Author: Hui Yu; Jianbo Shao; Yuqi Guo; Yun Xiang; Chuan Sun; Ye Yuan
Title: Data-driven discovery of clinical routes for severity detection in COVID-19 pediatric cases Document date: 2020_3_10
ID: fm6lf66a_8
Snippet: Due to the scantiness of clinical data from confirmed COVID-19 children cases, especially for severe ones, it is an urgent yet challenging mission to promptly distinguish the severe ones from the mild cases for early diagnosis and intervention. To this end, with the assistance of machine learning methods, we identified that DBIL and ALT, surfacing from over 300 clinical features, were able to serve as a combination index to screen out all the cri.....
Document: Due to the scantiness of clinical data from confirmed COVID-19 children cases, especially for severe ones, it is an urgent yet challenging mission to promptly distinguish the severe ones from the mild cases for early diagnosis and intervention. To this end, with the assistance of machine learning methods, we identified that DBIL and ALT, surfacing from over 300 clinical features, were able to serve as a combination index to screen out all the critically ill cases. Although the increase of DBIL and ALT has been reported to reflect tissue destruction or injury, for the first time, their combination is revealed as a precise indicator for the severity of COVID-19 pediatric cases, which is quite different from the discovered clinical route for adult [4] .
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