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is a postdoctoral fellow in the medical informatics programme at OHSU. Dr Cohen works in the area of text mining, focusing on issues and applications important to biomedical researchers. He was chairman of the W3C working group that produced

Like the primary scientific literature, GeneRIFs exhibit both growth and obsolescence. NLM’s control over the contents of the Entrez Gene database provides a mechanism for dealing with obsolete data: GeneRIFs are removed from the database when they are found to be of low quality. However, the rapid and extensive growth of Entrez Gene makes manual location of low-quality GeneRIFs problematic. This paper presents a system that takes advantage of the summary-like quality of GeneRIFs to detect low-quality GeneRIFs via a summary revision approach, achieving precision of 89 % and recall of 77%. Aspects of the system have been adopted by NLM as a quality assurance mechanism.

A new paradigm, which models the relationships between handwriting and topic categories, in the context of medical forms, is presented. The ultimate goals are (i) the recognition of medical handwriting, and (ii) the use of such information for practical applications such as a medical form search engine. Medical forms have diverse, complex and large lexicons consisting of English, Medical and Pharmacology corpus. Our technique shows that a few recognized characters, returned by handwriting recognition, can be used to construct a linguistic model capable of representing a medical topic

We propose a method that aligns biomedical acronyms and their long-form definitions across different languages. We use a freely available search and extraction tool by which abbreviations, together with their fully expanded forms, are massively mined from the Web. In a subsequent step, language-specific variants, synonyms, and translations of the extracted acronym definitions are normalized by referring to a language-independent, shared semantic interlingua. 1

is a postdoctoral fellow in the medical informatics programme at OHSU. Dr Cohen works in the area of text mining, focusing on issues and applications important to biomedical researchers. He was chairman of the W3C working group that produced

This informal tutorial is intended for investigators and students who would like to understand the workings of information retrieval systems, including the most frequently used search engines: PubMed and Google. Having a basic knowledge of the terms and concepts of information retrieval should improve the efficiency and productivity of searches. As well, this knowledge is needed in order to follow current research efforts in biomedical information retrieval and text mining that are developing new systems not only for finding documents on a given topic, but extracting and integrating knowledge across documents.

We introduce an interlingua-based approach to cross-language information retrieval, in which queries, as well as documents, are mapped onto a language-independent concept layer on which retrieval operations are performed. This approach is contrasted with one which directly translates non-English queries (German and Portuguese, in our experiments) to English ones which, subsequently, are processed on English documents. We report on the empirical evaluation of both approaches on a large medical document collection (the OHSUMED corpus).

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