Artificial neural networks improve the prediction of mortality in intracerebral hemorrhage

Background: Artificial neural network (ANN) analysis methods have led to mo re sensitive diagnosis of myocardial infarction and improved prediction of mortality in breast cancer, prostate cancer, and trauma patients. Prognosti c studies have identified early clinical and radiographic predictors of mor tality after intracerebral hemorrhage (ICH). To date, published models have not achieved the accuracy necessary for use in making decisions to limit m edical interventions. We recently reported a logistic regression model that correctly classified 79% of patients who died and 90% of patients who surv ived. In an attempt to improve prediction of mortality we computed an ANN m odel with the same data. Objective: To determine whether an ANN analysis wo uld provide a more accurate prediction of mortality after ICH when compared with multiple logistic regression models computed using the same data. Met hods: Analyses were conducted on data collected prospectively on 81 patient s with supratentorial ICH. Multiple logistic regression was used to predict hospital mortality, then an ANN analysis was applied to the same data set. Input variables were age, gender, race, hydrocephalus, mean arterial press ure, pulse pressure, Glasgow Coma Scale score, intraventricular hemorrhage, hydrocephalus, hematoma size, hematoma location (ganglionic, thalamic, or lobar), cisternal effacement, pineal shift, history of hypertension, histor y of diabetes, and age. Results: The ANN model correctly classified all pat ients (100%) as alive or dead compared with 85% correct classification for the logistic regression model. A second ANN verification model was equally accurate. The ANN was superior to the logistic regression model on all obje ctive measures of fit. Conclusions: ANN analysis more effectively uses info rmation for prediction of mortality in this sample of patients with ICH. A well-validated ANN may have a role in the clinical management of ICH.