title: Predicting ICU mortality: a comparison of stationary and nonstationary temporal models.
abstract: OBJECTIVE
  This study evaluates the effectiveness of the stationarity assumption in predicting the mortality of intensive care unit (ICU) patients at the ICU discharge.
  
  
  METHODS
  This is a comparative study. A stationary temporal Bayesian network learned from data was compared to a set of (33) nonstationary temporal Bayesian networks learned from data. A process observed as a sequence of events is stationary if its stochastic properties stay the same when the sequence is shifted in a positive or negative direction by a constant time parameter. The temporal Bayesian networks forecast mortalities of patients, where each patient has one record per day. The predictive performance of the stationary model is compared with nonstationary models using the area under the receiver operating characteristics (ROC) curves.
  
  
  RESULTS
  The stationary model usually performed best. However, one nonstationary model using large data sets performed significantly better than the stationary model.
  
  
  CONCLUSIONS
  Results suggest that using a combination of stationary and nonstationary models may predict better than using either alone.
authors.affiliation: Center for Biomedical Informatics, Intelligent Systems Program, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. kayaalp@acm.org, gfc@cbmi.upmc.edu
authors.forename: M
authors.initials: M
authors.lastname: Kayaalp
authors.forename: G F
authors.initials: GF
authors.lastname: Cooper
authors.forename: G
authors.initials: G
authors.lastname: Clermont
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010351:Patient Discharge; D011237:Predictive Value of Tests; D011379:Prognosis; D012372:ROC Curve; D013997:Time Factors