Facilitating biomedical systematic reviews using ranked text retrieval and classification

Searching and selecting articles to be included in systematic reviews is a real challenge for healthcare agencies responsible for publishing these reviews. The current practice of manually reviewing all papers returned by complex hand-crafted boolean queries is human labour-intensive and difficult to maintain. We demonstrate a two-stage searching system that takes advantage of ranked queries and support-vector machine text classification to assist in the retrieval of relevant articles, and to restrict results to higher-quality documents. Our proposed approach shows significant work saved in the systematic review process over a baseline of a keyword-based retrieval system.