Artificial neural networks improve the prediction of mortality in intracerebral hemorrhage

Background: Artificial neural network (ANN) analysis methods have led to more sensitive diagnosis of myocardial infarction and improved prediction of mortality in breast cancer, prostate cancer, and trauma patients. Prognostic studies have identified early clinical and radiographic predictors of mortality after intracerebral hemorrhage (ICH). To date, published models have not achieved the accuracy necessary for use in making decisions to limit medical interventions. We recently reported a logistic regression model that correctly classified 79% of patients who died and 90% of patients who survived. In an attempt to improve prediction of mortality we computed an ANN model with the same data. Objective: To determine whether an ANN analysis would provide a more accurate prediction of mortality after ICH when compared with multiple logistic regression models computed using the same data. Methods: Analyses were conducted on data collected prospectively on 81 patients 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 pressure, pulse pressure, Glasgow Coma Scale score, intraventricular hemorrhage, hydrocephalus, hematoma size, hematoma location (ganglionic, thalamic, or lobar), cisternal effacement, pineal shift, history of hypertension, history of diabetes, and age. Results: The ANN model correctly classified all patients (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 objective measures of fit. Conclusions: ANN analysis more effectively uses information 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.