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Summary: Detection of Evidence in Clinical Research Papers
Patrick Davis-Desmond Diego Moll´a
Department of Computing
Macquarie University
Sydney 2109, NSW, Australia
Email: diego.molla-aliod@mq.edu.au
Abstract
When appraising published clinical research, medical
doctors and researchers often need to know whether
the clinical outcomes presented had statistical evi-
dence. In this paper we present a study for the de-
tection 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 writ-
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