{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T07:20:00Z","timestamp":1784704800098,"version":"3.55.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"KSA of KAIST with funds from the Ministry of Science and ICT"},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["2022R1A5A1033624"],"award-info":[{"award-number":["2022R1A5A1033624"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-27277-2","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T11:46:32Z","timestamp":1765194392000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["COVID-19 severity analysis for clinical decision support based on machine learning approach"],"prefix":"10.1038","volume":"15","author":[{"given":"Jung Eun","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tobhin","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerardo","family":"Chowell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hee-Sung","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sunmi","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,8]]},"reference":[{"key":"27277_CR1","unstructured":"Kwon, S., Lee, H., Ki, M., Chung, D. W. & Baris, E. Republic of Korea\u2019s Covid-19 Preparedness and Response (World Bank Group Discussion Paper, World Bank, 2020)."},{"key":"27277_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.onehlt.2020.100137","volume":"10","author":"M Her","year":"2020","unstructured":"Her, M. Repurposing and reshaping of hospitals during the covid-19 outbreak in South Korea. One Health 10, 100137 (2020).","journal-title":"One Health"},{"key":"27277_CR3","unstructured":"Jeong, S.-J. et al. Severity of covid-19 associated with SARS-COV-2 variants dominant period in the Republic of Korea. In Public Health Weekly Report (2023)."},{"key":"27277_CR4","unstructured":"Korea Disease Control and Prevention Agency. Coronavirus disease 2019 response and management guidelines for local governments in South Korea. 13-3rd ed (2023). https:\/\/dportal.kdca.go.kr\/pot\/component\/resrce\/file\/ND_resrceFileDownload.do?dataSn=163&chgCycl=1&fileSn=1  (accessed 24 Jul 2024)."},{"key":"27277_CR5","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1056\/NEJMsa053993","volume":"355","author":"JM Kahn","year":"2006","unstructured":"Kahn, J. M. et al. Hospital volume and the outcomes of mechanical ventilation. N. Engl. J. Med. 355, 41\u201350 (2006).","journal-title":"N. Engl. J. Med."},{"key":"27277_CR6","doi-asserted-by":"publisher","first-page":"4329","DOI":"10.1038\/s41598-022-07890-1","volume":"12","author":"M Chieregato","year":"2022","unstructured":"Chieregato, M. et al. A hybrid machine learning\/deep learning covid-19 severity predictive model from ct images and clinical data. Sci. Rep. 12, 4329 (2022).","journal-title":"Sci. Rep."},{"key":"27277_CR7","doi-asserted-by":"publisher","DOI":"10.2196\/24572","volume":"9","author":"JC Quiroz","year":"2021","unstructured":"Quiroz, J. C. et al. Development and validation of a machine learning approach for automated severity assessment of covid-19 based on clinical and imaging data: retrospective study. JMIR Med. Inform. 9, e24572 (2021).","journal-title":"JMIR Med. Inform."},{"key":"27277_CR8","doi-asserted-by":"publisher","DOI":"10.2196\/21604","volume":"8","author":"D Li","year":"2020","unstructured":"Li, D. et al. Prediction of covid-19 severity using chest computed tomography and laboratory measurements: evaluation using a machine learning approach. JMIR Med. Inform. 8, e21604 (2020).","journal-title":"JMIR Med. Inform."},{"key":"27277_CR9","doi-asserted-by":"publisher","DOI":"10.2196\/25884","volume":"9","author":"S Aktar","year":"2021","unstructured":"Aktar, S. et al. Machine learning approach to predicting covid-19 disease severity based on clinical blood test data: statistical analysis and model development. JMIR Med. Inform. 9, e25884 (2021).","journal-title":"JMIR Med. Inform."},{"key":"27277_CR10","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1186\/s40249-022-00946-4","volume":"11","author":"Y Xiong","year":"2022","unstructured":"Xiong, Y. et al. Comparing different machine learning techniques for predicting covid-19 severity. Infect. Dis. Poverty 11, 19 (2022).","journal-title":"Infect. Dis. Poverty"},{"key":"27277_CR11","doi-asserted-by":"publisher","first-page":"135697","DOI":"10.1109\/ACCESS.2021.3116067","volume":"9","author":"SA-F Sayed","year":"2021","unstructured":"Sayed, S.A.-F., Elkorany, A. M. & Mohammad, S. S. Applying different machine learning techniques for prediction of covid-19 severity. IEEE Access 9, 135697\u2013135707 (2021).","journal-title":"IEEE Access"},{"key":"27277_CR12","doi-asserted-by":"publisher","first-page":"31","DOI":"10.3390\/computers10030031","volume":"10","author":"A Alotaibi","year":"2021","unstructured":"Alotaibi, A., Shiblee, M. & Alshahrani, A. Prediction of severity of covid-19-infected patients using machine learning techniques. Computers 10, 31 (2021).","journal-title":"Computers"},{"key":"27277_CR13","doi-asserted-by":"publisher","first-page":"683","DOI":"10.3389\/fcell.2020.00683","volume":"8","author":"H Yao","year":"2020","unstructured":"Yao, H. et al. Severity detection for the coronavirus disease 2019 (covid-19) patients using a machine learning model based on the blood and urine tests. Front. Cell Dev. Biol. 8, 683 (2020).","journal-title":"Front. Cell Dev. Biol."},{"key":"27277_CR14","doi-asserted-by":"publisher","first-page":"4673","DOI":"10.1038\/s41598-021-83967-7","volume":"11","author":"D Patel","year":"2021","unstructured":"Patel, D. et al. Machine learning based predictors for covid-19 disease severity. Sci. Rep. 11, 4673 (2021).","journal-title":"Sci. Rep."},{"key":"27277_CR15","doi-asserted-by":"crossref","unstructured":"Gull, H., Krishna, G., Aldossary, M.\u00a0I. & Iqbal, S.\u00a0Z. Severity prediction of covid-19 patients using machine learning classification algorithms: A case study of small city in Pakistan with minimal health facility. In 2020 IEEE 6th International Conference on Computer and Communications (ICCC), 1537\u20131541 (IEEE, 2020).","DOI":"10.1109\/ICCC51575.2020.9344984"},{"key":"27277_CR16","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s40537-021-00557-0","volume":"9","author":"M Laatifi","year":"2022","unstructured":"Laatifi, M. et al. Machine learning approaches in covid-19 severity risk prediction in Morocco. J. Big Data 9, 5 (2022).","journal-title":"J. Big Data"},{"key":"27277_CR17","first-page":"1","volume":"2021","author":"SS Aljameel","year":"2021","unstructured":"Aljameel, S. S., Khan, I. U., Aslam, N., Aljabri, M. & Alsulmi, E. S. Machine learning-based model to predict the disease severity and outcome in covid-19 patients. Sci. Program. 2021, 1\u201310 (2021).","journal-title":"Sci. Program."},{"key":"27277_CR18","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1186\/s12911-023-02132-4","volume":"23","author":"G Raman","year":"2023","unstructured":"Raman, G. et al. Machine learning prediction for covid-19 disease severity at hospital admission. BMC Med. Inform. Decis. Mak. 23, 46 (2023).","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"27277_CR19","doi-asserted-by":"publisher","first-page":"1007205","DOI":"10.3389\/fpubh.2022.1007205","volume":"10","author":"S Hwangbo","year":"2022","unstructured":"Hwangbo, S. et al. Machine learning models to predict the maximum severity of covid-19 based on initial hospitalization record. Front. Public Health 10, 1007205 (2022).","journal-title":"Front. Public Health"},{"key":"27277_CR20","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0240200","volume":"16","author":"M Marcos","year":"2021","unstructured":"Marcos, M. et al. Development of a severity of disease score and classification model by machine learning for hospitalized covid-19 patients. PLoS One 16, e0240200 (2021).","journal-title":"PLoS One"},{"key":"27277_CR21","doi-asserted-by":"publisher","first-page":"52","DOI":"10.3390\/technologies9030052","volume":"9","author":"MM Ahsan","year":"2021","unstructured":"Ahsan, M. M., Mahmud, M. P., Saha, P. K., Gupta, K. D. & Siddique, Z. Effect of data scaling methods on machine learning algorithms and model performance. Technologies 9, 52 (2021).","journal-title":"Technologies"},{"key":"27277_CR22","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N. V., Bowyer, K. W., Hall, L. O. & Kegelmeyer, W. P. Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002).","journal-title":"J. Artif. Intell. Res."},{"key":"27277_CR23","doi-asserted-by":"crossref","unstructured":"Ray, S. A quick review of machine learning algorithms. In 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), 35\u201339 (IEEE, 2019).","DOI":"10.1109\/COMITCon.2019.8862451"},{"key":"27277_CR24","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","volume":"114","author":"M Belgiu","year":"2016","unstructured":"Belgiu, M. & Dr\u0103gu\u0163, L. Random forest in remote sensing: A review of applications and future directions. ISPRS J. Photogramm. Remote. Sens. 114, 24\u201331 (2016).","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"27277_CR25","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1038\/nbt1206-1565","volume":"24","author":"WS Noble","year":"2006","unstructured":"Noble, W. S. What is a support vector machine?. Nat. Biotechnol. 24, 1565\u20131567 (2006).","journal-title":"Nat. Biotechnol."},{"key":"27277_CR26","unstructured":"Ke, G. et al. Lightgbm: A highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst. 30 (2017)."},{"key":"27277_CR27","doi-asserted-by":"crossref","unstructured":"Chen, T. & Guestrin, C. Xgboost: a scalable tree boosting system proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining; 2016, 785\u2013794 (ACM, 2016).","DOI":"10.1145\/2939672.2939785"},{"key":"27277_CR28","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.inffus.2004.04.008","volume":"6","author":"D Ruta","year":"2005","unstructured":"Ruta, D. & Gabrys, B. Classifier selection for majority voting. Inf. Fusion 6, 63\u201381 (2005).","journal-title":"Inf. Fusion"},{"key":"27277_CR29","first-page":"61","volume":"13","author":"IK Nti","year":"2021","unstructured":"Nti, I. K. et al. Performance of machine learning algorithms with different k values in k-fold cross-validation. Int. J. Inf. Technol. Comput. Sci. 13, 61\u201371 (2021).","journal-title":"Int. J. Inf. Technol. Comput. Sci."},{"key":"27277_CR30","unstructured":"Scikit-learn: Machine learning in python (2011). https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.model_selection.GridSearchCV.html (accessed 22 Dec 2023)."},{"key":"27277_CR31","unstructured":"Powers, D.\u00a0M. Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation. arXiv preprint arXiv:2010.16061 (2020)."},{"key":"27277_CR32","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1007\/s40484-016-0081-2","volume":"4","author":"Y Jiao","year":"2016","unstructured":"Jiao, Y. & Du, P. Performance measures in evaluating machine learning based bioinformatics predictors for classifications. Quant. Biol. 4, 320\u2013330 (2016).","journal-title":"Quant. Biol."},{"key":"27277_CR33","unstructured":"Scikit-learn: Machine learning in python (2011). https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.inspection.permutation_importance.html (accessed 22 Dec 2023)."},{"key":"27277_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.exger.2021.111507","volume":"154","author":"M Farshbafnadi","year":"2021","unstructured":"Farshbafnadi, M., Zonouzi, S. K., Sabahi, M., Dolatshahi, M. & Aarabi, M. H. Aging & covid-19 susceptibility, disease severity, and clinical outcomes: The role of entangled risk factors. Exp. Gerontol. 154, 111507 (2021).","journal-title":"Exp. Gerontol."},{"key":"27277_CR35","doi-asserted-by":"publisher","first-page":"689","DOI":"10.2217\/cer-2022-0027","volume":"11","author":"R Chaturvedi","year":"2022","unstructured":"Chaturvedi, R., Lui, B., Aaronson, J. A., White, R. S. & Samuels, J. D. Covid-19 complications in males and females: recent developments. J. Comp. Effect. Res. 11, 689\u2013698 (2022).","journal-title":"J. Comp. Effect. Res."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-27277-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-27277-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-27277-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T00:04:15Z","timestamp":1765584255000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-27277-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,8]]},"references-count":35,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["27277"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-27277-2","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,8]]},"assertion":[{"value":"16 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 December 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":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"43327"}}