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Briefings in Bioinformatics 2005 6(1):57-71; doi:10.1093/bib/6.1.57
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© Henry Stewart Publications

Special Issue Papers

A survey of current work in biomedical text mining

Aaron M. Cohen
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 version 2 of the Synchronized Multimedia Integration Language (SMIL 2.0).

William R. Hersh
Professor and Chair of the Department of Medical Informatics & Clinical Epidemiology in the School of Medicine at Oregon Health & Science University (OHSU) in Portland, Oregon. Dr Hersh's research focuses on the development and evaluation of information retrieval systems for biomedical practitioners and researchers.


Aaron Michael Cohen, Postdoctoral Fellow, Department of Medical Informatics and Clinical Epidemiology, School of Medicine, Oregon Health & Science University, 3181 S.W. Sam Jackson Park Road, Portland, OR 97239—309, USA Tel: +1 503 494 0046 Fax: +1 503 494 4551 E-mail: cohenaa{at}ohsu.edu

The volume of published biomedical research, and therefore the underlying biomedical knowledge base, is expanding at an increasing rate. Among the tools that can aid researchers in coping with this information overload are text mining and knowledge extraction. Significant progress has been made in applying text mining to named entity recognition, text classification, terminology extraction, relationship extraction and hypothesis generation. Several research groups are constructing integrated flexible text-mining systems intended for multiple uses. The major challenge of biomedical text mining over the next 5–10 years is to make these systems useful to biomedical researchers. This will require enhanced access to full text, better understanding of the feature space of biomedical literature, better methods for measuring the usefulness of systems to users, and continued cooperation with the biomedical research community to ensure that their needs are addressed.

Keywords: text-mining, bioinformatics, natural language processing


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