The invention discloses a
cytarabine syndrome risk prediction model, a training method thereof and a
system adopting the same. The model training method comprises the following steps: constructing an
artificial intelligence model; wherein the input of the
artificial intelligence model comprises the weight and the heating duration of the to-be-evaluated object; the output of the
artificial intelligence model is the
risk probability that the to-be-evaluated object belongs to the
cytarabine syndrome fever;
large sample data are adopted to
train the artificial intelligence model, cases with fever of the
cytarabine syndrome are positive samples, and other fever cases are negative samples. According to the present invention, the identification standard of the cytarabine syndrome fever and the infected fever is established, and the
risk probability of the cytarabine syndrome of the object to be evaluated can be evaluated by analyzing the difference of the two detection results in the laboratory, such that the unreasonable use of the
antibacterial agent is reduced, the medical cost is reduced, and the
drug resistance risk of the
antibacterial agent is reduced.