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Du, Richard; Tsougenis, Efstratios D; Ho, Joshua W K; Chan, Joyce K Y; Chiu, Keith W H; Fang, Benjamin X H; Ng, Ming Yen; Leung, Siu-Ting; Lo, Christine S Y; Wong, Ho-Yuen F; Lam, Hiu-Yin S; Chiu, Long-Fung J; So, Tiffany Y; Wong, Ka Tak; Wong, Yiu Chung I; Yu, Kevin; Yeung, Yiu-Cheong; Chik, Thomas; Pang, Joanna W K; Wai, Abraham Ka-Chung; Kuo, Michael D; Lam, Tina P W; Khong, Pek-Lan; Cheung, Ngai-Tseung; Vardhanabhuti, Varut
Scientific reports, 07/2021, Letnik: 11, Številka: 1Journal Article
Triaging and prioritising patients for RT-PCR test had been essential in the management of COVID-19 in resource-scarce countries. In this study, we applied machine learning (ML) to the task of detection of SARS-CoV-2 infection using basic laboratory markers. We performed the statistical analysis and trained an ML model on a retrospective cohort of 5148 patients from 24 hospitals in Hong Kong to classify COVID-19 and other aetiology of pneumonia. We validated the model on three temporal validation sets from different waves of infection in Hong Kong. For predicting SARS-CoV-2 infection, the ML model achieved high AUCs and specificity but low sensitivity in all three validation sets (AUC: 89.9-95.8%; Sensitivity: 55.5-77.8%; Specificity: 91.5-98.3%). When used in adjunction with radiologist interpretations of chest radiographs, the sensitivity was over 90% while keeping moderate specificity. Our study showed that machine learning model based on readily available laboratory markers could achieve high accuracy in predicting SARS-CoV-2 infection.
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in: SICRIS
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