title: [Artificial neural network in the prediction of nosocomial infection risk].
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.
  
  
  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.
authors.affiliation: Department of Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China.
authors.forename: Lin-yong
authors.initials: LY
authors.lastname: Xu
authors.forename: Yi
authors.initials: Y
authors.lastname: Bai
authors.forename: Ming
authors.initials: M
authors.lastname: Hu
authors.forename: Yong-yong
authors.initials: YY
authors.lastname: Xu
authors.forename: Zhen-qiu
authors.initials: ZQ
authors.lastname: Sun
mesh_terms: D003428:Cross Infection; D005260:Female; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors