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AuthorIslam, Khandaker R.
AuthorKumar, Jaya
AuthorTan, Toh L.
AuthorReaz, Mamun B.
AuthorRahman, Tawsifur
AuthorKhandakar, Amith
AuthorAbbas, Tariq
AuthorHossain, Md. S.
AuthorZughaier, Susu M.
AuthorChowdhury, Muhammad E. H.
Available date2023-04-17T06:57:42Z
Publication Date2022
Publication NameDiagnostics
ResourceScopus
URIhttp://dx.doi.org/10.3390/diagnostics12092144
URIhttp://hdl.handle.net/10576/41945
AbstractWith the onset of the COVID-19 pandemic, the number of critically sick patients in intensive care units (ICUs) has increased worldwide, putting a burden on ICUs. Early prediction of ICU requirement is crucial for efficient resource management and distribution. Early-prediction scoring systems for critically ill patients using mathematical models are available, but are not generalized for COVID-19 and Non-COVID patients. This study aims to develop a generalized and reliable prognostic model for ICU admission for both COVID-19 and non-COVID-19 patients using best feature combination from the patient data at admission. A retrospective cohort study was conducted on a dataset collected from the pulmonology department of Moscow City State Hospital between 20 April 2020 and 5 June 2020. The dataset contains ten clinical features for 231 patients, of whom 100 patients were transferred to ICU and 131 were stable (non-ICU) patients. There were 156 COVID positive patients and 75 non-COVID patients. Different feature selection techniques were investigated, and a stacking machine learning model was proposed and compared with eight different classification algorithms to detect risk of need for ICU admission for both COVID-19 and non-COVID patients combined and COVID patients alone. C-reactive protein (CRP), chest computed tomography (CT), lung tissue affected (%), age, admission to hospital, and fibrinogen parameters at hospital admission were found to be important features for ICU-requirement risk prediction. The best performance was produced by the stacking approach, with weighted precision, sensitivity, F1-score, specificity, and overall accuracy of 84.45%, 84.48%, 83.64%, 84.47%, and 84.48%, respectively, for both types of patients, and 85.34%, 85.35%, 85.11%, 85.34%, and 85.35%, respectively, for COVID-19 patients only. The proposed work can help doctors to improve management through early prediction of the risk of need for ICU admission of patients during the COVID-19 pandemic, as the model can be used for both types of patients. 2022 by the authors.
SponsorThis work was supported by the Faculty of Medicine, Universiti Kebangsaan Malaysia (UKM), and UKM Grant Number DIP-2020-004, Grant Number GP-2020-K017701, and by the Qatar National Research fund under Grant UREP28-144-3-046. The statements made herein are solely the responsibility of the authors.
Languageen
PublisherMDPI
Subjectclinical biomarkers
COVID-19
early prediction
intensive care unit
machine learning
TitlePrognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning
TypeArticle
Issue Number9
Volume Number12
dc.accessType Open Access


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