%0 Journal Article
%T MScanner: a classifier for retrieving Medline citations
%A Graham L Poulter
%A Daniel L Rubin
%A Russ B Altman
%A Cathal Seoighe
%J BMC Bioinformatics
%D 2008
%I BioMed Central
%R 10.1186/1471-2105-9-108
%X 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 results ranked by decreasing probability of relevance. For maximum speed it uses the Medical Subject Headings (MeSH) and journal of publication as a concise document representation, and takes roughly 90 seconds to return results against the 16 million records in Medline. The web interface provides interactive exploration of the results, and cross validated performance evaluation on the relevant input against a random subset of Medline. We describe the classifier implementation, cross validate it on three domain-specific topics, and compare its performance to that of an expert PubMed query for a complex topic. In cross validation on the three sample topics against 100,000 random articles, the classifier achieved excellent separation of relevant and irrelevant article score distributions, ROC areas between 0.97 and 0.99, and averaged precision between 0.69 and 0.92.MScanner is an effective non-domain-specific classifier that operates on the entire Medline database, and is suited to retrieving topics for which many features may indicate relevance. Its web interface simplifies the task of classifying Medline citations, compared to building a pre-filter and classifier specific to the topic. The data sets and open source code used to obtain the results in this paper are available on-line and as supplementary material, and the web interface may be accessed at http://mscanner.stanford.edu webcite.Information retrieval on the biomedical literature indexed by Medline [1] is most often carried out using ad-hoc retrieval. The PubMed [2] boolean search engine is the most widely used Medline retrieval system. Other interfaces to searching Medline include relevance ranking systems such as Relemed [3] and systems such as EBIMed [4] that perform information extraction and clustering on results. Certa
%U http://www.biomedcentral.com/1471-2105/9/108