejim.2019.10.025","volume":"72","author":"K Kashani","year":"2020","unstructured":"Kashani, K., Rosner, M.H., Ostermann, M.: Creatinine: from physiology to clinical application. Eur. J. Intern. Med. 72, 9\u201314 (2020)","journal-title":"Eur. J. Intern. Med."},{"key":"6_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.iccn.2021.103035","volume":"65","author":"SL Kausch","year":"2021","unstructured":"Kausch, S.L., Moorman, J.R., Lake, D.E., Keim-Malpass, J.: Physiological machine learning models for prediction of sepsis in hospitalized adults: An integrative review. Intensive Crit. Care Nurs. 65, 103035 (2021)","journal-title":"Intensive Crit. Care Nurs."},{"key":"6_CR26","unstructured":"Kotelnikov, A., Baranchuk, D., Rubachev, I., Babenko, A.: TabDDPM: Modelling tabular data with diffusion models. In: International Conference on Machine Learning. pp. 17564\u201317579 (2023)"},{"key":"6_CR27","first-page":"407","volume":"9","author":"RJ Little","year":"1993","unstructured":"Little, R.J., et al.: Statistical analysis of masked data. Journal of Official Statistics-stockholm- 9, 407\u2013407 (1993)","journal-title":"Journal of Official Statistics-stockholm-"},{"issue":"1","key":"6_CR28","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1146\/annurev.nutr.17.1.141","volume":"17","author":"ME Lowe","year":"1997","unstructured":"Lowe, M.E.: Structure and function of pancreatic lipase and colipase. Annu. Rev. Nutr. 17(1), 141\u2013158 (1997)","journal-title":"Annu. Rev. Nutr."},{"key":"6_CR29","doi-asserted-by":"crossref","unstructured":"Miao, L., Guo, X., Abbas, H.T., Qaraqe, K.A., Abbasi, Q.H.: Using machine learning to predict the future development of disease. In: International conference on UK-China emerging technologies (UCET). pp.\u00a01\u20134 (2020)","DOI":"10.1109\/UCET51115.2020.9205373"},{"issue":"6","key":"6_CR30","doi-asserted-by":"publisher","first-page":"2158","DOI":"10.1109\/TPAMI.2020.3015420","volume":"43","author":"A Morales","year":"2020","unstructured":"Morales, A., Fierrez, J., Vera-Rodriguez, R., Tolosana, R.: SensitiveNets: Learning agnostic representations with application to face images. IEEE Trans. Pattern Anal. Mach. Intell. 43(6), 2158\u20132164 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"4","key":"6_CR31","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1097\/CCM.0000000000002936","volume":"46","author":"S Nemati","year":"2018","unstructured":"Nemati, S., Holder, A., Razmi, F., Stanley, M.D., Clifford, G.D., Buchman, T.G.: An interpretable machine learning model for accurate prediction of sepsis in the icu. Crit. Care Med. 46(4), 547\u2013553 (2018)","journal-title":"Crit. Care Med."},{"key":"6_CR32","doi-asserted-by":"crossref","unstructured":"Neves, J.C., Tolosana, R., Vera-Rodriguez, R., Lopes, V., Proenca, H., Fierrez, J.: Gan fingerprints in face image synthesis. In: H.\u00a0T.\u00a0Sencar, L.\u00a0Verdoliva, N.M. (ed.) Multimedia Forensics. pp. 175\u2013204. ACVPR (April 2022)","DOI":"10.1007\/978-981-16-7621-5_8"},{"key":"6_CR33","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"6_CR34","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1016\/j.procs.2021.10.046","volume":"193","author":"E Plesovskaya","year":"2021","unstructured":"Plesovskaya, E., Ivanov, S.: An empirical analysis of KDE-based generative models on small datasets. Procedia Computer Science 193, 442\u2013452 (2021)","journal-title":"Procedia Computer Science"},{"issue":"7","key":"6_CR35","volume":"8","author":"D Rankin","year":"2020","unstructured":"Rankin, D., Black, M., Bond, R., Wallace, J., Mulvenna, M., Epelde, G., et al.: Reliability of supervised machine learning using synthetic data in health care: Model to preserve privacy for data sharing. Med. Inform. 8(7), e18910 (2020)","journal-title":"Med. Inform."},{"issue":"2","key":"6_CR36","first-page":"461","volume":"9","author":"DB Rubin","year":"1993","unstructured":"Rubin, D.B.: Statistical disclosure limitation. Journal of Official Statistics 9(2), 461\u2013468 (1993)","journal-title":"Journal of Official Statistics"},{"issue":"10219","key":"6_CR37","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/S0140-6736(19)32989-7","volume":"395","author":"KE Rudd","year":"2020","unstructured":"Rudd, K.E., Johnson, S.C., Agesa, K.M., Shackelford, K.A., Tsoi, D., Kievlan, D.R., Colombara, D.V., Ikuta, K.S., Kissoon, N., Finfer, S., et al.: Global, regional, and national sepsis incidence and mortality, 1990\u20132017: analysis for the global burden of disease study. The Lancet 395(10219), 200\u2013211 (2020)","journal-title":"The Lancet"},{"key":"6_CR38","unstructured":"Schamoni, S., Hagmann, M., Riezler, S.: Ensembling neural networks for improved prediction and privacy in early diagnosis of sepsis. In: Machine Learning for Healthcare Conference. pp. 123\u2013145 (2022)"},{"key":"6_CR39","doi-asserted-by":"crossref","unstructured":"Shafique, R., Rustam, F., Choi, G.S., D\u00edez, I.d.l.T., Mahmood, A., Lipari, V., Velasco, C.L.R., Ashraf, I.: Breast cancer prediction using fine needle aspiration features and upsampling with supervised machine learning. Cancers 15(3), 681 (2023)","DOI":"10.3390\/cancers15030681"},{"issue":"7","key":"6_CR40","doi-asserted-by":"publisher","first-page":"4875","DOI":"10.1007\/s11831-021-09556-z","volume":"28","author":"A Sharma","year":"2021","unstructured":"Sharma, A., Rani, R.: A systematic review of applications of machine learning in cancer prediction and diagnosis. Archives of Computational Methods in Engineering 28(7), 4875\u20134896 (2021)","journal-title":"Archives of Computational Methods in Engineering"},{"key":"6_CR41","unstructured":"Shrimanker, I., Bhattarai, S.: Electrolytes. StatPearls Publishing (2019)"},{"key":"6_CR42","doi-asserted-by":"crossref","unstructured":"Siddiq, M.: Use of machine learning to predict patient developing a disease or condition for early diagnose. International Journal of Multidisciplinary Sciences and Arts 1(1) (2022)","DOI":"10.47709\/ijmdsa.v1i1.2271"},{"issue":"8","key":"6_CR43","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1001\/jama.2016.0287","volume":"315","author":"M Singer","year":"2016","unstructured":"Singer, M., Deutschman, C.S., Seymour, C.W., Shankar-Hari, M., Annane, D., Bauer, M., Bellomo, R., Bernard, G.R., Chiche, J.D., Coopersmith, C.M., et al.: The third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA 315(8), 801\u2013810 (2016)","journal-title":"JAMA"},{"key":"6_CR44","doi-asserted-by":"publisher","first-page":"117643","DOI":"10.1109\/ACCESS.2023.3325705","volume":"11","author":"N Sinha","year":"2023","unstructured":"Sinha, N., Kumar, M.G., Joshi, A.M., Cenkeramaddi, L.R.: DASMcC: Data augmented SMOTE multi-class classifier for prediction of cardiovascular diseases using time series features. IEEE Access 11, 117643\u2013117655 (2023)","journal-title":"IEEE Access"},{"issue":"29","key":"6_CR45","doi-asserted-by":"publisher","first-page":"3775","DOI":"10.3748\/wjg.v18.i29.3775","volume":"18","author":"S Sookoian","year":"2012","unstructured":"Sookoian, S., Pirola, C.J.: Alanine and aspartate aminotransferase and glutamine-cycling pathway: their roles in pathogenesis of metabolic syndrome. World J. Gastroenterol. 18(29), 3775 (2012)","journal-title":"World J. Gastroenterol."},{"issue":"6","key":"6_CR46","doi-asserted-by":"publisher","first-page":"1485","DOI":"10.1016\/j.jhep.2021.06.010","volume":"75","author":"L V\u00edtek","year":"2021","unstructured":"V\u00edtek, L., Tiribelli, C.: Bilirubin: The yellow hormone? J. Hepatol. 75(6), 1485\u20131490 (2021)","journal-title":"J. Hepatol."},{"issue":"2","key":"6_CR47","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1097\/SHK.0000000000002010","volume":"59","author":"B Weber","year":"2023","unstructured":"Weber, B., Henrich, D., Hildebrand, F., Marzi, I., Leppik, L.: The roles of extracellular vesicles in sepsis and systemic inflammatory response syndrome. Shock 59(2), 161 (2023)","journal-title":"Shock"},{"key":"6_CR48","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.neucom.2019.12.136","volume":"416","author":"A Yale","year":"2020","unstructured":"Yale, A., Dash, S., Dutta, R., Guyon, I., Pavao, A., Bennett, K.P.: Generation and evaluation of privacy preserving synthetic health data. Neurocomputing 416, 244\u2013255 (2020)","journal-title":"Neurocomputing"},{"key":"6_CR49","doi-asserted-by":"crossref","unstructured":"Yang, F., Yu, Z., Liang, Y., Gan, X., Lin, K., Zou, Q., Zeng, Y.: Grouped correlational Generative Adversarial Networks for discrete electronic health records. In: IEEE International Conference on Bioinformatics and Biomedicine. pp. 906\u2013913 (2019)","DOI":"10.1109\/BIBM47256.2019.8983215"},{"key":"6_CR50","unstructured":"Zhao, Z., Kunar, A., Birke, R., Chen, L.Y.: CTAB-GAN: Effective table data synthesizing. In: Asian Conference on Machine Learning. pp. 97\u2013112 (2021)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78198-8_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T10:17:40Z","timestamp":1733221060000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78198-8_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,4]]},"ISBN":["9783031781971","9783031781988"],"references-count":50,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78198-8_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,4]]},"assertion":[{"value":"4 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}