{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T11:55:49Z","timestamp":1777550149583,"version":"3.51.4"},"reference-count":40,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Korea Health Technology R&D Project through the Korea Health Industry Development Institute","award":["HI19C0481, HC20C0040"],"award-info":[{"award-number":["HI19C0481, HC20C0040"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["DIGITAL HEALTH"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Delirium is commonly reported from the inpatients with Coronavirus disease 2019 (COVID-19) infection. As delirium is closely associated with adverse clinical outcomes, prediction and prevention of delirium is critical. We developed a machine learning (ML) model to predict delirium in hospitalized patients with COVID-19 and to identify modifiable factors to prevent delirium.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The data set (n\u2009=\u2009878) from four medical centers was constructed. Total of 78 predictors were included such as demographic characteristics, vital signs, laboratory results and medication, and the primary outcome was delirium occurrence during hospitalization. For analysis, the extreme gradient boosting (XGBoost) algorithm was applied, and the most influential factors were selected by recursive feature elimination. Among the indicators of performance for ML model, the area under the curve of the receiver operating characteristic (AUROC) curve was selected as the evaluation metric.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Regarding the performance of developed delirium prediction model, the accuracy, precision, recall, F1 score, and the AUROC were calculated (0.944, 0.581, 0.421, 0.485, 0.873, respectively). The influential factors of delirium in this model included were mechanical ventilation, medication (antipsychotics, sedatives, ambroxol, piperacillin\/tazobactam, acetaminophen, ceftriaxone, and propacetamol), and sodium ion concentration (all p\u2009&lt;\u20090.05).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We developed and internally validated an ML model to predict delirium in COVID-19 inpatients. The model identified modifiable factors associated with the development of delirium and could be clinically useful for the prediction and prevention of delirium in COVID-19 inpatients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076231223811","type":"journal-article","created":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T04:28:27Z","timestamp":1704428907000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting delirium and the effects of medications in hospitalized COVID-19 patients using machine learning: A retrospective study within the Korean Multidisciplinary Cohort for Delirium Prevention (KoMCoDe)"],"prefix":"10.1177","volume":"10","author":[{"given":"So Hee","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Psychiatry, National Medical Center, Seoul, \rSouth Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyun Jung","family":"Hur","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sung Nyun","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Seoul Medical Center, Seoul, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jang Ho","family":"Ahn","sequence":"additional","affiliation":[{"name":"Seoul National University College of Medicine, Seoul, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Du Hyun","family":"Ro","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery, Seoul National University Hospital, Seoul, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arum","family":"Hong","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hye Yoon","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Seoul National University Hospital, Seoul National University College of Medicine, Seongnam, \rSouth Korea"},{"name":"Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pyoeng Gyun","family":"Choe","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seongnam, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6368-0221","authenticated-orcid":false,"given":"Back","family":"Kim","sequence":"additional","affiliation":[{"name":"Seoul National University College of Medicine, Seoul, South 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