MScanner: a classifier for retrieving Medline citations

Background: Keyword searching through PubMed and other systems is the standard means of retrieving information from Medline. However, ad-hoc retrieval systems do not meet all of the needs of databases that curate information from literature, or of text miners developing a corpus on a topic that has many terms indicative of relevance. Several databases have developed supervised learning methods that operate on a filtered subset of Medline, to classify Medline records so that fewer articles have to be manually reviewed for relevance. A few studies have considered generalisation of Medline classification to operate on the entire Medline database in a non-domainspecific manner, but existing applications lack speed, available implementations, or a means to measure performance in new domains. Results: MScanner is an implementation of a Bayesian classifier that provides a simple web interface for submitting a corpus of relevant training examples in the form of PubMed IDs and returning result...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Graham L. Poulter, Daniel L. Rubin, Russ B. Altman, Cathal Seoighe
• Keywords: BMCBI 2008, Database, Entire Medline Database, Medline