{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T14:05:41Z","timestamp":1784642741158,"version":"3.55.0"},"publisher-location":"Cham","reference-count":9,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032191045","type":"print"},{"value":"9783032191052","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-19105-2_14","type":"book-chapter","created":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T22:04:44Z","timestamp":1778364284000},"page":"187-191","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Federated Markov Imputation: Privacy-Preserving Temporal Imputation in\u00a0Multi-centric ICU Environments"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7817-9448","authenticated-orcid":false,"given":"Christoph","family":"D\u00fcsing","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philipp","family":"Cimiano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Bonawitz, K., et al.: Practical secure aggregation for privacy-preserving machine learning. In: ACM SIGSAC Conference on Computer and Communications Security, pp. 1175\u20131191 (2017)","DOI":"10.1145\/3133956.3133982"},{"key":"14_CR2","doi-asserted-by":"crossref","unstructured":"D\u00fcsing, C., Cimiano, P.: SHAP\u2013FL: improving explainability for multicentric sepsis onset prediction through background dataset synthesis. In: International Conference on Artificial Intelligence in Medicine. Springer (2025)","DOI":"10.1007\/978-3-031-95838-0_12"},{"key":"14_CR3","doi-asserted-by":"publisher","first-page":"102982","DOI":"10.1016\/j.artmed.2024.102982","volume":"157","author":"C D\u00fcsing","year":"2024","unstructured":"D\u00fcsing, C., et al.: Integrating federated learning for improved counterfactual explanations in clinical decision support systems for sepsis therapy. Artif. Intell. Med. 157, 102982 (2024)","journal-title":"Artif. Intell. Med."},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"Gkillas, A., Lalos, A.S.: Missing data imputation for multivariate time series in industrial IoT: a federated learning approach. In: 2022 IEEE 20th International Conference on Industrial Informatics (INDIN), pp. 87\u201394. IEEE (2022)","DOI":"10.1109\/INDIN51773.2022.9976093"},{"key":"14_CR5","unstructured":"Johnson, A., Bulgarelli, L., Pollard, T., Horng, S., Celi, L.A., Mark, R.: MIMIC-IV (2022). https:\/\/doi.org\/10.13026\/rrgf-xw32"},{"key":"14_CR6","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., y\u00a0Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282. PMLR (2017)"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Min, S., Asif, H., Wang, X., Vaidya, J.: Cafe: Improved federated data imputation by leveraging missing data heterogeneity. IEEE Trans. Knowl. Data Eng. (2025)","DOI":"10.1109\/TKDE.2025.3537403"},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Mondrejevski, L., Azzopardi, D., Miliou, I.: Predicting sepsis onset with deep federated learning. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 73\u201386. Springer (2023)","DOI":"10.1007\/978-3-031-74640-6_6"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Wang, L., Bian, J., Xu, J.: Federated learning with instance-dependent noisy label. In: ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8916\u20138920. IEEE (2024)","DOI":"10.1109\/ICASSP48485.2024.10447823"}],"container-title":["Communications in Computer and Information Science","Machine Learning and Principles and Practice of Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-19105-2_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T13:55:02Z","timestamp":1784642102000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-19105-2_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032191045","9783032191052"],"references-count":9,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-19105-2_14","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Porto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecmlpkdd.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}