A Multiple-Stage Approach to Re-ranking Medical Documents

The widespread use of the Web has radically changed the way people acquire medical information. Every day, patients, their caregivers, and doctors themselves search for medical information to resolve their medical information needs. However, search results provided by existing medical search engines often contain irrelevant or uninformative documents that are not appropriate for the purposes of the users. As a solution, this paper presents a method of re-ranking medical documents. The key concept of our method is to compute accurate similarity scores through multiple stages of re-ranking documents from the initial documents retrieved by a search engine. Specifically, our method combines query expansion with abbreviations, query expansion with discharge summary, clustering-based document scoring, centrality-based document scoring, and pseudo relevance feedback with relevance model. The experimental results from participating in Task 3a of the CLEF 2014 eHealth show the performance of our method.