Prediction of hemorrhagic blood loss with a genetic algorithm neural network

There is no established method for accurately predicting how much blood loss has occurred during hemorrhage. In the present study, we examine whether a genetic algorithm neural network (GANN) can predict volume of hemorrhage in an experimental model in rats and we compare its accuracy to stepwise linear regression (SLR). Serial measurements of heart period; diastolic, systolic, and mean blood pressures; hemoglobin; pH; arterial PO2; arterial PCO2; bicarbonate; base deficit; and blood loss as percent of total estimated blood volume were made in 33 male Wistar rats during a stepwise hemorrhage. The GANN and SLR used a randomly assigned training set to predict actual volume of hemorrhage in a test set. Diastolic blood pressure, arterial PO2, and base deficit were selected by the GANN as the optimal predictors set. Root mean square error in prediction of estimated blood volume by GANN was significantly lower than by SLR (2.63%, SD 1.44, and 4.22%, SD 3.48, respectively; P < 0.001). A GANN can predict highly accurately and significantly better than SLR volume of hemorrhage without knowledge of prehemorrhage status, rate of blood loss, or trend in physiological variables.