Improving the Performance of Text Categorization Models used for the Selection of High Quality Articles

OBJECTIVES: Machine learning systems can considerably reduce the time and effort needed by experts to perform new systematic reviews (SRs). This study investigates categorization models, which are trained on a combination of included and commonly excluded articles, which can improve performance by identifying high quality articles for new procedures or drug SRs. METHODS: Test collections were built using the annotated reference files from 19 procedure and 15 drug systematic reviews. The classification models, using a support vector machine, were trained by the combined even data of other topics, excepting the desired topic. This approach was compared to the combination of included and commonly excluded articles with the combination of included and excluded articles. Accuracy was used for the measure of comparison. RESULTS: On average, the performance was improved by about 15% in the procedure topics and 11% in the drug topics when the classification models trained on the combination of articles included and commonly excluded, were used. The system using the combination of included and commonly excluded articles performed better than the combination of included and excluded articles in all of the procedure topics. CONCLUSIONS: Automatically rigorous article classification using machine learning can reduce the workload of experts when they perform systematic reviews when the topic-specific data are scarce. In particular, when the combination of included and commonly excluded articles is used, this system will be more effective.