Reducing systematic review workload through certainty-based screening.

ABSTRACT: In systematic reviews, the growing number of published studies imposes a significant screening workload on reviewers. Active learning is a promising approach to reduce the workload by automating some of the screening decisions, but it has been evaluated for a limited number of disciplines. The suitability of applying active learning to complex topics in disciplines such as social science has not been studied, and the selection of useful criteria and enhancements to address the data imbalance problem in systematic reviews remains an open problem. We applied active learning with two criteria (certainty and uncertainty) and several enhancements in both clinical medicine and social science (specifically, public health) areas, and compared the results in both. The results show that the certaint

SUBMITTER: Miwa M 

PROVIDER: S-EPMC4199186 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature