Predicting severe respiratory failure in patients with covid-19: a machine learning approach

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dc.contributor.authorCeylan, Bahadır
dc.contributor.authorOlmuşçelik, Oktay
dc.contributor.authorKaraalioğlu, Banu
dc.contributor.authorŞahin, Meyha
dc.contributor.authorAydın, Selda
dc.contributor.authorYılmaz, Ezgi
dc.contributor.authorDumlu, Rıdvan
dc.contributor.authorKapmaz, Mahir
dc.contributor.authorÇiçek, Yeliz
dc.contributor.authorKansu, Abdullah
dc.contributor.authorDüger, Mustafa
dc.contributor.authorMert, Ali
dc.date.accessioned2025-12-05T18:07:31Z
dc.date.available2025-12-05T18:07:31Z
dc.date.issued2024
dc.departmentİstanbul Medipol Üniversitesi, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümü, Enfeksiyon Hastalıkları ve Klinik Mikrobiyoloji Ana Bilim Dalı
dc.departmentİstanbul Medipol Üniversitesi, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümü, Göğüs Hastalıkları Ana Bilim Dalı
dc.departmentİstanbul Medipol Üniversitesi, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümü, İç Hastalıkları Ana Bilim Dalı
dc.departmentİstanbul Medipol Üniversitesi, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümü, Radyoloji Ana Bilim Dalı
dc.description.abstractBackground/Objectives: Studies attempting to predict the development of severe respiratory failure in patients with a COVID-19 infection using machine learning algorithms have yielded different results due to differences in variable selection. We aimed to predict the development of severe respiratory failure, defined as the need for high-flow oxygen support, continuous positive airway pressure, or mechanical ventilation, in patients with COVID-19, using machine learning algorithms to identify the most important variables in achieving this prediction. Methods: This retrospective, cross-sectional study included COVID-19 patients with mild respiratory failure (mostly receiving oxygen through a mask or nasal cannula). We used XGBoost, support vector machines, multi-layer perceptron, k-nearest neighbor, random forests, decision trees, logistic regression, and naïve Bayes methods to accurately predict severe respiratory failure in these patients. Results: A total of 320 patients (62.1% male; average age, 54.67 ± 15.82 years) were included in this study. During the follow-ups of these cases, 114 patients (35.6%) required high-level oxygen support, 67 (20.9%) required intensive care unit admission, and 43 (13.4%) died. The machine learning algorithms with the highest accuracy values were XGBoost, support vector machines, k-nearest neighbor, logistic regression, and multi-layer perceptron (0.7395, 0.7395, 0.7291, 0.7187, and 0.75, respectively). The method that obtained the highest ROC-AUC value was logistic regression (ROC-AUC = 0.7274). The best predictors of severe respiratory failure were a low lymphocyte count, a high computed tomography score in the right and left upper lung zones, an elevated neutrophil count, a small decrease in CRP levels on the third day of admission, a high Charlson comorbidity index score, and a high serum procalcitonin level. Conclusions: The development of severe respiratory failure in patients with COVID-19 could be successfully predicted using machine learning methods, especially logistic regression, and the best predictors of severe respiratory failure were the lymphocyte count and the degree of upper lung zone involvement.
dc.identifier.citationCeylan, B., Olmuşçelik, O., Karaalioğlu, B., Şahin, M., Aydın, S., Yılmaz, E. ... Mert, A. (2024). Predicting severe respiratory failure in patients with covid-19: a machine learning approach. Journal of Clinical Medicine, 13(23). http://dx.doi.org/10.3390/jcm13237386
dc.identifier.doi10.3390/jcm13237386
dc.identifier.issn2077-0383
dc.identifier.issue23
dc.identifier.pmid39685844
dc.identifier.scopus2-s2.0-85211937848
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.3390/jcm13237386
dc.identifier.urihttps://hdl.handle.net/20.500.12511/13296
dc.identifier.volume13
dc.identifier.wosWOS:001376567600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorCeylan, Bahadır
dc.institutionauthorOlmuşçelik, Oktay
dc.institutionauthorKaraalioğlu, Banu
dc.institutionauthorŞahin, Meyha
dc.institutionauthorAydın, Selda
dc.institutionauthorYılmaz, Ezgi
dc.institutionauthorDumlu, Rıdvan
dc.institutionauthorKapmaz, Mahir
dc.institutionauthorÇiçek, Yeliz
dc.institutionauthorKansu, Abdullah
dc.institutionauthorDüger, Mustafa
dc.institutionauthorMert, Ali
dc.institutionauthorid0000-0001-9658-7560
dc.institutionauthorid0000-0002-9815-1848
dc.institutionauthorid0000-0003-4147-3587
dc.institutionauthorid0000-0002-3131-442X
dc.institutionauthorid0000-0002-4181-5674
dc.institutionauthorid0000-0001-8213-6064
dc.institutionauthorid0000-0002-4115-3914
dc.institutionauthorid0000-0002-4091-6465
dc.language.isoen
dc.relation.ispartofJournal of Clinical Medicine
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCOVID-19
dc.subjectMachine Learning
dc.subjectPrognosis
dc.titlePredicting severe respiratory failure in patients with covid-19: a machine learning approach
dc.typeArticle

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