Prediction of intracranial findings on CT-scans by alternative modelling techniques
Background: Prediction rules for intracranial traumatic findings in patients with minor head injury are designed to reduce the use of computed tomography (CT) without missing patients at risk for complications. This study investigates whether alternative modelling techniques might improve the applicability and simplicity of such prediction rules. Methods. We included 3181 patients with minor head injury who had received CT scans between February 2002 and August 2004. Of these patients 243 (7.6%) had intracranial traumatic findings and 17 (0.5%) underwent neurosurgical intervention. We analyzed sensitivity, specificity and area under the ROC curve (AUC-value) to compare the performance of various modelling techniques by 10 × 10 cross-validation. The techniques included logistic regression, Bayes network, Chi-squared Automatic Interaction Detection (CHAID), neural net, support vector machines, Classification And Regression Trees (CART) and "decision list" models. Results: The cross-validated performance was best for the logistic regression model (AUC 0.78), followed by the Bayes network model and the neural net model (both AUC 0.74). The other models performed poorly (AUC < 0.70). The advantage of the Bayes network model was that it provided a graphical representation of the relationships between the predictors and the outcome. Conclusions: No alternative modelling technique outperformed the logistic regression model. However, the Bayes network model had a presentation format which provided more detailed insights into the structure of the prediction problem. The search for methods with good predictive performance and an attractive presentation format should continue.
|Persistent URL||dx.doi.org/10.1186/1471-2288-11-143, hdl.handle.net/1765/34347|
van der Ploeg, T., Smits, M., Dippel, D.W.J., Hunink, M.G.M., & Steyerberg, E.W.. (2011). Prediction of intracranial findings on CT-scans by alternative modelling techniques. B M C Medical Research Methodology, 11. doi:10.1186/1471-2288-11-143