title: Using an Artificial Neural Networks (ANNs) Model for Prediction of Intensive Care Unit (ICU) Outcome and Length of Stay at Hospital in Traumatic Patients.
abstract: BACKGROUND
  Currently applications of artificial neural network (ANN) models in outcome predicting of patients have made considerable strides in clinical medicine. This project aims to use a neural network for predicting survival and length of stay of patients in the ward and the intensive care unit (ICU) of trauma patients and to obtain predictive power of the current method.
  
  
  METHODS
  We used Neuro-Solution software (NS), a leading-edge neural network software for data mining to create highly accurate and predictive models using advanced preprocessing techniques, intelligent automated neural network topology through cutting-edge distributed computing. This ANN model was used based on back-propagation, feed forward, and fed by Trauma and injury severity score (TRISS) components, biochemical findings, risk factors and outcome of 95 patients. In the next step a trained ANN was used to predict outcome, ICU and ward length of stay for 30 test group patients by processing primary data.
  
  
  RESULTS
  The sensitivity and specificity of an ANN for predicting the outcome of traumatic patients in this study calculated 75% and 96.26%, respectively. 93.33% of outcome predictions obtained by ANN were correct. In 3.33% of predictions, results of ANN were optimistic and 3.33% of cases predicted ANN results were worse than the actual outcome of patients. Neither difference in average length of stay in the ward and ICU with predicted ANN results, were statistically significant. Correlation coefficient of two variables of ANN prediction and actual length of stay in hospital was equal to 0.643.
  
  
  CONCLUSIONS
  Using ANN model based on clinical and biochemical variables in patients with moderate to severe traumatic injury, resulted in satisfactory outcome prediction when applied to a test set.
authors.affiliation: Associate Professor, Department of General Surgery, Tabriz University of Medical Sciences , Tabriz, Iran .
authors.forename: Changiz
authors.initials: C
authors.lastname: Gholipour
authors.affiliation: PhD in molecular, Hearing Research Center, Ahvaz Jundishapur University of Medical Science , Ahvaz, Iran .
authors.forename: Fakher
authors.initials: F
authors.lastname: Rahim
authors.affiliation: Assistant Professor, Department of General Surgery, Tabriz University of Medical Sciences , Tabriz, Iran .
authors.forename: Abolghasem
authors.initials: A
authors.lastname: Fakhree
authors.affiliation: Assistant Professor, Department of Emergency Medicine, Ahvaz Jundishapur University of Medical Sciences , Ahvaz, Iran .
authors.forename: Behrad
authors.initials: B
authors.lastname: Ziapour
keywords: Diagnostic value; Information technology; Trauma