MScanner: a classifier for retrieving Medline citations |
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Authors: | Graham L Poulter Daniel L Rubin Russ B Altman Cathal Seoighe |
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Affiliation: | (1) UCT NBN Node, Department of Molecular and Cell Biology, University of Cape Town, Cape Town, South Africa;(2) Stanford Medical Informatics, Stanford University, San Francisco, USA;(3) Department of Bioengineering and Department of Genetics, Stanford University, San Francisco, USA |
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Abstract: | 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-domain-specific manner, but existing applications lack speed, available implementations, or a means to measure performance in new domains. |
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