Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph

• Published in: Scientific reports Vol. 11; no. 1; p. 14250
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 09.07.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 639/705; 692/53; 692/700; Adolescent; Adult; Biomarkers - blood; Blood tests; Chest; Coronaviruses; COVID-19; COVID-19 - blood; COVID-19 - diagnostic imaging; COVID-19 Testing; Female; Humanities and Social Sciences; Humans; Infections; Laboratories; Learning algorithms; Machine Learning; Male; Middle Aged; Models, Biological; multidisciplinary; Patients; Polymerase chain reaction; Predictive Value of Tests; Radiography; Retrospective Studies; SARS-CoV-2 - metabolism; Science; Science (multidisciplinary); Severe acute respiratory syndrome coronavirus 2; Statistical analysis; Thorax - diagnostic imaging; Hong Kong China; China
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-93719-2

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.