Artificial neural network in the prediction of nosocomial infection risk.
- Author:
Lin-yong XU
1
;
Yi BAI
;
Ming HU
;
Yong-yong XU
;
Zhen-qiu SUN
Author Information
1. Department of Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China.
- Publication Type:Journal Article
- MeSH:
Cross Infection;
Female;
Humans;
Male;
Neural Networks, Computer;
Risk Assessment;
methods;
Risk Factors
- From:
Journal of Central South University(Medical Sciences)
2006;31(3):404-407
- CountryChina
- Language:Chinese
-
Abstract:
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.
CONCLUSION: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.
