Abstract
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.