{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T23:46:58Z","timestamp":1777852018578,"version":"3.51.4"},"reference-count":51,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Health Informatics J"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:p>\n                    <jats:bold>Objective:<\/jats:bold>\n                    COVID-19 disease has a high prevalence and mortality rate among chronic kidney disease patients. Previous studies have shown that ML-based prediction models are effective for developing preventive strategies. Despite several efforts to establish prediction models for COVID-19 mortality risk among different populations, few studies have focused on COVID-19 mortality among patients with chronic kidney disease. The current study aimed to develop an effective, efficient preventive strategy to improve prognosis and survival among these patients by constructing a machine-learning-based prediction model.\n                    <jats:bold>Methods:<\/jats:bold>\n                    The current retrospective study used single-center data from 556 hospitalized patients with CKD. All patients in the cohort had respiratory failure following COVID-19. We leveraged ensemble and non-ensemble algorithms to construct a prediction model and scored the chosen features for better interpretability.\n                    <jats:bold>Results:<\/jats:bold>\n                    The empirical results of this study showed that XG-Boost achieved an AU-ROC of 0.921 with a 95% CI of [0.906\u20130.941] and an AU-ROC of 0.851 with a 95% CI of [0.835\u20130.877] in training and validation modes, respectively, yielding more favourable predictive performance than the models.\n                    <jats:bold>Conclusion:<\/jats:bold>\n                    XG-Boost demonstrated predictive merit that can be leveraged as an auxiliary tool to aid clinicians in making more informed decisions in clinical settings.\n                  <\/jats:p>","DOI":"10.1177\/14604582261425071","type":"journal-article","created":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T10:05:35Z","timestamp":1775210735000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting mortality risk of COVID-19 among chronic kidney disease patients using machine learning algorithms"],"prefix":"10.1177","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3770-2375","authenticated-orcid":false,"given":"Raoof","family":"Nopour","sequence":"first","affiliation":[{"name":"Department of Health Information Management, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2026,4,3]]},"reference":[{"key":"e_1_3_7_2_2","doi-asserted-by":"publisher","DOI":"10.1002\/jmv.25689"},{"key":"e_1_3_7_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijantimicag.2020.105924"},{"key":"e_1_3_7_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sjbs.2020.04.033"},{"key":"e_1_3_7_5_2","doi-asserted-by":"publisher","DOI":"10.4269\/ajtmh.20-0564"},{"issue":"3","key":"e_1_3_7_6_2","first-page":"157","article-title":"International public health responses to COVID-19 outbreak: a rapid review","volume":"45","author":"Tabari P","year":"2020","unstructured":"Tabari P, Amini M, Moghadami M, et al. International public health responses to COVID-19 outbreak: a rapid review. 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Management recommendations for patients with chronic kidney disease during the novel coronavirus disease 2019 (COVID-19) epidemic. Chronic Dis Transl Med 2020; 06(02): 119\u2013123.","journal-title":"Chronic Dis Transl Med"},{"key":"e_1_3_7_21_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0254525"},{"key":"e_1_3_7_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.kint.2020.03.005"},{"key":"e_1_3_7_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11255-020-02740-3"},{"key":"e_1_3_7_24_2","doi-asserted-by":"publisher","DOI":"10.1093\/ndt\/gfaa271"},{"key":"e_1_3_7_25_2","doi-asserted-by":"crossref","unstructured":"Mart\u00edn CZ-S. Telenephrology: a resource for universalizing access to kidney care perspectives from Latin America. In: Bezerra da Silva Junior G. Nangaku M. (eds) Innovations in nephrology: breakthrough technologies in kidney disease care. 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