title: Predicting ICU survival: a meta-level approach.
abstract: BACKGROUND
  The performance of separate Intensive Care Unit (ICU) status scoring systems vis-à-vis prediction of outcome is not satisfactory. Computer-based predictive modeling techniques may yield good results but their performance has seldom been extensively compared to that of other mature or emerging predictive models. The objective of the present study was twofold: to propose a prototype meta-level predicting approach concerning Intensive Care Unit (ICU) survival and to evaluate the effectiveness of typical mining models in this context.
  
  
  METHODS
  Data on 158 men and 46 women, were used retrospectively (75% of the patients survived). We used Glasgow Coma Scale (GCS), Acute Physiology And Chronic Health Evaluation II (APACHE II), Sequential Organ Failure Assessment (SOFA) and Injury Severity Score (ISS) values to structure a decision tree (DTM), a neural network (NNM) and a logistic regression (LRM) model and we evaluated the assessment indicators implementing Receiver Operating Characteristics (ROC) plot analysis.
  
  
  RESULTS
  Our findings indicate that regarding the assessment of indicators' capacity there are specific discrete limits that should be taken into account. The Az score +/- SE was 0.8773+/- 0.0376 for the DTM, 0.8061+/- 0.0427 for the NNM and 0.8204+/- 0.0376 for the LRM, suggesting that the proposed DTM achieved a near optimal Az score.
  
  
  CONCLUSIONS
  The predicting processes of ICU survival may go "one step forward", by using classic composite assessment indicators as variables.
authors.affiliation: Telemedicine Unit, School of Medicine, University of Patras, Patras, Greece. gortzis@med.upatras.gr
authors.forename: Lefteris G
authors.initials: LG
authors.lastname: Gortzis
authors.forename: Filippos
authors.initials: F
authors.lastname: Sakellaropoulos
authors.forename: Ioannis
authors.initials: I
authors.lastname: Ilias
authors.forename: Konstantinos
authors.initials: K
authors.lastname: Stamoulis
authors.forename: Ioanna
authors.initials: I
authors.lastname: Dimopoulou
mesh_terms: D018806:APACHE; D000328:Adult; D000368:Aged; D003661:Decision Support Techniques; D003663:Decision Trees; D005260:Female; D017052:Hospital Mortality; D006784:Hospitals, Teaching; D006801:Humans; D007362:Intensive Care Units; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D017064:Process Assessment, Health Care / Q000379:methods; D011379:Prognosis; D012372:ROC Curve; D012189:Retrospective Studies; D012720:Severity of Illness Index; D016019:Survival Analysis; D015599:Trauma Severity Indices; D014947:Wounds and Injuries / Q000145:classification / Q000401:mortality