[Artificial neural network in the prediction of nosocomial infection risk].
OBJECTIVE
To establish a model based on artificial neural network in the prediction of nosocomial infection risk.


METHODS
Clinical data of 27,352 inpatients extracted from hospital information system were cleaned and coded, and the model of prediction in nosocomial infection risk was developed based on artificial neural network.


RESULTS
The structure of artificial neural network is {16-6-1}-BP, and the fit rate of prediction was 0.9891. The area under ROC curve was 0.986.


CONCLUSIONS
Artificial neural network model can be used as a tool for nosocomial infection forecasting, which can provide supplementary information for the diagnosis and control of nosocomial infection.
Xu
Lin-yong
LY
Department of Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China.
Bai
Yi
Y
Hu
Ming
M
Xu
Yong-yong
YY
Sun
Zhen-qiu
ZQ
D003428:Cross Infection; D005260:Female; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors