title: The use of artificial neural networks to stratify the length of stay of cardiac patients based on preoperative and initial postoperative factors.
abstract: BACKGROUND
  The limitations of current prognostic models in identifying postoperative cardiac patients at risk of experiencing morbidity and subsequently an extended intensive care unit length of stay (ICU LOS) is well recognized. This coupled with the desire for risk stratification in order to prioritize medical intervention has lead to the need for the development of a system that can accurately predict individual patient outcome based on both preoperative and immediate postoperative clinical factors. The usefulness of artificial neural networks (ANNs) as an outcome prediction tool in the critical care environment has been previously demonstrated for medical intensive care unit (ICU) patients and it is the aim of this study to apply this methodology to postoperative cardiac patients.
  
  
  METHODS
  A review of contemporary literature revealed 15 preoperative risk factors and 17 operative and postoperative variables that have a determining effect on LOS. An integrated, multi-functional software package was developed to automate the ANN development process. The efficacy of the resultant individual ANNs as well as groupings or ensembles of ANNs were measured by calculating sensitivity and specificity estimates as well as the area under the receiver operating curve (AUC) when the ANN is applied to an independent test dataset.
  
  
  RESULTS
  The individual ANN with the highest discriminating ability produced an AUC of 0.819. The use of the ensembles of networks technique significantly improved the classification accuracy. Consolidating the output of three ANNs improved the AUC to 0.90.
  
  
  CONCLUSIONS
  This study demonstrates the suitability of ANNs, in particular ensembles of ANNs, to outcome prediction tasks in postoperative cardiac patients.
authors.affiliation: Department of Medical Physics and Bioengineering, St. James's Hospital, James's St, Dublin 8, Ireland. mrowan@mater.ie
authors.forename: Michael
authors.initials: M
authors.lastname: Rowan
authors.forename: Thomas
authors.initials: T
authors.lastname: Ryan
authors.forename: Francis
authors.initials: F
authors.lastname: Hegarty
authors.forename: Neil
authors.initials: N
authors.lastname: O'Hare
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D006348:Cardiac Surgical Procedures; D005260:Female; D006801:Humans; D007362:Intensive Care Units; D007902:Length of Stay / Q000706:statistics & numerical data*; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011182:Postoperative Care / Q000706:statistics & numerical data*; D011300:Preoperative Care / Q000706:statistics & numerical data*; D018570:Risk Assessment