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In Computational and structural biotechnology journal

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.

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

2021

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