Generating a mortality model from a pediatric ICU (PICU) database utilizing knowledge discovery.
Current models for predicting outcomes are limited by biases inherent in a priori hypothesis generation. Knowledge discovery algorithms generate models directly from databases, minimizing such limitations. Our objective was to generate a mortality model from a PICU database utilizing knowledge discovery techniques. The database contained 5067 records with 192 clinically relevant variables. It was randomly split into training (75%) and validation (25%) groups. We used decision tree induction to generate a mortality model from the training data, and validated its performance on the validation data. The original PRISM algorithm was used for comparison. The decision tree model contained 25 variables and predicted 53/88 deaths; 29 correctly (Sens:33%, Spec:98%, PPV:54%). PRISM predicted 27/88 deaths correctly (Sens:30%, Spec:98%, PPV:51%). Performance difference between models was not significant. We conclude that knowledge discovery algorithms can generate a mortality model from a PICU database, helping establish validity of such tools in the clinical medical domain.
Kennedy
Curtis E
CE
Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA.
Aoki
Noriaki
N
D000465:Algorithms; D002648:Child; D016208:Databases, Factual; D003663:Decision Trees; D017052:Hospital Mortality; D006801:Humans; D015278:Intensive Care Units, Pediatric; D012306:Risk; D012720:Severity of Illness Index