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Eleven routine clinical features predict COVID-19 severity uncovered by machine learning of longitudinal measurements
Zhou, Kai1; Sun, Yaoting2,3,4; Li, Lu2,3,4; Zang, Zelin5; Wang, Jing1; Li, Jun1; Liang, Junbo1; Zhang, Fangfei2,3,4; Zhang, Qiushi6; Ge, Weigang6; Chen, Hao6; Sun, Xindong2,3,4; Yue, Liang2,3,4; Wu, Xiaomai1; Shen, Bo1; Xu, Jiaqin1; Zhu, Hongguo1; Chen, Shiyong1; Yang, Hai1; Huang, Shigao7; Peng, Minfei1; Lv, Dongqing1; Zhang, Chao1; Zhao, Haihong1; Hong, Luxiao1; Zhou, Zhehan1; Chen, Haixiao1; Dong, Xuejun8; Tu, Chunyu8; Li, Minghui8; Zhu, Yi2,3,4; Chen, Baofu1; Li, Stan Z.5; Guo, Tiannan2,3,4
2021-06
Source PublicationComputational and Structural Biotechnology Journal
ISSN2001-0370
Volume19Pages:3640-3649
Abstract

Severity prediction of COVID-19 remains one of the major clinical challenges for the ongoing pandemic. Here, we have recruited a 144 COVID-19 patient cohort, resulting in a data matrix containing 3,065 readings for 124 types of measurements over 52 days. A machine learning model was established to predict the disease progression based on the cohort consisting of training, validation, and internal test sets. A panel of eleven routine clinical factors constructed a classifier for COVID-19 severity prediction, achieving accuracy of over 98% in the discovery set. Validation of the model in an independent cohort containing 25 patients achieved accuracy of 80%. The overall sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.70, 0.99, 0.93, and 0.93, respectively. Our model captured predictive dynamics of lactate dehydrogenase (LDH) and creatine kinase (CK) while their levels were in the normal range. This model is accessible at https://www.guomics.com/covidAI/ for research purpose.

KeywordCovid-19 Longitudinal Dynamics Machine Learning Routine Clinical Test Sars-cov-2 Severity Prediction
DOI10.1016/j.csbj.2021.06.022
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaBiochemistry & Molecular Biology ; Biotechnology & Applied Microbiology
WOS SubjectBiochemistry & Molecular Biology ; Biotechnology & Applied Microbiology
WOS IDWOS:000684845300008
Scopus ID2-s2.0-85108592595
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Cited Times [WOS]:12   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorZhu, Yi; Chen, Baofu; Li, Stan Z.; Guo, Tiannan
Affiliation1.Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, 150 Ximen Street, 317000, China
2.Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
3.Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
4.Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, 18 Shilongshan Road, 310024, China
5.School of Engineering, Westlake University, Hangzhou, 18 Shilongshan Road, 310024, China
6.Westlake Omics (Hangzhou) Biotechnology Co., Ltd., Hangzhou, 310024, China
7.Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau SAR, 999078, China
8.Shaoxing People's Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, 312000, China
Recommended Citation
GB/T 7714
Zhou, Kai,Sun, Yaoting,Li, Lu,et al. Eleven routine clinical features predict COVID-19 severity uncovered by machine learning of longitudinal measurements[J]. Computational and Structural Biotechnology Journal,2021,19:3640-3649.
APA Zhou, Kai.,Sun, Yaoting.,Li, Lu.,Zang, Zelin.,Wang, Jing.,Li, Jun.,Liang, Junbo.,Zhang, Fangfei.,Zhang, Qiushi.,Ge, Weigang.,Chen, Hao.,Sun, Xindong.,Yue, Liang.,Wu, Xiaomai.,Shen, Bo.,Xu, Jiaqin.,Zhu, Hongguo.,Chen, Shiyong.,Yang, Hai.,...&Guo, Tiannan.(2021).Eleven routine clinical features predict COVID-19 severity uncovered by machine learning of longitudinal measurements.Computational and Structural Biotechnology Journal,19,3640-3649.
MLA Zhou, Kai,et al."Eleven routine clinical features predict COVID-19 severity uncovered by machine learning of longitudinal measurements".Computational and Structural Biotechnology Journal 19(2021):3640-3649.
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