1
|
Du R, Tsougenis ED, Ho JWK, Chan JKY, Chiu KWH, Fang BXH, Ng MY, Leung ST, Lo CSY, Wong HYF, Lam HYS, Chiu LFJ, So TY, Wong KT, Wong YCI, Yu K, Yeung YC, Chik T, Pang JWK, Wai AKC, Kuo MD, Lam TPW, Khong PL, Cheung NT, Vardhanabhuti V. Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph. Sci Rep 2021; 11:14250. [PMID: 34244563 PMCID: PMC8270945 DOI: 10.1038/s41598-021-93719-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Accepted: 06/21/2021] [Indexed: 01/08/2023] Open
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.
Collapse
Affiliation(s)
- Richard Du
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
- Artificial Intelligence Lab, Head Office Information Technology and Health Informatics Division, Hospital Authority, Hong Kong, SAR, China
| | - Efstratios D Tsougenis
- Artificial Intelligence Lab, Head Office Information Technology and Health Informatics Division, Hospital Authority, Hong Kong, SAR, China
| | - Joshua W K Ho
- The School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | - Joyce K Y Chan
- Clinical Systems, Information Technology and Health Informatics Division, Hospital Authority, Hong Kong, SAR, China
| | - Keith W H Chiu
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | | | - Ming Yen Ng
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
- Department of Medical Imaging, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Siu-Ting Leung
- Department of Radiology, Pamela Youde Nethersole Eastern Hospital, Hong Kong, SAR, China
| | - Christine S Y Lo
- Department of Radiology, Hong Kong Sanatorium & Hospital, Hong Kong, SAR, China
| | - Ho-Yuen F Wong
- Department of Radiology, Queen Mary Hospital, Hong Kong, SAR, China
| | - Hiu-Yin S Lam
- Department of Radiology, Queen Mary Hospital, Hong Kong, SAR, China
| | - Long-Fung J Chiu
- Department of Radiology and Imaging, Queen Elizabeth Hospital, Hong Kong, SAR, China
| | - Tiffany Y So
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Ka Tak Wong
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, Hong Kong, SAR, China
| | - Yiu Chung I Wong
- Department of Radiology, Tuen Muen Hospital, Hong Kong, SAR, China
| | - Kevin Yu
- Department of Radiology, Tuen Muen Hospital, Hong Kong, SAR, China
| | - Yiu-Cheong Yeung
- Department of Medicine, Princess Margaret Hospital, Hong Kong, SAR, China
| | - Thomas Chik
- Department of Medicine, Princess Margaret Hospital, Hong Kong, SAR, China
| | - Joanna W K Pang
- Health Informatics, Information Technology and Health Informatics Division, Hospital Authority, Hong Kong, SAR, China
| | - Abraham Ka-Chung Wai
- Emergency Medicine Unit, Li Ka Shing, Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Michael D Kuo
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | - Tina P W Lam
- Department of Radiology, Queen Mary Hospital, Hong Kong, SAR, China
| | - Pek-Lan Khong
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | - Ngai-Tseung Cheung
- Information Technology and Health Informatics Division, Hospital Authority, Hong Kong, SAR, China
| | - Varut Vardhanabhuti
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China.
| |
Collapse
|