Eleven routine clinical features predict COVID-19 severity uncovered by machine learning of longitudinal measurements

Eleven routine clinical features predict COVID-19 severity uncovered by machine learning of longitudinal measurements

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 p...

Full description

Bibliographic Details
Main Authors: Kai Zhou, Yaoting Sun, Lu Li, Zelin Zang, Jing Wang, Jun Li, Junbo Liang, Fangfei Zhang, Qiushi Zhang, Weigang Ge, Hao Chen, Xindong Sun, Liang Yue, Xiaomai Wu, Bo Shen, Jiaqin Xu, Hongguo Zhu, Shiyong Chen, Hai Yang, Shigao Huang, Minfei Peng, Dongqing Lv, Chao Zhang, Haihong Zhao, Luxiao Hong, Zhehan Zhou, Haixiao Chen, Xuejun Dong, Chunyu Tu, Minghui Li, Yi Zhu, Baofu Chen, Stan Z. Li, Tiannan Guo
Format: Article
Language:English
Published: Elsevier 2021-01-01
Series:Computational and Structural Biotechnology Journal
Subjects:
COVID-19
SARS-CoV-2
Severity prediction
Machine learning
Routine clinical test
Longitudinal dynamics
Online Access:http://www.sciencedirect.com/science/article/pii/S2001037021002609

Internet

http://www.sciencedirect.com/science/article/pii/S2001037021002609

Similar Items