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

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 ana...

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Autores principales:	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
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f01d34f4f45d4097bfbec59ffa2e34af
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spelling	oai:doaj.org-article:f01d34f4f45d4097bfbec59ffa2e34af2021-12-02T15:23:08ZMachine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph10.1038/s41598-021-93719-22045-2322https://doaj.org/article/f01d34f4f45d4097bfbec59ffa2e34af2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93719-2https://doaj.org/toc/2045-2322Abstract 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.Richard DuEfstratios D. TsougenisJoshua W. K. HoJoyce K. Y. ChanKeith W. H. ChiuBenjamin X. H. FangMing Yen NgSiu-Ting LeungChristine S. Y. LoHo-Yuen F. WongHiu-Yin S. LamLong-Fung J. ChiuTiffany Y SoKa Tak WongYiu Chung I. WongKevin YuYiu-Cheong YeungThomas ChikJoanna W. K. PangAbraham Ka-chung WaiMichael D. KuoTina P. W. LamPek-Lan KhongNgai-Tseung CheungVarut VardhanabhutiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q 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 Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph
description	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.
format	article
author	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
author_facet	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
author_sort	Richard Du
title	Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph
title_short	Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph
title_full	Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph
title_fullStr	Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph
title_full_unstemmed	Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph
title_sort	machine learning application for the prediction of sars-cov-2 infection using blood tests and chest radiograph
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f01d34f4f45d4097bfbec59ffa2e34af
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Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph

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