A corpus for research in text processing for evidence based medicine

Diego Mollá, María Elena Santiago-Martínez, Abeed Sarker, Cécile Paris

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Evidence based medicine (EBM) urges the medical doctor to incorporate the latest available clinical evidence at point of care. A major stumbling block in the practice of EBM is the difficulty to keep up to date with the clinical advances. In this paper we describe a corpus designed for the development and testing of text processing tools for EBM, in particular for tasks related to the extraction and summarisation of answers and corresponding evidence related to a clinical query. The corpus is based on material from the Clinical Inquiries section of The Journal of Family Practice. It was gathered and annotated by a combination of automated information extraction, crowdsourcing tasks, and manual annotation. It has been used for the original summarisation task for which it was designed, as well as for other related tasks such as the appraisal of clinical evidence and the clustering of the results. The corpus is available at SourceForge (http://sourceforge.net/projects/ebmsumcorpus/).

LanguageEnglish
Pages705-727
Number of pages23
JournalLanguage Resources and Evaluation
Volume50
Issue number4
DOIs
Publication statusPublished - 1 Dec 2016

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text processing
medicine
evidence
Text Processing
Evidence-based Medicine

Keywords

  • Annotation
  • Corpus
  • Crowdsourcing
  • Evidence based medicine
  • Text summarization

Cite this

Mollá, Diego ; Santiago-Martínez, María Elena ; Sarker, Abeed ; Paris, Cécile. / A corpus for research in text processing for evidence based medicine. In: Language Resources and Evaluation. 2016 ; Vol. 50, No. 4. pp. 705-727.
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A corpus for research in text processing for evidence based medicine. / Mollá, Diego; Santiago-Martínez, María Elena; Sarker, Abeed; Paris, Cécile.

In: Language Resources and Evaluation, Vol. 50, No. 4, 01.12.2016, p. 705-727.

Research output: Contribution to journalArticleResearchpeer-review

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