| Autor: |
Richard Du, Efstratios D. Tsougenis, Joshua W. K. Ho, Joyce K. Y. Chan, Keith W. H. Chiu, Benjamin X. H. Fang, Ming Yen Ng, Siu-Ting Leung, Christine S. Y. Lo, Ho-Yuen F. Wong, Hiu-Yin S. Lam, Long-Fung J. Chiu, Tiffany Y So, Ka Tak Wong, Yiu Chung I. Wong, Kevin Yu, Yiu-Cheong Yeung, Thomas Chik, Joanna W. K. Pang, Abraham Ka-chung Wai, Michael D. Kuo, Tina P. W. Lam, Pek-Lan Khong, Ngai-Tseung Cheung, Varut Vardhanabhuti |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Scientific Reports, Vol 11, Iss 1, Pp 1-13 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2045-2322 |
| DOI: |
10.1038/s41598-021-93719-2 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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