{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T09:53:59Z","timestamp":1753869239070,"version":"3.41.2"},"reference-count":52,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T00:00:00Z","timestamp":1725235200000},"content-version":"vor","delay-in-days":1,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Health Science Reports"],"published-print":{"date-parts":[[2024,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background &amp; Aim<\/jats:title><jats:p>Timely identification of the patients requiring intensive care unit admission (ICU) could be life\u2010saving. We aimed to compare different machine learning algorithms to predict the requirements for ICU admission in COVID\u201019 patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We screened all patients with COVID\u201019 at six academic hospitals in Tehran comprising our study population. A total of 44,112 COVID\u201019 patients (\u226518 years old) were included, among which 7722 patients were hospitalized. We used a Random Forest algorithm to select significant variables. Then, prediction models were developed using the Support Vector Machine, Na\u0131ve Bayes, logistic regression, lightGBM, decision tree, and K\u2010Nearest Neighbor algorithms. Sensitivity, specificity, accuracy, F1 score, and receiver operating characteristic\u2010Area Under the Curve (AUC) were used to compare the prediction performance of different models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Based on random Forest, the following predictors were selected: age, cardiac disease, cough, hypertension, diabetes, influenza &amp; pneumonia, malignancy, and nervous system disease. Age was found to have the strongest association with ICU admission among COVID\u201019 patients. All six models achieved an AUC greater than 0.60. Na\u0131ve Bayes achieved the best predictive performance (AUC\u2009=\u20090.71).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Na\u00efve Bayes and lightGBM demonstrated promising results in predicting ICU admission needs in COVID\u201019 patients. Machine learning models could help quickly identify high\u2010risk patients upon entry and reduce mortality and morbidity among COVID\u201019 patients.<\/jats:p><\/jats:sec>","DOI":"10.1002\/hsr2.70041","type":"journal-article","created":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T04:48:50Z","timestamp":1725338930000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine learning approaches to predict the need for\u00a0intensive care unit\u00a0admission among Iranian COVID\u201019 patients based on ICD\u201010: A cross\u2010sectional study"],"prefix":"10.1002","volume":"7","author":[{"given":"Zahra","family":"Karimi","sequence":"first","affiliation":[{"name":"Department of Epidemiology and Biostatistics, School of Public Health Tehran University of Medical Sciences Tehran Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaleh S.","family":"Malak","sequence":"additional","affiliation":[{"name":"Department of Digital Health, School of Medicine Tehran University of Medical Sciences Tehran Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1804-6034","authenticated-orcid":false,"given":"Amirhossein","family":"Aghakhani","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, School of Public Health Tehran University of Medical Sciences Tehran Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5041-6798","authenticated-orcid":false,"given":"Mohammad S.","family":"Najafi","sequence":"additional","affiliation":[{"name":"Tehran Heart Center, Cardiovascular Diseases Research Institute Tehran University of Medical Sciences Tehran Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hamid","family":"Ariannejad","sequence":"additional","affiliation":[{"name":"Tehran Heart Center, Cardiovascular Diseases Research Institute Tehran University of Medical Sciences Tehran Iran"},{"name":"Department of Artificial Intelligence in Medical Sciences, Faculty of Advanced Technologies in Medicine Iran University of Medical Sciences Tehran Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hojjat","family":"Zeraati","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, School of Public Health Tehran University of Medical Sciences Tehran Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mir S.","family":"Yekaninejad","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, School of Public Health Tehran University of Medical Sciences Tehran Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,9,2]]},"reference":[{"key":"e_1_2_11_2_1","unstructured":"Covid\u201019 Cases dashboard. 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