title: A comparison of neural networks for computing predicted probability of survival for trauma victims.
abstract: TRISS is a statistical method for predicting the probability of survival of trauma victims. Analysis of data from the Trauma Registry at Charleston Area Medical Center showed that only 48% of the trauma fatalities in the 5-year period 1992-1996 were correctly predicted by TRISS. Trauma practitioners from other Trauma Centers report similar problems with TRISS. Researchers have suggested improvements that range from simply changing the input variables and/or regression coefficients in TRISS to using an entirely different model. In this study we describe a method of calculating survival probabilities using Artificial Neural Networks (ANN). This method was chosen because of the similarity of the ANN output function to the function that produces the TRISS probability of survival. Additional variables were added based on the results of other research efforts as well as analysis of the CAMC Trauma Registry. A comparison was made between the abilities of TRISS to predict fatalities and to approximate probability of survival. The ANN outperformed TRISS in predicting fatalities in a training set (68.1% correct vs. 47.9% correct) and in a testing set (61.3% correct vs. 51.3% correct). More importantly, the ANN produced better estimates of predicted deaths. Using a data set that included 119 deaths, the ANN model predicted 125 deaths for a 5% relative error. The predicted number using TRISS was 86 for a relative error of 27.7%. Since effective quality improvement for trauma care depends on accurately identifying cases that fall outside the expected results, a more accurate predictive tool allows a more focused review of those significant cases, thus conserving resources without compromising quality. Neural Networks appear to be a predictive tool that can provide probability of survival estimates that are more accurate than TRISS.
authors.affiliation: Marshall University, Joan C. Edwards School of Medicine, Huntington, USA.
authors.forename: J Joseph
authors.initials: JJ
authors.lastname: Fuller
authors.forename: Mary
authors.initials: M
authors.lastname: Emmett
authors.forename: James W
authors.initials: JW
authors.lastname: Kessel
authors.forename: Pamela D
authors.initials: PD
authors.lastname: Price
authors.forename: James H
authors.initials: JH
authors.lastname: Forsythe
mesh_terms: D006801:Humans; D015601:Injury Severity Score; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011336:Probability; D011379:Prognosis; D012042:Registries; D016019:Survival Analysis; D014947:Wounds and Injuries / Q000401:mortality*