Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 904860, 10 pages
Clinical Outcome Prediction in Aneurysmal Subarachnoid Hemorrhage Using Bayesian Neural Networks with Fuzzy Logic Inferences
1Divisions of Neurosurgery & Critical Care Medicine, St. Michael's Hospital, University of Toronto, 30 Bond Street, 3 Bond Wing, Toronto, ON, Canada M5B 1W8
2Keenan Research Centre of the Li Ka Shing Knowledge Institute of St. Michael's Hospital, University of Toronto, 30 Bond Street, 3 Bond Wing, Toronto, ON, Canada M5B 1W8
3Department of Critical Care, Trauma and Neurosurgery Program, Keenan Research Centre, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, ON, Canada
4Departments of Anesthesia and Surgery, University of Toronto, 30 Bond Street, 3 Bond Wing, Toronto, ON, Canada M5B 1W8
5Department of Clinical Epidemiology & Biostatistics, and Department of Medicine, Centre for Evaluation of Medicines, St. Joseph's Hospital, McMaster University Toronto, ON, Canada
Received 24 January 2013; Accepted 23 March 2013
Academic Editor: Nestor V. Torres
Copyright © 2013 Benjamin W. Y. Lo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Objective. The novel clinical prediction approach of Bayesian neural networks with fuzzy logic inferences is created and applied to derive prognostic decision rules in cerebral aneurysmal subarachnoid hemorrhage (aSAH). Methods. The approach of Bayesian neural networks with fuzzy logic inferences was applied to data from five trials of Tirilazad for aneurysmal subarachnoid hemorrhage (3551 patients). Results. Bayesian meta-analyses of observational studies on aSAH prognostic factors gave generalizable posterior distributions of population mean log odd ratios (ORs). Similar trends were noted in Bayesian and linear regression ORs. Significant outcome predictors include normal motor response, cerebral infarction, history of myocardial infarction, cerebral edema, history of diabetes mellitus, fever on day 8, prior subarachnoid hemorrhage, admission angiographic vasospasm, neurological grade, intraventricular hemorrhage, ruptured aneurysm size, history of hypertension, vasospasm day, age and mean arterial pressure. Heteroscedasticity was present in the nontransformed dataset. Artificial neural networks found nonlinear relationships with 11 hidden variables in 1 layer, using the multilayer perceptron model. Fuzzy logic decision rules (centroid defuzzification technique) denoted cut-off points for poor prognosis at greater than 2.5 clusters. Discussion. This aSAH prognostic system makes use of existing knowledge, recognizes unknown areas, incorporates one's clinical reasoning, and compensates for uncertainty in prognostication.