Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph
Richard Du, Efstratios Tsougenis, Joshua W. K. Ho, Joyce Ka Yin Chan, K.W. Chiu, Benjamin Fang, Ming‐Yen Ng, Siu-Ting Leung, Christine Shing-Yen Lo, Ho-Yuen Frank Wong, Hiu-Yin S. Lam, Long-Fung J. Chiu, Tiffany Y. So, Ka Tak Wong, Yiu Chung Wong, Kevin Yu, Yiu-cheong Yeung, Thomas Shiu Hong Chik, Joanna W. K. Pang, Abraham Ka Chung Wai, Michael Kuo, Tina Lam, Pek‐Lan Khong, Ngai-Tseung Cheung, Varut Vardhanabhuti
Abstract
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.