{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T13:22:21Z","timestamp":1770297741821,"version":"3.49.0"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T00:00:00Z","timestamp":1754870400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T00:00:00Z","timestamp":1754870400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","award":["HI23C0896"],"award-info":[{"award-number":["HI23C0896"]}],"id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","award":["HR20C0026"],"award-info":[{"award-number":["HR20C0026"]}],"id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"DOI":"10.1186\/s12911-025-03145-x","type":"journal-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T09:55:18Z","timestamp":1754906118000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Leveraging BERT for embedding ICD codes from large scale cardiovascular EMR data to understand patient diagnostic patterns"],"prefix":"10.1186","volume":"25","author":[{"given":"Minkyoung","family":"Kim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunha","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hee Jun","family":"Kang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyeram","family":"Seo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heejung","family":"Choi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"JiYe","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gaeun","family":"Kee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soyoung","family":"Ko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"HyoJe","family":"Jung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Byeolhee","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boeun","family":"Choi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tae Joon","family":"Jun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Young-Hak","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,11]]},"reference":[{"key":"3145_CR1","doi-asserted-by":"crossref","unstructured":"Kim HS, Lee S, Kim JH. Real-world evidence versus randomized controlled trial: clinical research based on electronic medical records. J Korean Med Sci. 2018;33(34).","DOI":"10.3346\/jkms.2018.33.e213"},{"key":"3145_CR2","unstructured":"Hannan TJ. Electronic medical records. Health informatics: an overview. 1996. p. 133."},{"key":"3145_CR3","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.cmpb.2017.02.016","volume":"143","author":"C Zhao","year":"2017","unstructured":"Zhao C, Jiang J, Xu Z, Guan Y. A study of EMR-based medical knowledge network and its applications. Comput Methods Programs Biomed. 2017;143:13\u201323.","journal-title":"Comput Methods Programs Biomed"},{"key":"3145_CR4","doi-asserted-by":"publisher","first-page":"105117","DOI":"10.1016\/j.cmpb.2019.105117","volume":"184","author":"M Moradi","year":"2020","unstructured":"Moradi M, Dorffner G, Samwald M. Deep contextualized embeddings for quantifying the informative content in biomedical text summarization. Comput Methods Programs Biomed. 2020;184:105117.","journal-title":"Comput Methods Programs Biomed"},{"key":"3145_CR5","doi-asserted-by":"crossref","unstructured":"Yuan Z, Tan C, Huang S. Code synonyms do matter: multiple synonyms matching network for automatic ICD coding. arXiv preprint arXiv:2203.01515. 2022.","DOI":"10.18653\/v1\/2022.acl-short.91"},{"issue":"03","key":"3145_CR6","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.imed.2022.03.003","volume":"2","author":"C Yan","year":"2022","unstructured":"Yan C, Fu X, Liu X, Zhang Y, Gao Y, Wu J, Li Q. A survey of automated international classification of diseases coding: development, challenges, and applications. Intell Med. 2022;2(03):161\u201373.","journal-title":"Intell Med"},{"issue":"10","key":"3145_CR7","doi-asserted-by":"publisher","first-page":"e65","DOI":"10.1097\/MLR.0000000000000108","volume":"54","author":"B Polnaszek","year":"2016","unstructured":"Polnaszek B, Gilmore-Bykovskyi A, Hovanes M, Roiland R, Ferguson P, Brown R, Kind AJ. Overcoming the challenges of unstructured data in multisite, electronic medical record-based abstraction. Med Care. 2016;54(10):e65\u201372.","journal-title":"Med Care"},{"key":"3145_CR8","doi-asserted-by":"crossref","unstructured":"Sun W, Cai Z, Li Y, Liu F, Fang S, Wang G. Data processing and text mining technologies on electronic medical records: a review. J Healthc Eng. 2018;2018.","DOI":"10.1155\/2018\/4302425"},{"key":"3145_CR9","doi-asserted-by":"crossref","unstructured":"Si Y, Du J, Li Z, Jiang X, Miller T, Wang F, et al. Deep representation learning of patient data from electronic health records (EHR): a systematic review. J Biomed Inform. 2021;115:103671.","DOI":"10.1016\/j.jbi.2020.103671"},{"key":"3145_CR10","doi-asserted-by":"crossref","unstructured":"Xiang X, Duan S, Pan H, Han P, Cao J, Liu C. From one-hot encoding to privacy-preserving synthetic electronic health records embedding. In: Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies; 2020 Dec. p. 407\u2013413.","DOI":"10.1145\/3444370.3444605"},{"key":"3145_CR11","unstructured":"Devlin J. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. 2018."},{"key":"3145_CR12","unstructured":"Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, et al. Exploring the limits of transfer learning with a unified text-to-text transformer. J Mach Learn Res. 2020;21(140):1\u201367."},{"key":"3145_CR13","unstructured":"Brown TB. Language models are few-shot learners. arXiv preprint arXiv:2005.14165. 2020."},{"issue":"4","key":"3145_CR14","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2020","unstructured":"Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, Kang J. BioBERT: a pre-trained biomedical Language representation model for biomedical text mining. Bioinformatics. 2020;36(4):1234\u201340.","journal-title":"Bioinformatics"},{"key":"3145_CR15","doi-asserted-by":"crossref","unstructured":"Alsentzer E, Murphy JR, Boag W, Weng WH, Jin D, Naumann T, McDermott M. Publicly available clinical BERT embeddings. arXiv preprint arXiv:1904.03323. 2019.","DOI":"10.18653\/v1\/W19-1909"},{"key":"3145_CR16","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1016\/j.jbi.2014.07.007","volume":"52","author":"PL Peissig","year":"2014","unstructured":"Peissig PL, Costa VS, Caldwell MD, Rottscheit C, Berg RL, Mendonca EA, Page D. Relational machine learning for electronic health record-driven phenotyping. J Biomed Inform. 2014;52:260\u201370.","journal-title":"J Biomed Inform"},{"key":"3145_CR17","unstructured":"Mikolov T. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781. 2013."},{"key":"3145_CR18","unstructured":"Choi Y, Chiu CYI, Sontag D. Learning low-dimensional representations of medical concepts. AMIA Summits Transl Sci Proc. 2016;2016:41."},{"key":"3145_CR19","doi-asserted-by":"publisher","first-page":"65333","DOI":"10.1109\/ACCESS.2018.2875677","volume":"6","author":"J Zhang","year":"2018","unstructured":"Zhang J, Kowsari K, Harrison JH, Lobo JM, Barnes LE. Patient2vec: a personalized interpretable deep representation of the longitudinal electronic health record. IEEE Access. 2018;6:65333\u201346.","journal-title":"IEEE Access"},{"key":"3145_CR20","unstructured":"Sajjad H, Alam F, Dalvi F, Durrani N. Effect of post-processing on contextualized word representations. arXiv preprint arXiv:2104.07456. 2021."},{"key":"3145_CR21","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. Distributed representations of words and phrases and their compositionality. Adv Neural Inf Process Syst. 2013;26."},{"key":"3145_CR22","doi-asserted-by":"crossref","unstructured":"Huang K, Singh A, Chen S, Moseley ET, Deng CY, George N, Lindvall C. Clinical XLNet: Modeling sequential clinical notes and predicting prolonged mechanical ventilation. arXiv preprint arXiv:1912.11975. 2019.","DOI":"10.18653\/v1\/2020.clinicalnlp-1.11"},{"key":"3145_CR23","doi-asserted-by":"crossref","unstructured":"Li F, Jin Y, Liu W, Rawat BPS, Cai P, Yu H. Fine-tuning bidirectional encoder representations from transformers (BERT)\u2013based models on large-scale electronic health record notes: an empirical study. JMIR Med Inf. 2019;7(3):e14830.","DOI":"10.2196\/14830"},{"key":"3145_CR24","doi-asserted-by":"crossref","unstructured":"Gao S, Alawad M, Young MT, Gounley J, Schaefferkoetter N, Yoon HJ, et al. Limitations of transformers on clinical text classification. IEEE J Biomed Health Inform. 2021;25(9):3596\u20133607.","DOI":"10.1109\/JBHI.2021.3062322"},{"issue":"22","key":"3145_CR25","doi-asserted-by":"publisher","first-page":"11709","DOI":"10.3390\/app122211709","volume":"12","author":"J Xu","year":"2022","unstructured":"Xu J, Xi X, Chen J, Sheng VS, Ma J, Cui Z. A survey of deep learning for electronic health records. Appl Sci. 2022;12(22):11709.","journal-title":"Appl Sci"},{"key":"3145_CR26","doi-asserted-by":"crossref","unstructured":"Song H, Rajan D, Thiagarajan J, Spanias A. Attend and diagnose: Clinical time series analysis using attention models. In: Proceedings of the AAAI Conference on Artificial Intelligence; 2018 Apr;32(1).","DOI":"10.1609\/aaai.v32i1.11635"},{"key":"3145_CR27","doi-asserted-by":"crossref","unstructured":"Choi E, Xu Z, Li Y, Dusenberry M, Flores G, Xue E, Dai A. Learning the graphical structure of electronic health records with graph convolutional transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence; 2020 Apr;34(01):606\u2013613.","DOI":"10.1609\/aaai.v34i01.5400"},{"issue":"1","key":"3145_CR28","doi-asserted-by":"publisher","first-page":"1","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):1\u201313.","journal-title":"NPJ Digit Med"},{"key":"3145_CR29","doi-asserted-by":"crossref","unstructured":"Ahn I, Na W, Kwon O, Yang DH, Park GM, Gwon H, et al. CardioNet: a manually curated database for artificial intelligence-based research on cardiovascular diseases. BMC Med Inform Decis Mak. 2021;21:1\u201315.","DOI":"10.1186\/s12911-021-01392-2"},{"issue":"2","key":"3145_CR30","doi-asserted-by":"publisher","first-page":"102","DOI":"10.4258\/hir.2013.19.2.102","volume":"19","author":"SY Shin","year":"2013","unstructured":"Shin SY, Lyu Y, Shin Y, Choi HJ, Park J, Kim WS, Lee JH. Lessons learned from development of de-identification system for biomedical research in a Korean tertiary hospital. Healthc Inf Res. 2013;19(2):102\u20139.","journal-title":"Healthc Inf Res"},{"key":"3145_CR31","doi-asserted-by":"crossref","unstructured":"Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, et al. Scalable and accurate deep learning with electronic health records. NPJ Digit Med. 2018;1(1):18.","DOI":"10.1038\/s41746-018-0029-1"},{"key":"3145_CR32","unstructured":"Choi E, Bahadori MT, Schuetz A, Stewart WF, Sun J. Doctor AI: Predicting clinical events via recurrent neural networks. In: Machine Learning for Healthcare Conference; 2016 Dec. p. 301\u2013318. PMLR."},{"key":"3145_CR33","doi-asserted-by":"crossref","unstructured":"Pawar CS, Makwana A. Comparison of bert-base and GPT-3 for marathi text classification. In: Futuristic Trends in Networks and Computing Technologies: Select Proceedings of Fourth International Conference on FTNCT 2021. Singapore: Springer Nature Singapore; 2022 Nov. p. 563\u2013574.","DOI":"10.1007\/978-981-19-5037-7_40"},{"key":"3145_CR34","doi-asserted-by":"publisher","first-page":"104998","DOI":"10.1016\/j.compbiomed.2021.104998","volume":"139","author":"S Ji","year":"2021","unstructured":"Ji S, H\u00f6ltt\u00e4 M, Marttinen P. Does the magic of BERT apply to medical code assignment? A quantitative study. Comput Biol Med. 2021;139:104998.","journal-title":"Comput Biol Med"},{"key":"3145_CR35","unstructured":"Lan Z, Chen M, Goodman S, Gimpel K, Sharma P, Soricut R. Albert: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942. 2019."},{"key":"3145_CR36","doi-asserted-by":"crossref","unstructured":"Ethayarajh K. How contextual are contextualized word representations? Comparing the geometry of BERT, ELMo, and GPT-2 embeddings. arXiv preprint arXiv:1909.00512. 2019.","DOI":"10.18653\/v1\/D19-1006"},{"key":"3145_CR37","unstructured":"Llaquet SI, Sallinen A, Boye G, Zhang M, Dupont-Roc M, Bernath B, et al. Llama-Tree-Meditron [70B] [Master\u2019s thesis]. Universitat Polit\u00e8cnica de Catalunya; 2024."},{"key":"3145_CR38","doi-asserted-by":"crossref","unstructured":"Kim H, Hwang H, Lee J, Park S, Kim D, Lee T, et al. Small language models learn enhanced reasoning skills from medical textbooks. arXiv preprint arXiv:2404.00376. 2024.","DOI":"10.1038\/s41746-025-01653-8"},{"key":"3145_CR39","unstructured":"Chen Z, Cano AH, Romanou A, Bonnet A, Matoba K, Salvi F, et al. Meditron-70B: Scaling medical pretraining for large language models. arXiv preprint arXiv:2311.16079. 2023."},{"key":"3145_CR40","unstructured":"Wang L, Yang N, Huang X, Yang L, Majumder R, Wei F. Improving text embeddings with large language models. arXiv Preprint arXiv:240100368. 2023."},{"key":"3145_CR41","doi-asserted-by":"crossref","unstructured":"Peng Y, Yan S, Lu Z. Transfer learning in biomedical natural language processing: an evaluation of BERT and ELMo on ten benchmarking datasets. arXiv preprint arXiv:1906.05474. 2019.","DOI":"10.18653\/v1\/W19-5006"},{"key":"3145_CR42","doi-asserted-by":"publisher","first-page":"101139","DOI":"10.1016\/j.imu.2022.101139","volume":"36","author":"A Turchin","year":"2023","unstructured":"Turchin A, Masharsky S, Zitnik M. Comparison of BERT implementations for natural Language processing of narrative medical documents. Inf Med Unlocked. 2023;36:101139.","journal-title":"Inf Med Unlocked"},{"key":"3145_CR43","doi-asserted-by":"crossref","unstructured":"Beaney T, Jha S, Alaa A, Smith A, Clarke J, Woodcock T, et al. Comparing natural language processing representations of coded disease sequences for prediction in electronic health records. J Am Med Inform Assoc. 2024;31(7):1451\u20131462.","DOI":"10.1093\/jamia\/ocae091"},{"key":"3145_CR44","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C. Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2016 Aug. p. 785\u2013794.","DOI":"10.1145\/2939672.2939785"},{"key":"3145_CR45","doi-asserted-by":"crossref","unstructured":"Kwon O, Na W, Kang H, Jun TJ, Kweon J, Park GM, et al. Electronic medical record\u2013based machine learning approach to predict the risk of 30-day adverse cardiac events after invasive coronary treatment: Machine learning model development and validation. JMIR Med Inform. 2022;10(5):e26801.","DOI":"10.2196\/26801"},{"issue":"1","key":"3145_CR46","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","volume":"143","author":"JA Hanley","year":"1982","unstructured":"Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143(1):29\u201336.","journal-title":"Radiology"},{"key":"3145_CR47","unstructured":"Van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008;9(11)."},{"issue":"1","key":"3145_CR48","doi-asserted-by":"publisher","first-page":"31","DOI":"10.17849\/insm-47-01-31-39.1","volume":"47","author":"SJ Rigatti","year":"2017","unstructured":"Rigatti SJ. Random forest. J Insur Med. 2017;47(1):31\u20139.","journal-title":"J Insur Med"},{"key":"3145_CR49","doi-asserted-by":"crossref","unstructured":"Suthaharan S, Suthaharan S. Support vector machine. In: Machine learning models and algorithms for big data classification: Thinking with examples for effective learning. 2016. p. 207\u2013235.","DOI":"10.1007\/978-1-4899-7641-3_9"},{"issue":"18","key":"3145_CR50","doi-asserted-by":"publisher","first-page":"2395","DOI":"10.1161\/CIRCULATIONAHA.106.682658","volume":"117","author":"MP LaValley","year":"2008","unstructured":"LaValley MP. Logistic regression. Circulation. 2008;117(18):2395\u20139.","journal-title":"Circulation"},{"issue":"Suppl 6","key":"3145_CR51","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1186\/s12911-021-01539-1","volume":"21","author":"CG Chute","year":"2021","unstructured":"Chute CG, \u00c7elik C. Overview of ICD-11 architecture and structure. BMC Med Inf Decis Mak. 2021;21(Suppl 6):378.","journal-title":"BMC Med Inf Decis Mak"},{"key":"3145_CR52","doi-asserted-by":"crossref","unstructured":"Choi EJ, Jun TJ, Park HS, Lee JH, Lee KH, Kim YH, et al. Predicting long-term survival after allogeneic hematopoietic cell transplantation in patients with hematologic malignancies: Machine learning\u2013based model development and validation. JMIR Med Inform. 2022;10(3):e32313.","DOI":"10.2196\/32313"},{"key":"3145_CR53","doi-asserted-by":"crossref","unstructured":"Han J, Kim Y, Kang HJ, Seo J, Choi H, Kim M, et al. Predicting low density lipoprotein cholesterol target attainment using machine learning in patients with coronary artery disease receiving moderate-dose statin therapy. Sci Rep. 2025;15(1):5346.","DOI":"10.1038\/s41598-025-88693-y"},{"issue":"e1","key":"3145_CR54","doi-asserted-by":"publisher","first-page":"e11","DOI":"10.1136\/amiajnl-2013-001636","volume":"21","author":"D Lee","year":"2014","unstructured":"Lee D, de Keizer N, Lau F, Cornet R. Literature review of SNOMED CT use. J Am Med Inform Assoc. 2014;21(e1):e11\u20139.","journal-title":"J Am Med Inform Assoc"},{"key":"3145_CR55","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-019-1002-x","volume":"20","author":"D Zhang","year":"2020","unstructured":"Zhang D, Yin C, Zeng J, Yuan X, Zhang P. Combining structured and unstructured data for predictive models: a deep learning approach. BMC Med Inf Decis Mak. 2020;20:1\u201311.","journal-title":"BMC Med Inf Decis Mak"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-03145-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-025-03145-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-03145-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T20:17:34Z","timestamp":1757362654000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-025-03145-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,11]]},"references-count":55,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["3145"],"URL":"https:\/\/doi.org\/10.1186\/s12911-025-03145-x","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,11]]},"assertion":[{"value":"11 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was approved by the Institutional Review Board of Asan Medical Center (AMC) (approval no. 2021\u2009\u2212\u20090303) in accordance with the Declaration of Helsinki (2008). The requirement for informed consent was waived by the IRB because all data were fully anonymized within the Asan Biomedical Research Environment (ABLE) database. All study procedures were conducted in compliance with relevant guidelines and regulations.","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":"300"}}