@inproceedings{ecf3de290fb84095b66f6eef5f4582c0,
title = "Detection of evidence in clinical research papers",
abstract = "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.",
keywords = "evidence-based medicine, appraisal, text classification",
author = "Patrick Davis-Desmond and Diego Moll{\'a}",
year = "2012",
language = "English",
isbn = "9781921770104",
series = "Conferneces in research and practice in information technology",
publisher = "Australian Computer Society",
pages = "13--20",
editor = "Kerryn Butler-Henderson and Kathleen Gray",
booktitle = "Proceedings of the Fifth Australasian Workshop on Health Informatics and Knowledge Management (HIKM 2012), Melbourne, Australia, 31 January - 3 February 2012",
address = "Australia",
note = "Australasian Workshop on Health Informatics and Knowledge Management (5th : 2012) ; Conference date: 30-01-2012 Through 03-02-2012",
}