Detection of evidence in clinical research papers

Patrick Davis-Desmond, Diego Mollá

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review


When appraising published clinical research, medical doctors and researchers often need to know whether the clinical outcomes presented had statistical evidence. In this paper we present a study for the detection of expressions of such statistical evidence. An effective rule-based classifier has been developed that uses regular expressions and a list of negation phrases to automatically classify documents as either showing evidence of effect in the results or not. The classifier performed with an accuracy between 88% and 98% at 95% confidence intervals, and it also outperformed a set of baselines using bag-of-word features in several statistical classifiers. The rule-based system is written in Python and is available as open-source code.
Original languageEnglish
Title of host publicationProceedings of the Fifth Australasian Workshop on Health Informatics and Knowledge Management (HIKM 2012), Melbourne, Australia, 31 January - 3 February 2012
EditorsKerryn Butler-Henderson, Kathleen Gray
Place of PublicationSydney
PublisherAustralian Computer Society
Number of pages8
ISBN (Print)9781921770104
Publication statusPublished - 2012
EventAustralasian Workshop on Health Informatics and Knowledge Management (5th : 2012) - Melbourne, VIC
Duration: 30 Jan 20123 Feb 2012

Publication series

NameConferneces in research and practice in information technology
PublisherAustralian Computer Society
ISSN (Print)1445-1336


WorkshopAustralasian Workshop on Health Informatics and Knowledge Management (5th : 2012)
CityMelbourne, VIC


  • evidence-based medicine
  • appraisal
  • text classification


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