In Scientific reports ; h5-index 158.0
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.
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