Role of artificial neural networks in prediction of survival of burn patients—a new approach

Abstract

A burn patient may require the most complicated treatment regimes encountered among trauma victims. Predicting the outcome of such treatment depends on several factors which have non-linear relationships. Traditional methods in prediction are “logistic regression” and “maximum likelihood”. In this study, an artificial neural network (ANN) is used for computing survival among burn patients admitted to the “Motahary Burn Center”, during a 1 year period (1996–1997). Fifteen different observations, such as total body surface area (TBSA), rescue time, admission period, surgery, inhalation injuries, etc. were obtained, retrospectively. A normal feed forward ANN was developed by Thinkspro software. It has 15 input-units, two hidden layers, and one output-unit. Survival was higher in males, those in whom early fluid resuscitation had been initiated and in patients in the middle of the age spectrum ( P<0.0001). Strong correlations with these factors were noted. In the training phase, the ANNs accuracy reached 90%. In this study, the ANN has been applied for the first time to predict burn victim survival. This study can enable a different view point to help burn center physicians in the prediction of survival of their patients.