Predicting adequacy of vancomycin regimens: A learning-based classification approach to improving clinical decision making

Abstract

Clinicians' drug regimen decision making is critical, particularly when involving high-alert medications. In this study, we use decision-tree induction C4.5 and a backpropagation neural network to construct decision support systems for predicting the regimen adequacy of vancomycin, a glycopeptide antimicrobial antibiotic effective for Gram-positive bacterial infections. We comparatively evaluate the respective systems using a total of 987 clinical vancomycin cases collected from a major tertiary medical center in southern Taiwan. We supplement each system using Bagging and then examine the predictive power of the extended system. Overall, our evaluation results show the overall accuracy of the decision support system based on C4.5 or the neural network to be significantly higher than that of the benchmark one-compartment pharmacokinetic model. Use of Bagging can considerably improve the effectiveness of each system across different performance measures, particularly for cases of decision classes in which the base systems (i.e., without Bagging) perform modestly. Taken together, our evaluation results seem to favor the use of Bagging to enhance the performance of decision support systems constructed using decision-tree induction C4.5.