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...

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Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8225590/
https://www.ncbi.nlm.nih.gov/pubmed/34188785
http://dx.doi.org/10.1016/j.csbj.2021.06.022

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8225590/
https://www.ncbi.nlm.nih.gov/pubmed/34188785
http://dx.doi.org/10.1016/j.csbj.2021.06.022

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