A novel MEDLINE topic indexing method using image presentation

Abstract MEDLINE is one of the largest databases of biomedical literatures. The search results from MEDLINE for medical terms are in the form of lists of articles with PubMed IDs. To further explore and select articles that may help identify potentially interesting interactions between terms, users need to navigate through the lists of URLs to retrieve and read actual articles to find relevancies among these terms. Such work becomes extremely time consuming and unbearably tedious when each query returns tens of thousands results with an uncertain recall rate. To overcome this problem, we develop a topic-specific image indexing and presentation method for discovering interactions or relatedness of medical terms from MEDLINE, based on which a prototype tool is implemented to help discover interactions between terms of types of diseases. The merits of the method is illustrated by search examples using the tool and MEDLINE abstract dataset.