Author: Zhao, Chun-Hong; Wu, Hui-Tao; Che, He-Bin; Song, Ya-Nan; Zhao, Yu-Zhuo; Li, Kai-Yuan; Xiao, Hong-Ju; Zhai, Yong-Zhi; Liu, Xin; Lu, Hong-Xi; Li, Tan-Shi
Title: Prediction of fatal adverse prognosis in patients with fever-related diseases based on machine learning: A retrospective study Document date: 2020_3_5
ID: tk3861u0_19
Snippet: We selected 70 clinical variables associated with "fever" as candidate indicators. It can be seen that in the model with 15 impact factors (HR, RR, SBP, DBP, SPO 2 , T, CK-MB, TBIL, LDH, AMY, LIP, CTnT, K, TP, and ALB) and in the model with 11 impact factors (RR, SPO 2 , T, Na, CL, CTnT, K, ALB, Ca, P, and RBC), the co-existing influence factors were: RR, SPO 2 , T, CTnT, K, and ALB. These factors could be the main clinical indicators of concern .....
Document: We selected 70 clinical variables associated with "fever" as candidate indicators. It can be seen that in the model with 15 impact factors (HR, RR, SBP, DBP, SPO 2 , T, CK-MB, TBIL, LDH, AMY, LIP, CTnT, K, TP, and ALB) and in the model with 11 impact factors (RR, SPO 2 , T, Na, CL, CTnT, K, ALB, Ca, P, and RBC), the co-existing influence factors were: RR, SPO 2 , T, CTnT, K, and ALB. These factors could be the main clinical indicators of concern in this experiment, which should be given clinical attention.
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