Details

Autor(en) / Beteiligte

• 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

Titel

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

Ist Teil von

• Scientific reports, 2021-07, Vol.11 (1), p.14250-14250, Article 14250

Ort / Verlag

London: Nature Publishing Group UK

Erscheinungsjahr

2021

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.