Performance of active learning models for screening prioritization in systematic reviews: a simulation study into the Average Time to Discover relevant records

ConclusionsActive learning models for screening prioritization demonstrate significant potential for reducing the workload in systematic reviews. The Naive Bayes + TF-IDF model yielded the best results overall. The Average Time to Discovery (ATD) measures performance of active learning models throughout the entire screening process without the need for an arbitrary cut-off point. This makes the ATD a promising metric for comparing the performance of different models across different datasets.
Source: Systematic Reviews - Category: International Medicine & Public Health Source Type: research