{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T11:48:40Z","timestamp":1785930520271,"version":"3.56.0"},"reference-count":78,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T00:00:00Z","timestamp":1785888000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T00:00:00Z","timestamp":1785888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"DOI":"10.1186\/s12911-026-03733-5","type":"journal-article","created":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T11:08:21Z","timestamp":1785928101000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GERBEHRT: a BERT-based model tailored for German electronic health records \u2013 potential in chronic kidney disease prediction"],"prefix":"10.1186","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6856-728X","authenticated-orcid":false,"given":"Anja","family":"Seidel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9937-4007","authenticated-orcid":false,"given":"Edgar","family":"Steiger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4492-696X","authenticated-orcid":false,"given":"Friedrich Alexander","family":"von Samson-Himmelstjerna","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6626-7600","authenticated-orcid":false,"given":"Lars Eric","family":"Kroll","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,5]]},"reference":[{"issue":"3","key":"3733_CR1","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1038\/sj.ki.5002343","volume":"72","author":"AS Levey","year":"2007","unstructured":"Levey AS, Atkins R, Coresh J, Cohen EP, Collins AJ, Eckardt KU, et al. Chronic kidney disease as a global public health problem: approaches and initiatives \u2013 a position statement from Kidney Disease Improving Global Outcomes. Kidney Int. 2007;72(3):247\u201359. https:\/\/doi.org\/10.1038\/sj.ki.5002343.","journal-title":"Kidney Int"},{"issue":"1","key":"3733_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/kisup.2012.73","volume":"3","author":"A Levin","year":"2013","unstructured":"Levin A, Stevens PE, Bilous RW, Coresh J, Francisco ALMD, Jong PED, et al. Kidney disease: improving global outcomes (KDIGO) CKD work group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl. 2013;3(1):1\u2013150. https:\/\/doi.org\/10.1038\/kisup.2012.73.","journal-title":"Kidney Int Suppl"},{"issue":"10225","key":"3733_CR3","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1016\/S0140-6736(20)30045-3","volume":"395","author":"B Bikbov","year":"2020","unstructured":"Bikbov B, Purcell CA, Levey AS, Smith M, Abdoli A, Abebe M, et al. Global, regional, and national burden of chronic kidney disease, 1990\u20132017: a systematic analysis for the global burden of disease study 2017. Lancet. 2020;395(10225):709\u201333. https:\/\/doi.org\/10.1016\/S0140-6736(20)30045-3.","journal-title":"Lancet"},{"key":"3733_CR4","doi-asserted-by":"publisher","unstructured":"Girndt M, Trocchi P, Scheidt-Nave C, Markau S, Stang A. Pr\u00e4valenz der eingeschr\u00e4nkten Nierenfunktion. Dtsch Arztebl. 2016. https:\/\/doi.org\/10.3238\/arztebl.2016.0085.","DOI":"10.3238\/arztebl.2016.0085"},{"key":"3733_CR5","doi-asserted-by":"publisher","unstructured":"Gandjour A, Armsen W, Wehmeyer W, Multmeier J, Tschulena U. Costs of patients with chronic kidney disease in Germany. PLoS On. 2020;15(4):e0231375. https:\/\/doi.org\/10.1371\/journal.pone.0231375.","DOI":"10.1371\/journal.pone.0231375"},{"issue":"3","key":"3733_CR6","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1097\/MNH.0000000000000597","volume":"29","author":"S Curtis","year":"2020","unstructured":"Curtis S, Komenda P. Screening for chronic kidney disease: moving toward more sustainable health care. Curr Opin Nephrol Hypertens. 2020;29(3):333\u201338. https:\/\/doi.org\/10.1097\/MNH.0000000000000597.","journal-title":"Curr Opin Nephrol Hypertens"},{"issue":"2","key":"3733_CR7","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.kisu.2017.07.003","volume":"7","author":"VA Luyckx","year":"2017","unstructured":"Luyckx VA, Tuttle KR, Garcia-Garcia G, Gharbi MB, Heerspink HJL, Johnson DW, et al. Reducing major risk factors for chronic kidney disease. Kidney Int Suppl. 2017;7(2):71\u201387. https:\/\/doi.org\/10.1016\/j.kisu.2017.07.003.","journal-title":"Kidney Int Suppl"},{"key":"3733_CR8","doi-asserted-by":"publisher","unstructured":"Stevens PE, Ahmed SB, Carrero JJ, Foster B, Francis A, Hall RK, et al. KDIGO 2024 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int. 2024;105(4):S117\u2013314. https:\/\/doi.org\/10.1016\/j.kint.2023.10.018.","DOI":"10.1016\/j.kint.2023.10.018"},{"key":"3733_CR9","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-662-58225-1_1","volume-title":"Einf\u00fchrung einer elektronischen Patientenakte in Deutschland vor dem Hintergrund der internationalen Erfahrungen","author":"N Bertram","year":"2019","unstructured":"Bertram N, P\u00fcschner F, Gon\u00e7alves ASO, Binder S, Amelung VE. Einf\u00fchrung einer elektronischen patientenakte in deutschland vor dem Hintergrund der internationalen Erfahrungen. Berlin Heidelberg: Springer; 2019. p. 3\u201316. https:\/\/doi.org\/10.1007\/978-3-662-58225-1_1."},{"key":"3733_CR10","doi-asserted-by":"publisher","unstructured":"Rau E, Tischendorf T, Mitzscherlich B. Implementation of the electronic health record in the German healthcare system: an assessment of the current status and future development perspectives considering the potentials of health data utilisation by representatives of different stakeholder groups. Front Health Serv. 2024;4:1370759. https:\/\/doi.org\/10.3389\/frhs.2024.1370759.","DOI":"10.3389\/frhs.2024.1370759"},{"key":"3733_CR11","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.compbiomed.2019.04.017","volume":"109","author":"NA Almansour","year":"2019","unstructured":"Almansour NA, Syed HF, Khayat NR, Altheeb RK, Juri RE, Alhiyafi J, et al. Neural network and support vector machine for the prediction of chronic kidney disease: a comparative study. Comput Biol Med. 2019;109:101\u201311. https:\/\/doi.org\/10.1016\/j.compbiomed.2019.04.017.","journal-title":"Comput Biol Med"},{"issue":"4","key":"3733_CR12","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1001\/archinte.167.4.374","volume":"167","author":"H Bang","year":"2007","unstructured":"Bang H, Vupputuri S, Shoham DA, Klemmer PJ, Falk RJ, Mazumdar M, et al. Screening for Occult REnal Disease (SCORED): a simple prediction model for chronic kidney disease. Arch Intern Med. 2007;167(4):374\u201381. https:\/\/doi.org\/10.1001\/archinte.167.4.374.","journal-title":"Arch Intern Med"},{"key":"3733_CR13","doi-asserted-by":"publisher","unstructured":"Collins G, Altman D. Predicting the risk of chronic kidney disease in the UK: an evaluation of QKidney\u00ae scores using a primary care database. Br J Gen Pract. 2012;62(597):e243\u201350. https:\/\/doi.org\/10.3399\/bjgp12X636065.","DOI":"10.3399\/bjgp12X636065"},{"key":"3733_CR14","doi-asserted-by":"publisher","first-page":"100189","DOI":"10.1016\/j.jpi.2023.100189","volume":"14","author":"MA Islam","year":"2023","unstructured":"Islam MA, Majumder MZH, Hussein MA. Chronic kidney disease prediction based on machine learning algorithms. J Pathol Inf. 2023;14:100189. https:\/\/doi.org\/10.1016\/j.jpi.2023.100189.","journal-title":"J Pathol Inf"},{"key":"3733_CR15","doi-asserted-by":"publisher","first-page":"20991","DOI":"10.1109\/ACCESS.2019.2963053","volume":"8","author":"J Qin","year":"2020","unstructured":"Qin J, Chen L, Liu Y, Liu C, Feng C, Chen B. A machine learning methodology for diagnosing chronic kidney disease. IEEE Access. 2020;8:20991\u20131002. https:\/\/doi.org\/10.1109\/ACCESS.2019.2963053.","journal-title":"IEEE Access"},{"issue":"1","key":"3733_CR16","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1186\/s40537-022-00657-5","volume":"9","author":"DA Debal","year":"2022","unstructured":"Debal DA, Sitote TM. Chronic kidney disease prediction using machine learning techniques. J Big Data. 2022;9(1):109. https:\/\/doi.org\/10.1186\/s40537-022-00657-5.","journal-title":"J Big Data"},{"issue":"21","key":"3733_CR17","doi-asserted-by":"publisher","first-page":"2104","DOI":"10.1001\/jama.2019.17379","volume":"322","author":"RG Nelson","year":"2019","unstructured":"Nelson RG, Grams ME, Ballew SH, Sang Y, Azizi F, Chadban SJ, et al. Development of risk prediction equations for incident chronic kidney disease. JAMA. 2019;322(21):2104\u201314. https:\/\/doi.org\/10.1001\/jama.2019.17379.","journal-title":"JAMA"},{"issue":"4","key":"3733_CR18","doi-asserted-by":"publisher","first-page":"872","DOI":"10.1093\/jamia\/ocv024","volume":"22","author":"A Perotte","year":"2015","unstructured":"Perotte A, Ranganath R, Hirsch JS, Blei D, Elhadad N. Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis. J Am Med Inf Assoc. 2015;22(4):872\u201380. https:\/\/doi.org\/10.1093\/jamia\/ocv024.","journal-title":"J Am Med Inf Assoc"},{"issue":"14","key":"3733_CR19","doi-asserted-by":"publisher","first-page":"4973","DOI":"10.3390\/ijerph17144973","volume":"17","author":"CC Shih","year":"2020","unstructured":"Shih CC, Lu CJ, Chen GD, Chang CC. Risk prediction for early chronic kidney disease: results from an adult health examination program of 19,270 individuals. Int J Environ Res Public Health. 2020;17(14):4973. https:\/\/doi.org\/10.3390\/ijerph17144973.","journal-title":"Int J Environ Res Public Health"},{"issue":"3","key":"3733_CR20","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1093\/jamia\/ocy165","volume":"26","author":"X Song","year":"2019","unstructured":"Song X, Waitman LR, Hu Y, Yu ASL, Robins D, Liu M. Robust clinical marker identification for diabetic kidney disease with ensemble feature selection. J Am Med Inf Assoc. 2019;26(3):242\u201353. https:\/\/doi.org\/10.1093\/jamia\/ocy165.","journal-title":"J Am Med Inf Assoc"},{"key":"3733_CR21","doi-asserted-by":"publisher","unstructured":"Zhu Y, Bi D, Saunders M, Ji Y. Prediction of chronic kidney disease progression using recurrent neural network and electronic health records. Sci Rep. 2023;13(1):22091. https:\/\/doi.org\/10.1038\/s41598-023-49271-2.","DOI":"10.1038\/s41598-023-49271-2"},{"issue":"5","key":"3733_CR22","doi-asserted-by":"publisher","first-page":"546","DOI":"10.3390\/healthcare9050546","volume":"9","author":"S Krishnamurthy","year":"2021","unstructured":"Krishnamurthy S, Ks K, Dovgan E, Lu\u0161trek M, Gradi\u0161ek Pileti\u010d B, Srinivasan K, et al. Machine learning prediction models for chronic kidney disease using national health insurance claim data in Taiwan. Healthcare. 2021;9(5):546. https:\/\/doi.org\/10.3390\/healthcare9050546.","journal-title":"Healthcare"},{"issue":"1","key":"3733_CR23","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s13755-023-00261-8","volume":"12","author":"D Saif","year":"2023","unstructured":"Saif D, Sarhan AM, Elshennawy NM. Deep-kidney: an effective deep learning framework for chronic kidney disease prediction. Health Inf Sci Syst. 2023;12(1):3. https:\/\/doi.org\/10.1007\/s13755-023-00261-8.","journal-title":"Health Inf Sci Syst"},{"issue":"1","key":"3733_CR24","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s43067-024-00142-4","volume":"11","author":"D Saif","year":"2024","unstructured":"Saif D, Sarhan AM, Elshennawy NM. Early prediction of chronic kidney disease based on ensemble of deep learning models and optimizers. J Electr Syst Inf Technol. 2024;11(1):17. https:\/\/doi.org\/10.1186\/s43067-024-00142-4.","journal-title":"J Electr Syst Inf Technol"},{"key":"3733_CR25","doi-asserted-by":"publisher","first-page":"152900","DOI":"10.1109\/ACCESS.2019.2948430","volume":"7","author":"GR V\u00e1squez-Morales","year":"2019","unstructured":"V\u00e1squez-Morales GR, Mart\u00ednez-Monterrubio SM, Moreno-Ger P, Recio-Garc\u00eda JA. Explainable prediction of chronic renal disease in the Colombian population using neural networks and case-based reasoning. IEEE Access. 2019;7:152900\u201310. https:\/\/doi.org\/10.1109\/ACCESS.2019.2948430.","journal-title":"IEEE Access"},{"key":"3733_CR26","doi-asserted-by":"publisher","unstructured":"Tangri N, Moriyama T, Schneider MP, Virgitti JB, De Nicola L, Arnold M, et al. Prevalence of undiagnosed stage 3 chronic kidney disease in France, Germany, Italy, Japan and the USA: results from the multinational observational REVEAL-CKD study. BMJ Open. 2023;13(5):e067386. https:\/\/doi.org\/10.1136\/bmjopen-2022-067386.","DOI":"10.1136\/bmjopen-2022-067386"},{"key":"3733_CR27","doi-asserted-by":"publisher","unstructured":"Shoham OB, Rappoport N. CPLLM: clinical prediction with large language models. PLoS Digit Health. 2024;3(12):e0000680. https:\/\/doi.org\/10.1371\/journal.pdig.0000680.","DOI":"10.1371\/journal.pdig.0000680"},{"issue":"1","key":"3733_CR28","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1038\/s41746-021-00455-y","volume":"4","author":"L Rasmy","year":"2021","unstructured":"Rasmy L, Xiang Y, Xie Z, Tao C, Zhi D. Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. NPJ Digit Med. 2021;4(1):86. https:\/\/doi.org\/10.1038\/s41746-021-00455-y.","journal-title":"NPJ Digit Med"},{"key":"3733_CR29","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint. 2018. https:\/\/doi.org\/10.48550\/arXiv.1810.04805.","DOI":"10.48550\/arXiv.1810.04805"},{"key":"3733_CR30","doi-asserted-by":"publisher","unstructured":"Shang J, Ma T, Xiao C, Sun J. Pre-training of graph augmented transformers for medication recommendation. arXiv preprint. 2019. https:\/\/doi.org\/10.48550\/arXiv.1906.00346.","DOI":"10.48550\/arXiv.1906.00346"},{"issue":"1","key":"3733_CR31","doi-asserted-by":"publisher","first-page":"7155","DOI":"10.1038\/s41598-020-62922-y","volume":"10","author":"Y Li","year":"2020","unstructured":"Li Y, Rao S, Solares JRA, Hassaine A, Ramakrishnan R, Canoy D, et al. BEHRT: transformer for electronic health records. Sci Rep. 2020;10(1):7155. https:\/\/doi.org\/10.1038\/s41598-020-62922-y.","journal-title":"Sci Rep"},{"issue":"1","key":"3733_CR32","doi-asserted-by":"publisher","first-page":"3517","DOI":"10.1038\/s41598-023-30657-1","volume":"13","author":"E Antikainen","year":"2023","unstructured":"Antikainen E, Linnosmaa J, Umer A, Oksala N, Eskola M, van Gils M, et al. Transformers for cardiac patient mortality risk prediction from heterogeneous electronic health records. Sci Rep. 2023;13(1):3517. https:\/\/doi.org\/10.1038\/s41598-023-30657-1.","journal-title":"Sci Rep"},{"issue":"9","key":"3733_CR33","doi-asserted-by":"publisher","first-page":"4548","DOI":"10.1109\/JBHI.2023.3288768","volume":"27","author":"M Lentzen","year":"2023","unstructured":"Lentzen M, Linden T, Veeranki S, Madan S, Kramer D, Leodolter W, et al. A transformer-based model trained on large scale claims data for prediction of severe COVID-19 disease progression. IEEE J Biomed Health Inf. 2023;27(9):4548\u201358. https:\/\/doi.org\/10.1109\/JBHI.2023.3288768.","journal-title":"IEEE J Biomed Health Inf"},{"issue":"8","key":"3733_CR34","doi-asserted-by":"publisher","first-page":"3121","DOI":"10.1109\/JBHI.2021.3063721","volume":"25","author":"Y Meng","year":"2021","unstructured":"Meng Y, Speier W, Ong MK, Arnold CW. Bidirectional representation learning from transformers using multimodal electronic health record data to predict depression. IEEE J Biomed Health Inf. 2021;25(8):3121\u201329. https:\/\/doi.org\/10.1109\/JBHI.2021.3063721.","journal-title":"IEEE J Biomed Health Inf"},{"key":"3733_CR35","doi-asserted-by":"publisher","unstructured":"Odgaard M, Klein KV, Thysen SM, Jimenez-Solem E, Sillesen M, Nielsen M. CORE-BEHRT: a carefully optimized and rigorously evaluated BEHRT. arXiv preprint. 2024. https:\/\/doi.org\/10.48550\/arXiv.2404.15201.","DOI":"10.48550\/arXiv.2404.15201"},{"key":"3733_CR36","unstructured":"Pang C, Jiang X, Kalluri KS, Spotnitz M, Chen R, Perotte A, et al. CEHR-BERT: incorporating temporal information from structured EHR data to improve prediction tasks. Proc Mach Learn Res. 2021:239\u201360;158."},{"issue":"1","key":"3733_CR37","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1609\/aaai.v35i1.16122","volume":"35","author":"P Prakash","year":"2021","unstructured":"Prakash P, Chilukuri S, Ranade N, Viswanathan S. RareBERT: transformer architecture for rare disease patient identification using administrative claims. Proc AAAI Conf Artif Intell. 2021;35(1):453\u201360. https:\/\/doi.org\/10.1609\/aaai.v35i1.16122.","journal-title":"Proceedings Of The AAAI Conference On Artificial Intelligence"},{"key":"3733_CR38","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/978-3-031-39539-0_7","volume-title":"Trustworthy machine learning for healthcare","author":"M Rupp","year":"2023","unstructured":"Rupp M, Peter O, Pattipaka T. ExBEHRT: extended transformer for electronic health records. In: Chen H, Luo L, editors. Trustworthy machine learning for healthcare. Cham: Springer Nature Switzerland; 2023. p. 73\u201384."},{"issue":"1","key":"3733_CR39","doi-asserted-by":"publisher","first-page":"7857","DOI":"10.1038\/s41467-023-43715-z","volume":"14","author":"Z Yang","year":"2023","unstructured":"Yang Z, Mitra A, Liu W, Berlowitz D, Yu H. TransformEHR: transformer-based encoder-decoder generative model to enhance prediction of disease outcomes using electronic health records. Nat Commun. 2023;14(1):7857. https:\/\/doi.org\/10.1038\/s41467-023-43715-z.","journal-title":"Nat Commun"},{"key":"3733_CR40","doi-asserted-by":"publisher","unstructured":"Moore A, Orset B, Yassaee A, Irving B, Morelli D. HEalthRecordBERT (HERBERT): leveraging transformers on electronic health records for chronic kidney disease risk stratification. ACM Trans Comput Healthcare. 2024;5(3):18:1\u201318:18. https:\/\/doi.org\/10.1145\/3665899.","DOI":"10.1145\/3665899"},{"key":"3733_CR41","doi-asserted-by":"publisher","unstructured":"Lee Y, Kang D, Park S, Park SY, Kim K. Chronic kidney disease prognosis prediction using transformer. arXiv preprint. 2025. https:\/\/doi.org\/10.48550\/arXiv.2511.02340.","DOI":"10.48550\/arXiv.2511.02340"},{"issue":"10","key":"3733_CR42","doi-asserted-by":"publisher","first-page":"1024","DOI":"10.1001\/jama.2018.1150","volume":"319","author":"I Papanicolas","year":"2018","unstructured":"Papanicolas I, Woskie LR, Jha AK. Health care spending in the United States and other high-income countries. JAMA. 2018;319(10):1024\u201339. https:\/\/doi.org\/10.1001\/jama.2018.1150.","journal-title":"JAMA"},{"issue":"2","key":"3733_CR43","doi-asserted-by":"publisher","first-page":"112","DOI":"10.5455\/msm.2012.24.112-120","volume":"24","author":"G Ridic","year":"2012","unstructured":"Ridic G, Gleason S, Ridic O. Comparisons of health care systems in the United States, Germany and Canada. Mater Sociomed. 2012;24(2):112. https:\/\/doi.org\/10.5455\/msm.2012.24.112-120.","journal-title":"Mater Sociomed"},{"key":"3733_CR44","unstructured":"Schneider EC, Sarnak DO, Squires D, Shah A, Doty MM. Mirror, mirror 2017: international comparison reflects flaws and opportunities for better U.S. health care. Commonwealth Fund New York; 2017."},{"key":"3733_CR45","unstructured":"World Health Organization (WHO). International statistical classification of diseases and related health problems: alphabetical index. Vol. 3. World Health Organization; 2004."},{"key":"3733_CR46","unstructured":"Bundesinstitut f\u00fcr Arzneimittel und Medizinprodukte (BfArM) im Auftrag des Bundesministeriums f\u00fcr Gesundheit (BMG) unter Beteiligung der Arbeitsgruppe ICD des Kuratoriums f\u00fcr Fragen der Klassifikation im Gesundheitswesen (KKG). ICD-10-GM Version 2021, Systematisches Verzeichnis: Internationale statistische Klassifikation der Krankheiten und verwandter Gesundheitsprobleme, 10. Revision (version dated September 18, 2020). K\u00f6ln: BfArM; 2020."},{"issue":"10097","key":"3733_CR47","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1016\/S0140-6736(17)31280-1","volume":"390","author":"R Busse","year":"2017","unstructured":"Busse R, Bl\u00fcmel M, Knieps F, B\u00e4rnighausen T. Statutory health insurance in Germany: a health system shaped by 135 years of solidarity, self-governance, and competition. Lancet. 2017;390(10097):882\u201397. https:\/\/doi.org\/10.1016\/S0140-6736(17)31280-1.","journal-title":"Lancet"},{"key":"3733_CR48","unstructured":"World Health Organization (WHO). Anatomical therapeutic chemical classification. https:\/\/www.whocc.no\/atc\/structure_and_principles\/. Accessed 17 Jul 2025."},{"key":"3733_CR49","doi-asserted-by":"crossref","unstructured":"Wolf T, Debut L, Sanh V, Chaumond J, Delangue C, Moi A, et al. Transformers: state-of-the-art natural language processing. In: Liu Q, Schlangen D, editors. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Online: Association for Computational Linguistics; 2020. p. 38\u201345.","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"3733_CR50","doi-asserted-by":"publisher","unstructured":"Loshchilov I, Hutter F. Decoupled weight decay regularization. arXiv preprint. 2017. https:\/\/doi.org\/10.48550\/arXiv.1711.05101.","DOI":"10.48550\/arXiv.1711.05101"},{"issue":"11","key":"3733_CR51","doi-asserted-by":"publisher","first-page":"205","DOI":"10.21105\/joss.00205","volume":"2","author":"L McInnes","year":"2017","unstructured":"McInnes L, Healy J, Astels S. HDBSCAN: hierarchical density based clustering. J Open Source Softw. 2017;2(11):205. https:\/\/doi.org\/10.21105\/joss.00205.","journal-title":"JOSS"},{"key":"3733_CR52","unstructured":"Rosenberg A, Hirschberg J. V-measure: a conditional entropy-based external cluster evaluation measure. In: Eisner J, editor. Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL). Association for Computational Linguistics; 2007. p. 410\u201320."},{"key":"3733_CR53","doi-asserted-by":"publisher","unstructured":"Neuen BL, Yeung EK, Rangaswami J, Vaduganathan M. Combination therapy as a new standard of care in diabetic and non-diabetic chronic kidney disease. Nephrol Dial Transpl. 2025;40(Supplement_1):i59\u201369. https:\/\/doi.org\/10.1093\/ndt\/gfae258.","DOI":"10.1093\/ndt\/gfae258"},{"key":"3733_CR54","doi-asserted-by":"publisher","unstructured":"Nicholls SG, Langan SM, Benchimol EI. Routinely collected data: the importance of high-quality diagnostic coding to research. Cmaj. 2017;189(33):E1054\u201355. https:\/\/doi.org\/10.1503\/cmaj.170807.","DOI":"10.1503\/cmaj.170807"},{"key":"3733_CR55","unstructured":"Dr\u00f6sler S, Hasford J, Kurth BM, Schaefer M, Wasem J, Wille E. Evaluationsbericht zum Jahresausgleich 2009 im Risikostrukturausgleich. Bundesministerium f\u00fcr Gesundheit; 2011."},{"key":"3733_CR56","doi-asserted-by":"publisher","unstructured":"Martin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, et al. 2024 heart disease and stroke statistics: a report of US and global data from the American Heart Association. Circulation. 2024;149(8):e347\u2013913. https:\/\/doi.org\/10.1161\/CIR.0000000000001209.","DOI":"10.1161\/CIR.0000000000001209"},{"issue":"4","key":"3733_CR57","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1038\/kisup.2013.79","volume":"3","author":"R Kazancio\u011flu","year":"2013","unstructured":"Kazancio\u011flu R. Risk factors for chronic kidney disease: an update. Kidney Int Suppl. 2013;3(4):368\u201371. https:\/\/doi.org\/10.1038\/kisup.2013.79.","journal-title":"Kidney Int Suppl"},{"key":"3733_CR58","doi-asserted-by":"publisher","unstructured":"van Mil D, Kieneker L, van Geer-Postmus I, Prins G, Harms E, Stoker N, et al. #1966 participation rate and yield of screening for albuminuria in the primary care setting for early identification of chronic kidney disease (SALINE). Nephrol Dial Transpl. 2024;39(Supplement_1):gfae069\u20130039\u20131966. https:\/\/doi.org\/10.1093\/ndt\/gfae069.039.","DOI":"10.1093\/ndt\/gfae069.039"},{"key":"3733_CR59","unstructured":"Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, et al. LightGBM: a highly efficient gradient boosting decision tree. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, et al., editors. Advances in neural information processing systems. Vol. 30. Curran Associates, Inc.; 2017. p. 3149\u201357."},{"issue":"1","key":"3733_CR60","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1002\/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3","volume":"3","author":"WJ Youden","year":"1950","unstructured":"Youden WJ. Index for rating diagnostic tests. Cancer. 1950;3(1):32\u201335. https:\/\/doi.org\/10.1002\/1097-0142(1950)3:1%3C32::AID-CNCR2820030106%3E3.0.CO;2-3.","journal-title":"Cancer"},{"issue":"null","key":"3733_CR61","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12(null):2825\u201330.","journal-title":"J Mach Learn Res"},{"issue":"3","key":"3733_CR62","first-page":"61","volume":"10","author":"J Platt","year":"1999","unstructured":"Platt J. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in Large Margin Classifiers. 1999;10(3):61\u201374.","journal-title":"Advances In Large Margin Classifiers"},{"issue":"1","key":"3733_CR63","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1175\/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2","volume":"78","author":"GW Brier","year":"1950","unstructured":"Brier GW. Verification of forecasts expressed in terms of probability. Mon Weather Rev. 1950;78(1):1\u20133. https:\/\/doi.org\/10.1175\/1520-0493(1950)078%3C0001:VOFEIT%3E2.0.CO;2.","journal-title":"Mon. Wea. Rev"},{"issue":"7","key":"3733_CR64","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","volume":"30","author":"AP Bradley","year":"1997","unstructured":"Bradley AP. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit. 1997;30(7):1145\u201359. https:\/\/doi.org\/10.1016\/S0031-3203(96)00142-2.","journal-title":"Pattern Recognit"},{"issue":"30","key":"3733_CR65","first-page":"6","volume":"2","author":"M Zhu","year":"2004","unstructured":"Zhu M. Recall, precision and average precision. Department of Statistics and Actuarial Science, University of Waterloo, Waterloo. 2004;2(30):6.","journal-title":"Department Stat Actuarial Sci, Univ Waterloo, Waterloo"},{"issue":"1","key":"3733_CR66","doi-asserted-by":"publisher","first-page":"2449","DOI":"10.1038\/s41598-024-51522-9","volume":"14","author":"V Bali","year":"2024","unstructured":"Bali V, Turzhitsky V, Schelfhout J, Paudel M, Hulbert E, Peterson-Brandt J, et al. Machine learning to identify chronic cough from administrative claims data. Sci Rep. 2024;14(1):2449. https:\/\/doi.org\/10.1038\/s41598-024-51522-9.","journal-title":"Sci Rep"},{"issue":"1","key":"3733_CR67","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12882-018-1048-5","volume":"19","author":"GFC Weckmann","year":"2018","unstructured":"Weckmann GFC, Stracke S, Haase A, Spallek J, Ludwig F, Angelow A, et al. Diagnosis and management of non-dialysis chronic kidney disease in ambulatory care: a systematic review of clinical practice guidelines. BMC Nephrol. 2018;19(1):1\u201318. https:\/\/doi.org\/10.1186\/s12882-018-1048-5.","journal-title":"BMC Nephrol"},{"key":"3733_CR68","doi-asserted-by":"publisher","first-page":"1495054","DOI":"10.3389\/fpubh.2024.1495054","volume":"12","author":"Y Yoshizaki","year":"2024","unstructured":"Yoshizaki Y, Kato K, Fujihara K, Sone H, Akazawa K. Development of a machine learning tool to predict the risk of incident chronic kidney disease using health examination data. Front Public Health. 2024;12:1495054. https:\/\/doi.org\/10.3389\/fpubh.2024.1495054.","journal-title":"Front Public Health"},{"issue":"6894","key":"3733_CR69","doi-asserted-by":"publisher","first-page":"1722","DOI":"10.1136\/bmj.306.6894.1722","volume":"306","author":"K Borch-Johnsen","year":"1993","unstructured":"Borch-Johnsen K, Wenzel H, Viberti G, Mogensen C. Is screening and intervention for microalbuminuria worthwhile in patients with insulin dependent diabetes? BMJ. 1993;306(6894):1722\u201325. https:\/\/doi.org\/10.1136\/bmj.306.6894.1722.","journal-title":"BMJ"},{"issue":"23","key":"3733_CR70","doi-asserted-by":"publisher","first-page":"3101","DOI":"10.1001\/jama.290.23.3101","volume":"290","author":"LE Boulware","year":"2003","unstructured":"Boulware LE, Jaar BG, Tarver-Carr ME, Brancati FL, Powe NR. Screening for proteinuria in US adults: a cost-effectiveness analysis. JAMA. 2003;290(23):3101\u201314. https:\/\/doi.org\/10.1001\/jama.290.23.3101.","journal-title":"JAMA"},{"issue":"3","key":"3733_CR71","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1053\/j.ajkd.2009.11.017","volume":"55","author":"TJ Hoerger","year":"2010","unstructured":"Hoerger TJ, Wittenborn JS, Segel JE, Burrows NR, Imai K, Eggers P, et al. A health policy model of CKD: 2. The cost-effectiveness of microalbuminuria screening. Am J Kidney Dis. 2010;55(3):463\u201373. https:\/\/doi.org\/10.1053\/j.ajkd.2009.11.017.","journal-title":"Am J Kidney Dis"},{"key":"3733_CR72","doi-asserted-by":"publisher","first-page":"102414","DOI":"10.1016\/j.eclinm.2023.102414","volume":"68","author":"XGLV Pouwels","year":"2024","unstructured":"Pouwels XGLV, van Mil D, Kieneker LM, Boersma C, van Etten RW, Evers-Roeten B, et al. Cost-effectiveness of home-based screening of the general population for albuminuria to prevent progression of cardiovascular and kidney disease. EClinicalMedicine. 2024;68:102414. https:\/\/doi.org\/10.1016\/j.eclinm.2023.102414.","journal-title":"eClinicalmedicine"},{"issue":"3","key":"3733_CR73","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3641289","volume":"15","author":"Y Chang","year":"2024","unstructured":"Chang Y, Wang X, Wang J, Wu Y, Yang L, Zhu K, et al. A survey on evaluation of large language models. ACM Trans Intell Syst Technol. 2024;15(3):1\u201345. https:\/\/doi.org\/10.1145\/3641289.","journal-title":"ACM Trans Intell Syst Technol"},{"key":"3733_CR74","unstructured":"Meta AI. The Llama 4 herd: the beginning of a new era of natively multimodal AI innovation. https:\/\/ai.meta.com\/blog\/llama-4-multimodal-intelligence\/. Accessed 30 Apr 2026."},{"key":"3733_CR75","doi-asserted-by":"publisher","unstructured":"Yang A, Li A, Yang B, Zhang B, Hui B, Zheng B, et al. Qwen3 technical report. arXiv preprint. 2025. https:\/\/doi.org\/10.48550\/arXiv.2505.09388.","DOI":"10.48550\/arXiv.2505.09388"},{"key":"3733_CR76","unstructured":"Google DeepMind. Gemma 4: byte for byte, the most capable open models. https:\/\/blog.google\/innovation-and-ai\/technology\/developers-tools\/gemma-4\/. Accessed 30 Apr 2026."},{"key":"3733_CR77","doi-asserted-by":"publisher","unstructured":"Hu EJ, Shen Y, Wallis P, Allen-Zhu Z, Li Y, Wang S, et al. LoRA: low-rank adaptation of large language models. arXiv preprint. 2021. https:\/\/doi.org\/10.48550\/arXiv.2106.09685.","DOI":"10.48550\/arXiv.2106.09685"},{"issue":"2","key":"3733_CR78","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1055\/s-0034-1396815","volume":"77","author":"E Swart","year":"2015","unstructured":"Swart E, Gothe H, Geyer S, Jaunzeme J, Maier B, Grobe TG, et al. Good practice of secondary data analysis (GPS): guidelines and recommendations. Das Gesundheitswes. 2015;77(2):120\u201326. https:\/\/doi.org\/10.1055\/s-0034-1396815.","journal-title":"Das Gesundheitswes"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-026-03733-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-026-03733-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-026-03733-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T11:38:25Z","timestamp":1785929905000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12911-026-03733-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,5]]},"references-count":78,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["3733"],"URL":"https:\/\/doi.org\/10.1186\/s12911-026-03733-5","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,5]]},"assertion":[{"value":"4 May 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 August 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This retrospective study is a secondary data analysis based on statutory health insurance claims data originally collected for reimbursement purposes and provided in fully pseudonymized form. The analysis was conducted to predict health care-relevant outcomes with the aim of improving health care quality. Ethics approval and informed consent to participate were not required for this type of secondary data analysis according to the German Social Code (SGB X 80 in conjunction with SGB V 68c). The study was conducted in accordance with the relevant guidelines and regulations, including the national recommendations for Good Practice of Secondary Data Analysis [\n                      \n                      ].","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This work and the Central Research Institute of Ambulatory Health Care in Germany (Zi) are funded and contracted by the Associations of Statutory Health Insurance Physicians in the German Federal States. It is the purpose of Zi to support and further develop the health care assurance mandate under German law. FS reports receiving lecturing fees and travel support from Astra Zeneca GmbH, Chiesi GmbH and Lilly GmbH and is supported by the Medical Faculty of the Christian-Albrechts-University Kiel.","order":3,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"293"}}