Severity Detection for the Coronavirus Disease 2019 (COVID-19) Patients Using a Machine Learning Model Based on the Blood and Urine Tests

被引:71
作者
Yao, Haochen [1 ]
Zhang, Nan [2 ]
Zhang, Ruochi [3 ,4 ]
Duan, Meiyu [3 ,4 ]
Xie, Tianqi [5 ]
Pan, Jiahui [1 ]
Peng, Ejun [6 ]
Huang, Juanjuan [1 ]
Zhang, Yingli [2 ]
Xu, Xiaoming [2 ]
Xu, Hong [2 ]
Zhou, Fengfeng [3 ,4 ]
Wang, Guoqing [1 ]
机构
[1] Jilin Univ, Dept Pathogenobiol, Coll Basic Med Sci, Key Lab Zoonosis,Chinese Minist Educ, Changchun, Peoples R China
[2] Jilin Univ, First Hosp Jilin Univ, Changchun, Peoples R China
[3] Jilin Univ, Coll Software, BioKnow Hlth Informat Lab, Minist Educ, Changchun, Peoples R China
[4] Jilin Univ, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun, Peoples R China
[5] Univ Pittsburgh, Sch Comp & Informat, Pittsburgh, PA USA
[6] Huazhong Univ Sci & Technol, Tongji Hosp, Tongji Med Coll, Wuhan, Peoples R China
基金
中国国家自然科学基金;
关键词
severity detection; COVID-19; model; blood and urine tests; biomarkers; PNEUMONIA; IDENTIFICATION; DIAGNOSIS; WUHAN;
D O I
10.3389/fcell.2020.00683
中图分类号
Q2 [细胞生物学];
学科分类号
071009 ; 090102 ;
摘要
The recent outbreak of the coronavirus disease-2019 (COVID-19) caused serious challenges to the human society in China and across the world. COVID-19 induced pneumonia in human hosts and carried a highly inter-person contagiousness. The COVID-19 patients may carry severe symptoms, and some of them may even die of major organ failures. This study utilized the machine learning algorithms to build the COVID-19 severeness detection model. Support vector machine (SVM) demonstrated a promising detection accuracy after 32 features were detected to be significantly associated with the COVID-19 severeness. These 32 features were further screened for inter-feature redundancies. The final SVM model was trained using 28 features and achieved the overall accuracy 0.8148. This work may facilitate the risk estimation of whether the COVID-19 patients would develop the severe symptoms. The 28 COVID-19 severeness associated biomarkers may also be investigated for their underlining mechanisms how they were involved in the COVID-19 infections.
引用
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页数:10
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