{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T17:10:32Z","timestamp":1781629832968,"version":"3.54.5"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T00:00:00Z","timestamp":1768348800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T00:00:00Z","timestamp":1770681600000},"content-version":"vor","delay-in-days":27,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100008332","name":"Graz University of Technology","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100008332","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Machine learning models have shown great potential in preventive medicine but require large datasets, which is a challenge due to strict privacy regulations in the healthcare sector. Federated learning is an approach that enables collaboration between institutions while preserving data privacy. The focus today in research is highly on developing federated learning methods using artificial neural networks. In this study, we aimed to contribute federated learning modelling methods applied for random forests with an use-case of predicting delirium in hospitalised patients using data from multiple hospitals.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We collected data from eleven hospitals, including 29,479 patients and 627 features. We developed individual random forest models for each hospital data and a general model using all data. We developed federated learning models by averaging the predictions of the individual hospital models, with different schemes based on the number of samples, positives cases, minority cases and maximum possible diversity and evaluated the models using area under the receiver operating characteristic curve (AUROC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The general model outperformed all the other models with an AUROC of 0.855 [0.845\u20130.865]. Models trained on data from single hospitals varied in performance with an AUROC ranging from 0.633 to 0.829. Models from hospitals with large datasets performed better than those of small hospitals. Federated learning models outperformed individual models. With an AUROC of 0.794 [0.782\u20130.806], unweighted averaging achieved the worst results. Among the weighting algorithms, the number of positive cases performed the best reaching an AUROC of 0.843 [0.832\u20130.854], followed by minority cases (AUROC\u2009=\u20090.841 [0.830\u20130.852]), maximum possible diversity (AUROC\u2009=\u20090.836 [0.825\u20130.847]) and number of samples (AUROC\u2009=\u20090.830 [0.819\u20130.841]).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Results show that federated learning models can perform better than hospital-specific models in some cases, especially hospitals with limited data. In case of datasets of different size, we suggest weighted averaging based on the number of samples. If the datasets are class imbalanced, minority cases or maximum possible diversity should also be considered. Additionally, federated learning models maintain consistency compared to hospital specific models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical trial registration<\/jats:title>\n                    <jats:p>Not applicable.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-025-03322-y","type":"journal-article","created":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T08:45:06Z","timestamp":1768380306000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Development and validation of random-forest based federated ensemble learning algorithms for delirium prediction using electronic medical records from eleven hospitals in Austria: a retrospective study"],"prefix":"10.1186","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2009-6428","authenticated-orcid":false,"given":"Sai Pavan Kumar","family":"Veeranki","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dieter","family":"Hayn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diether","family":"Kramer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Piyush Gajananrao","family":"Gampawar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Baumgartner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lena Delia","family":"Lorenzer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Schrempf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G\u00fcnter","family":"Schreier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,14]]},"reference":[{"key":"3322_CR1","doi-asserted-by":"crossref","unstructured":"Bonawitz K, Ivanov V, Kreuter B, Marcedone A, McMahan HB, Patel S, et al. Practical secure aggregation for privacy-preserving machine learning. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security [Internet]. Dallas Texas USA: ACM; 2017 [cited 2021 Jan 21]. P. 1175\u201391. Available from: https:\/\/dl.acm.org\/doi\/10.1145\/3133956.3133982.","DOI":"10.1145\/3133956.3133982"},{"issue":"1","key":"3322_CR2","doi-asserted-by":"publisher","first-page":"3069","DOI":"10.1038\/s41467-019-10933-3","volume":"10","author":"L Rocher","year":"2019","unstructured":"Rocher L, Hendrickx JM, de Montjoye YA. Estimating the success of re-identifications in incomplete datasets using generative models. Nat Commun. 23 Jul 2019;10(1):3069.","journal-title":"Nat Commun"},{"key":"3322_CR3","unstructured":"McMahan HB, Ramage D, Talwar K, Zhang L. Learning differentially Private recurrent language models. ArXiv171006963 Cs [Internet]. 23 Feb 2018; [cited 2021 Jan 21]. Available from: http:\/\/arxiv.org\/abs\/1710.06963."},{"issue":"5","key":"3322_CR4","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1197\/jamia.M2716","volume":"15","author":"K El Emam","year":"2008","unstructured":"El Emam K, Dankar FK. Protecting privacy using k-anonymity. J Am Med Inf Assoc JAMIA. Oct 2008;15(5):627\u201337.","journal-title":"J Am Med Inf Assoc JAMIA"},{"key":"3322_CR5","first-page":"210","volume":"260","author":"A Eggerth","year":"2019","unstructured":"Eggerth A, Hayn D, Kreiner K, Veeranki S, Traninger H, Modre-Osprian R, et al. Patient record linkage for data quality assessment based on time series matching. Stud Health Technol Inf. 2019;260:210\u201317.","journal-title":"Stud Health Technol Inf"},{"key":"3322_CR6","unstructured":"Li J, Khodak M, Caldas S, Talwalkar A. Differentially Private meta-learning. ArXiv190905830 Cs Stat [Internet]. 21 Feb 2020; [cited 2021 Jan 21]; Available from: http:\/\/arxiv.org\/abs\/1909.05830."},{"key":"#cr-split#-3322_CR7.1","unstructured":"Bhowmick A, Duchi J, Freudiger J, Kapoor G, Rogers R. Protection against reconstruction and its applications in private federated learning. ArXiv181200984 Cs Stat [Internet]. 2019 Jun 3"},{"key":"#cr-split#-3322_CR7.2","unstructured":"[cited 2021 Jan 21]. Available from: http:\/\/arxiv.org\/abs\/1812.00984."},{"issue":"8","key":"3322_CR8","doi-asserted-by":"publisher","first-page":"e186040","DOI":"10.1001\/jamanetworkopen.2018.6040","volume":"1","author":"L Na","year":"2018","unstructured":"Na L, Yang C, Lo CC, Zhao F, Fukuoka Y, Aswani A. Feasibility of reidentifying individuals in large National physical activity data sets from which protected Health Information has been removed with use of Machine learning. JAMA Netw Open. 2018 Dec 7;1(8):e186040.","journal-title":"JAMA Netw Open"},{"issue":"17","key":"3322_CR9","doi-asserted-by":"publisher","first-page":"1684","DOI":"10.1056\/NEJMc1908881","volume":"381","author":"CG Schwarz","year":"2019","unstructured":"Schwarz CG, Kremers WK, Therneau TM, Sharp RR, Gunter JL, Vemuri P, et al. Identification of anonymous MRI research participants with face-recognition software. N Engl J Med. 2019 Oct 24;381(17):1684\u201386.","journal-title":"N Engl J Med"},{"key":"3322_CR10","unstructured":"Malin B, Sweeney L. Re-identification of DNA through an automated linkage process. Proc AMIA Symp. 2001;423\u201327."},{"key":"3322_CR11","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1038\/s41746-020-00323-1","volume":"3","author":"N Rieke","year":"2020","unstructured":"Rieke N, Hancox J, Li W, Milletar\u00ec F, Roth HR, Albarqouni S, et al. The future of digital health with federated learning. NPJ Digit Med. 2020;3:119.","journal-title":"NPJ Digit Med"},{"issue":"2","key":"3322_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3298981","volume":"10","author":"Q Yang","year":"2019","unstructured":"Yang Q, Liu Y, Chen T, Tong Y. Federated Machine learning: concept and applications. ACM Trans Intell Syst Technol. 2019 Mar 31;10(2):1\u201319.","journal-title":"ACM Trans Intell Syst Technol"},{"key":"#cr-split#-3322_CR13.1","unstructured":"McMahan HB, Moore E, Ramage D, Hampson S, y ABA. Communication-Efficient learning of deep networks from decentralized data. ArXiv160205629 Cs [Internet]. 2017 Feb 28"},{"key":"#cr-split#-3322_CR13.2","unstructured":"[cited 2021 Jan 21]. Available from: http:\/\/arxiv.org\/abs\/1602.05629."},{"key":"3322_CR14","unstructured":"Reisizadeh A, Mokhtari A, Hassani H, Jadbabaie A, Pedarsani R. FedPAQ: a communication-Efficient federated learning method with periodic averaging and quantization. ArXiv190913014 Cs Math Stat [Internet]. Jun 7 2020. [cited 2021 Jan 21]. Available from: http:\/\/arxiv.org\/abs\/1909.13014."},{"key":"#cr-split#-3322_CR15.1","unstructured":"Kone\u010dn\u00fd J, McMahan HB, Ramage D, Richt\u00e1rik P. Federated optimization: distributed machine learning for on-device Intelligence. ArXiv161002527 Cs [Internet]. 2016 8 Oct"},{"key":"#cr-split#-3322_CR15.2","unstructured":"[cited 2021 Jan 21]. Available from: http:\/\/arxiv.org\/abs\/1610.02527."},{"key":"3322_CR16","unstructured":"Reisizadeh A, Farnia F, Pedarsani R, Jadbabaie A. Robust federated learning: the case of affine distribution shifts. Proceedings of the 34th International Conference on Neural Information Processing Systems. Red Hook, NY, USA: Curran Associates Inc; 2020. (NIPS\u201920)."},{"key":"3322_CR17","unstructured":"Wang H, Yurochkin M, Sun Y, Papailiopoulos D, Khazaeni Y. Federated learning with matched averaging. 2020."},{"issue":"21","key":"3322_CR18","doi-asserted-by":"publisher","first-page":"214001","DOI":"10.1088\/1361-6560\/ac97d9","volume":"67","author":"P Foley","year":"2022","unstructured":"Foley P, Sheller MJ, Edwards B, Pati S, Riviera W, Sharma M, et al. OpenFL: the open federated learning library. Phys Med Biol. 2022 Nov 7;67(21):214001.","journal-title":"Phys Med Biol"},{"issue":"6","key":"3322_CR19","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1038\/s42256-020-0186-1","volume":"2","author":"GA Kaissis","year":"2020","unstructured":"Kaissis GA, Makowski MR, R\u00fcckert D, Braren RF. Secure, privacy-preserving and federated machine learning in medical imaging. Nat Mach Intell. Jun 2020;2(6):305\u201311.","journal-title":"Nat Mach Intell"},{"key":"#cr-split#-3322_CR20.1","doi-asserted-by":"crossref","unstructured":"Li W, Milletar\u00ec F, Xu D, Rieke N, Hancox J, Zhu W, et al. Privacy-preserving federated brain tumour segmentation. ArXiv191000962 Cs [Internet]. 2019 Oct 2","DOI":"10.1007\/978-3-030-32692-0_16"},{"key":"#cr-split#-3322_CR20.2","unstructured":"[cited 2021 Jan 21]. Available from: http:\/\/arxiv.org\/abs\/1910.00962."},{"key":"#cr-split#-3322_CR21.1","unstructured":"Corinzia L, Buhmann JM. Variational federated multi-task learning. ArXiv190606268 Cs Stat [Internet]. 2019 Jun 14"},{"key":"#cr-split#-3322_CR21.2","unstructured":"[cited 2021 Jan 21]. Available from: http:\/\/arxiv.org\/abs\/1906.06268."},{"issue":"7862","key":"3322_CR22","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1038\/s41586-021-03583-3","volume":"594","author":"S Warnat-Herresthal","year":"2021","unstructured":"Warnat-Herresthal S, Schultze H, Shastry KL, Manamohan S, Mukherjee S, Garg V, et al. Swarm learning for decentralized and confidential clinical machine learning. Nature. Jun 2021;594(7862):265\u201370.","journal-title":"Nature"},{"key":"#cr-split#-3322_CR23.1","unstructured":"Lalitha A, Kilinc OC, Javidi T, Koushanfar F. Peer-to-peer federated learning on graphs. ArXiv190111173 Cs Stat [Internet]. 2019 Jan 30"},{"key":"#cr-split#-3322_CR23.2","unstructured":"[cited 2021 Jan 21]. Available from: http:\/\/arxiv.org\/abs\/1901.11173."},{"key":"3322_CR24","doi-asserted-by":"publisher","first-page":"e41588","DOI":"10.2196\/41588","volume":"25","author":"A Brauneck","year":"2023","unstructured":"Brauneck A, Schmalhorst L, Kazemi Majdabadi MM, Bakhtiari M, V\u00f6lker U, Baumbach J, et al. Federated machine learning, privacy-enhancing technologies, and data Protection laws in medical research: scoping review. J Med Internet Res. 30 Mar 2023;25:e41588.","journal-title":"J Med Internet Res"},{"key":"3322_CR25","doi-asserted-by":"publisher","unstructured":"Truex S, Baracaldo N, Anwar A, Steinke T, Ludwig H, Zhang R, et al. A hybrid approach to privacy-preserving federated learning. Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security [Internet]. New York, NY, USA: Association for Computing Machinery; 2019. p. 1\u201311. (AISec\u201919). Available from: https:\/\/doi.org\/10.1145\/3338501.3357370.","DOI":"10.1145\/3338501.3357370"},{"issue":"3","key":"3322_CR26","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/s41666-023-00142-5","volume":"7","author":"M Baumgartner","year":"2023","unstructured":"Baumgartner M, Veeranki SPK, Hayn D, Schreier G. Introduction and comparison of Novel decentral learning schemes with multiple data pools for privacy-preserving ECG Classification. J Healthc Inf Res. 2023 Sep 1;7(3):291\u2013312.","journal-title":"J Healthc Inf Res"},{"issue":"14","key":"3322_CR27","first-page":"2340","volume":"13","author":"MA Khan","year":"2023","unstructured":"Khan MA, Alsulami M, Yaqoob MM, Alsadie D, Saudagar AKJ, AlKhathami M, et al. Asynchronous federated learning for improved cardiovascular disease prediction using Artificial Intelligence. Diagn Basel Switz. 2023 Jul 11;13(14):2340.","journal-title":"Diagn Basel Switz"},{"issue":"20","key":"3322_CR28","first-page":"3166","volume":"13","author":"S Bebortta","year":"2023","unstructured":"Bebortta S, Tripathy SS, Basheer S, Chowdhary CL. FedEHR: a federated learning approach towards the prediction of heart diseases in IoT-Based electronic Health records. Diagn Basel Switz. 2023 Oct 10;13(20):3166.","journal-title":"Diagn Basel Switz"},{"key":"3322_CR29","first-page":"32","volume":"236","author":"D Kramer","year":"2017","unstructured":"Kramer D, Veeranki S, Hayn D, Quehenberger F, Leodolter W, Jagsch C, et al. Development and validation of a Multivariable prediction model for the occurrence of delirium in Hospitalized gerontopsychiatry and internal medicine patients. Stud Health Technol Inf. 2017;236:32\u201339.","journal-title":"Stud Health Technol Inf"},{"key":"3322_CR30","first-page":"31","volume":"271","author":"AM Lienhart","year":"2020","unstructured":"Lienhart AM, Kramer D, Jauk S, Gugatschka M, Leodolter W, Schlegl T. Multivariable risk prediction of dysphagia in Hospitalized patients using machine learning. Stud Health Technol Inf. 2020 Jun 23;271:31\u201338.","journal-title":"Stud Health Technol Inf"},{"key":"3322_CR31","first-page":"136","volume":"279","author":"M Schrempf","year":"2021","unstructured":"Schrempf M, Kramer D, Jauk S, Veeranki SPK, Leodolter W, Rainer PP. Machine learning based risk prediction for Major adverse cardiovascular events. Stud Health Technol Inf. 2021 May 7;279:136\u201343.","journal-title":"Stud Health Technol Inf"},{"issue":"1","key":"3322_CR32","first-page":"5","volume":"45","author":"BL Learn","year":"2001","unstructured":"Learn BL. Random forests. Mach. 2001 Oct;45(1):5\u201332.","journal-title":"Mach"},{"issue":"3","key":"3322_CR33","doi-asserted-by":"publisher","first-page":"843","DOI":"10.1109\/TBDATA.2020.2992755","volume":"8","author":"Y Liu","year":"2022","unstructured":"Liu Y, Liu Y, Liu Z, Liang Y, Meng C, Zhang J, et al. Federated forest. IEEE Trans Big Data. 2022 Jun 1;8(3):843\u201354.","journal-title":"IEEE Trans Big Data"},{"key":"3322_CR34","doi-asserted-by":"crossref","unstructured":"Liu Y, Ma Z, Liu X, Ma S, Nepal S, Deng R. boosting privately: privacy-preserving federated extreme Boosting for mobile crowdsensing. 2020.","DOI":"10.1109\/ICDCS47774.2020.00017"},{"issue":"6","key":"3322_CR35","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1109\/MIS.2021.3082561","volume":"36","author":"K Cheng","year":"2021","unstructured":"Cheng K, Fan T, Jin Y, Liu Y, Chen T, Papadopoulos D, et al. SecureBoost: a lossless federated learning framework. IEEE Intell Syst. 2021 Nov;36(6):87\u201398.","journal-title":"IEEE Intell Syst"},{"key":"3322_CR36","doi-asserted-by":"crossref","unstructured":"Markovic T, Leon M, Buffoni D, Punnekkat S. Random forest based on federated learning for intrusion detection. In: Maglogiannis I, Iliadis L, Macintyre J, Cortez P, editors. Artificial intelligence applications and innovations [internet]. Cham: Springer International Publishing; 2022 [cited 2023 Mar 16]. p. 132\u201344. Available from: https:\/\/link.springer.com\/10.1007\/978-3-031-08333-4_11.","DOI":"10.1007\/978-3-031-08333-4_11"},{"key":"3322_CR37","doi-asserted-by":"publisher","first-page":"89835","DOI":"10.1109\/ACCESS.2022.3202008","volume":"10","author":"M Gencturk","year":"2022","unstructured":"Gencturk M, Sinaci AA, Cicekli NK. BOFRF a Novel Boosting-based federated random forest algorithm on horizontally partitioned data. IEEE Access. 2022;10:89835\u201351.","journal-title":"IEEE Access"},{"issue":"8","key":"3322_CR38","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1093\/bioinformatics\/btac065","volume":"38","author":"AC Hauschild","year":"2022","unstructured":"Hauschild AC, Lemanczyk M, Matschinske J, Frisch T, Zolotareva O, Holzinger A, et al. Federated random forests can improve local performance of predictive models for various healthcare applications. Wren J. editor. Bioinformatics. 2022 Apr 12;38(8):2278\u201386.","journal-title":"Bioinformatics"},{"key":"3322_CR39","unstructured":"Fan T, Kang Y, Ma G, Chen W, Wei W, Fan L, et al. FATE-LLM: a industrial grade federated learning framework for large language models [Internet]. 2023. Available from: https:\/\/arxiv.org\/abs\/2310.10049."},{"key":"3322_CR40","doi-asserted-by":"crossref","unstructured":"Henshaw ER, Chukwudi Osamor V, Azeta AA. Federated learning-based asthma prediction model in children. 2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG). 2024. 1\u20136.","DOI":"10.1109\/SEB4SDG60871.2024.10629888"},{"key":"3322_CR41","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. Smote: synthetic minority over-sampling technique. J Artif Intell Res. 2002 Jun;16:321\u201357.","journal-title":"J Artif Intell Res"},{"key":"3322_CR42","unstructured":"Rajput PS, Satis K, Dellarosa S, Huang W, Agba O. cGans for cartoon to real-life images [Internet]. 2021. Available from: https:\/\/arxiv.org\/abs\/2101.09793."},{"key":"3322_CR43","doi-asserted-by":"crossref","unstructured":"Madhu G, De A, Pal G. An innovative approach towards Efficient diabetes mellitus prediction using federated forest. 2024 IEEE Silchar Subsection Conference (SILCON 2024). 2024. 1\u20136.","DOI":"10.1109\/SILCON63976.2024.10910400"},{"key":"3322_CR44","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/j.ject.2024.11.001","volume":"3","author":"K Meduri","year":"2025","unstructured":"Meduri K, Nadella GS, Yadulla AR, Kasula VK, Maturi MH, Brown S, et al. Leveraging federated learning for privacy-preserving analysis of multi-institutional electronic health records in rare disease research. J Econ Technol. 2025;3:177\u201389.","journal-title":"J Econ Technol"},{"issue":"1","key":"3322_CR45","doi-asserted-by":"publisher","first-page":"100898","DOI":"10.1016\/j.patter.2023.100898","volume":"5","author":"W Pan","year":"2024","unstructured":"Pan W, Xu Z, Rajendran S, Wang F. An adaptive federated learning framework for clinical risk prediction with electronic health records from multiple hospitals. Patterns N Y N. 2024 Jan 12;5(1):100898.","journal-title":"Patterns N Y N"},{"issue":"8","key":"3322_CR46","doi-asserted-by":"publisher","first-page":"631","DOI":"10.2165\/00023210-200822080-00002","volume":"22","author":"A Attard","year":"2008","unstructured":"Attard A, Ranjith G, Taylor D. Delirium and its treatment. CNS Drugs. 2008;22(8):631\u201344.","journal-title":"CNS Drugs"},{"issue":"2","key":"3322_CR47","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1111\/j.1532-5415.1995.tb06385.x","volume":"43","author":"BW Fisher","year":"1995","unstructured":"Fisher BW, Flowerdew G. A simple model for Predicting postoperative delirium in older patients undergoing elective orthopedic surgery. J Am Geriatr Soc. 1995 Feb;43(2):175\u201378.","journal-title":"J Am Geriatr Soc"},{"issue":"5","key":"3322_CR48","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1093\/ageing\/afw084","volume":"45","author":"SQ Fortes-Filho","year":"2016","unstructured":"Fortes-Filho SQ, Apolinario D, Melo JA, Suzuki I, Sitta M C, Garcez Leme LE. Predicting delirium after hip fracture with a 2-min cognitive screen: prospective cohort study. Age Ageing. 2016;45(5):713\u201317.","journal-title":"Age Ageing"},{"issue":"4","key":"3322_CR49","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1007\/s10916-021-01727-6","volume":"45","author":"S Jauk","year":"2021","unstructured":"Jauk S, Kramer D, Avian A, Berghold A, Leodolter W, Schulz S. Technology acceptance of a Machine learning algorithm Predicting delirium in a clinical setting: a mixed-methods study. J Med Syst. 2021 Apr;45(4):48.","journal-title":"J Med Syst"},{"issue":"9","key":"3322_CR50","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","volume":"21","author":"H He","year":"2009","unstructured":"He H, Garcia EA. Learning from imbalanced data. IEEE Trans Knowl Data Eng. 2009 Sep;21(9):1263\u201384.","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"8","key":"3322_CR51","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett T. An introduction to ROC analysis. Pattern Recognit Lett. 2006, Jun;27(8):861\u201374.","journal-title":"Pattern Recognit Lett"},{"key":"3322_CR52","doi-asserted-by":"crossref","unstructured":"Zhao L, Ni L, Hu S, Chen Y, Zhou P, Xiao F, et al. InPrivate digging: enabling tree-based distributed data mining with differential privacy. IEEE INFOCOM, 2018 - IEEE Conference on Computer Communications. 2018;2087-95.","DOI":"10.1109\/INFOCOM.2018.8486352"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-025-03322-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-03322-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-03322-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T16:44:02Z","timestamp":1781628242000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12911-025-03322-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,14]]},"references-count":58,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["3322"],"URL":"https:\/\/doi.org\/10.1186\/s12911-025-03322-y","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-2970317\/v1","asserted-by":"object"}]},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,14]]},"assertion":[{"value":"23 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study received approval from the ethics committee at Medical University of Graz under reference number 30\u2013146 ex 17\/18 1578\u20132017. This study strictly followed the guidelines and regulations set by KAGes guaranteeing privacy, confidentiality and responsible use of patients\u2019 data for scientific research and statistical purposes. Patients\u2019 provide informed consent at the point of registration at the hospital. The datasets were anonymised and de-identified prior to the study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"41"}}