Cross-validation experiments were performed to evaluate the classifier's parameters and performance. Measurements Work saved over sampling (WSS) at no less than a 95% recall was used as the main measure of performance. Results The minimum workload reduction for a systematic review for one topic, achieved with a FCNB/WE classifier, was 8.5%; the maximum was 62.2% and the average over the 15 topics was 33.5%. This is 15.0% higher than the average workload reduction obtained using a voting perceptron-based automated citation classification system. Conclusion The FCNB/WE classifier is simple, easy to implement, and produces significantly better results in reducing the workload than previously achieved. The results support it being a useful algorithm for machinelearning-based automation of systematic reviews of drug class efficacy for disease treatment.
Matwin et al. REF exploited a factorised complement naive Bayes classifier for reducing the workload of experts reviewing journal articles for building systematic reviews of drug class efficacy.
experts in performing systematic reviews A new algorithm for reducing the workload of
Journal of the American Medical Informatics Association : JAMIA
Journal of the American Medical Informatics Association : JAMIA
Computer Science Medicine