title: [Prediction of the length of stay in Intensive Care Units using artificial neural networks].
abstract: BACKGROUND
  The prediction of the length of stay at the moment of hospital admission is of utmost importance. Many studies have used lineal models to predict this variable, but there are inherent limitations to these models. The use of non lineal models has been scarce.
  
  
  OBJECTIVE
  To develop a non lineal model to predict length of stay in intensive care units.
  
  
  METHODS
  Retrospective analysis of 294 patients admitted to two intensive care units in Santiago, Chile. The severity of the disease motivating the admission was nominally quantified. This and other physiological variables were included in the model. The length of stay was modeled using Artificial Neural Networks.
  
  
  RESULTS
  The model yielded an error of 8.7% (3.6 +/- 0.4 days, CI 95%) and a correlation coefficient of 0.9 (p < 0.001) for the prediction of length of stay. Using net sensitivity analysis, the model determined that gastrointestinal diseases, infections and respiratory problems were the main causes of prolongation of intensive care unit stay.
  
  
  CONCLUSIONS
  Intensive care units should have, in the future, computer systems that gather vital information to predict length of stay.
authors.affiliation: Departamento de Ingeniería Informática, Universidad de Santiago de Chile. mchacon@diinf.usach.cl
authors.forename: Max
authors.initials: M
authors.lastname: Chacón
authors.forename: Víctor
authors.initials: V
authors.lastname: Rocco
authors.forename: Enrique
authors.initials: E
authors.lastname: Morgado
authors.forename: Enzo
authors.initials: E
authors.lastname: Sáez
authors.forename: Sergio
authors.initials: S
authors.lastname: Pliscoff
mesh_terms: D005260:Female; D006801:Humans; D007362:Intensive Care Units; D007902:Length of Stay; D008297:Male; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D012680:Sensitivity and Specificity