Comparison of artificial intelligence techniques with UKTRISS for estimating probability of survival after trauma. UK Trauma and Injury Severity Score.
BACKGROUND
The development of TRISS was principally a search for variables that correlated with outcome. It is not known, however, if linear statistical models provide optimal results. Artificial intelligence techniques can answer this question and also determine the most important predictor variables.


METHODS
An artificial neural network, using 16 anatomic and physiologic predictor variables, was compared with the latest United Kingdom version of TRISS model.


RESULTS
Both methods were 89.6% correct, but TRISS was significantly better by the area under the receiver operating characteristic curve (0.941 vs. 0.921, p < 0.001). The artificial neural network, however, was better calibrated to the test data (Hosmer-Lemeshow statistic, 58.3 vs. 105.4). Head injury, age, and chest injury were the most important predictors by linear or nonlinear methods, whereas respiration rate, heart rate, and systolic blood pressure were underused.


CONCLUSIONS
Prediction using linear statistics is adequate but not optimal. Only half the predictors have important predictive value, fewer still when using linear classification. The strongest predictors swamp any nonlinearity observed in other variables.
Becalick
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DC
Academic Unit of Accident and Emergency, St. Bartholomew's and the Royal London School of Medicine, Queen Mary and Westfield College, London, England.
Coats
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TJ
D001185:Artificial Intelligence; D005103:Expert Systems; D006801:Humans; D015601:Injury Severity Score; D008131:London; D008432:Mathematical Computing; D009104:Multiple Trauma / Q000401:mortality*; D016571:Neural Networks, Computer; D011336:Probability; D015996:Survival Rate; D006113:United Kingdom