1) Biomedical literature search is the main entry point for an ever-increasing range of information. PubMed/MEDLINE is the most widely used service for this purpose. However, finding citations relevant to a users information need is not always easy in PubMed. Improving our understanding of the growing population of PubMed users, their information needs and the way in which they meet these needs opens opportunities to improve information services and information access provided by PubMed. One resource for understanding and characterizing patrons of search engines is the transaction logs. Our previous investigation of user query logs has led us to develop and deploy a useful application in assisting user query formulation in PubMed, namely Related Queries (RQ). Inspired by its success, we have continued using log analysis to identify research problems which are closely related to PubMed operations. 2) Another application of query log analysis starts with a disease name or some other significant term and asks how often a document containing this term is clicked on in response to a query containing this term. This click through rate is used as a feature in machine learning to decide whether a document has the disease as a central focus.
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