Towards a multi-level framework for supporting systematic review — A pilot study

As part of the forefront of evidence-based medicine, a systematic review (SR) identifies, appraises, and synthesizes all the available literature relevant to a question of interest in a transparent and systematic way. A time-consuming step in conducting systematic review (SR) is the manual article screening from a list of potentially relevant articles retrieved by librarians. In this study, we propose a multi-level SR supporting framework including three levels: Level 1 - ranking with multiple metrics aiming to assist the screening process by increasing the efficiency without compromising the validity; Level 2 - topic analysis for discovering distributed semantics; and Level 3 - network analysis on relation extracted for comprehensive semantic summarization. The sensitivities in two case studies for relevance ranking reached above 80%, while the screening burdens were lowered to 25% based on a crude relevance ranking approach. Topic analysis based on Latent Dirichlet Allocations (LDAs) showed high consistency among the domain experts´ selection of articles. The predicate relation network summarized important predicate relations between medical concepts.