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- Title
Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph.
- 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
- 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.
- Subjects
MACHINE learning; BLOOD testing; REVERSE transcriptase polymerase chain reaction; SARS-CoV-2; COVID-19 pandemic; ETIOLOGY of pneumonia
- Publication
Scientific Reports, 2021, Vol 11, Issue 1, p1
- ISSN
2045-2322
- Publication type
Article
- DOI
10.1038/s41598-021-93719-2