Predicting ICU survival: a meta-level approach.
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.
Gortzis
Lefteris G
LG
Telemedicine Unit, School of Medicine, University of Patras, Patras, Greece. gortzis@med.upatras.gr
Sakellaropoulos
Filippos
F
Ilias
Ioannis
I
Stamoulis
Konstantinos
K
Dimopoulou
Ioanna
I
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