indexed search engine

We combined four machine learning techniques and four data preprocessing for class imbalance to identify the outperforming strategy to screen articles in PubMed for inclusion in systematic reviews. We used textual data of 14 systematic reviews as case studies. Meta-analytic fixed-effect models were used to pool delta AUCs separately by classifier and strategy. Resampling techniques slightly improved the performance of the investigated machine learning techniques. From a computational perspective, random undersampling 35:65 may be preferred.

The proposed machine learning instrument has the potential to help researchers identify relevant studies in the SR process by reducing workload, without losing sensitivity and at a small price in terms of specificity.