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Detection of Evidence in Clinical Research Papers
Davis-Desmond, P. and Moll�, D.
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.
Cite as: Davis-Desmond, P. and Moll�, D. (2012). Detection of Evidence in Clinical Research Papers. In Proc. Australasian Workshop on Health Informatics and Knowledge Management (HIKM 2012) Melbourne, Australia. CRPIT, 129. Butler-Henderson, K. and Gray, K. Eds., ACS. 13-20
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