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Eleven routine clinical features predict COVID-19 severity uncovered by machine learning of longitudinal measurements.
In Computational and structural biotechnology journal
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, Huang Shigao, Peng Minfei, Lv Dongqing, Zhang Chao, Zhao Haihong, Hong Luxiao, Zhou Zhehan, Chen Haixiao, Dong Xuejun, Tu Chunyu, Li Minghui, Zhu Yi, Chen Baofu, Li Stan Z, Guo Tiannan
ABG, arterial blood gas, APTT, activated partial thromboplastin time, AST, aspartate aminotransferase, AUC, area under the curve, BASO#, basophil counts, CFDA, China Food and Drug Administration, CK, creatine kinase, COVID-19, CRP, C-reactive protein, CT, computed tomography, ESR, erythrocyte sedimentation rate, GA, genetic algorithm, GGT, gamma glutamyl transpeptidase, HIS, hospital information system, LAC, lactate, LDH, lactate dehydrogenase, LOESS, locally estimated scatterplot smoothing, LOS, length of stay, Longitudinal dynamics, Machine learning, Mg, magnesium, NETs, neutrophil extracellular traps, NPV, negative predictive value, PCT, procalcitonin, PPV, positive predictive value, ROC, receiver operating characteristics, RT-PCR, reverse transcriptase -polymerase chain reaction, Routine clinical test, SARS-CoV-2, SHAP, SHapley Additive exPlanations, SVM, support vector machine, SaO2, oxygen saturation, Severity prediction, TT, thrombin time, eGFR, estimated glomerular filtration rate