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Systematic use of the published results of randomized clinical trials isincreasingly important in evidence-based medicine. In order to collate andanalyze the results from potentially numerous trials, evidence tables are usedto represent trials concerning a set of interventions of interest. An evidencetable has columns for the patient group, for each of the interventions beingcompared, for the criterion for the comparison (e.g. proportion who survivedafter 5 years from treatment), and for each of the results. Currently, it is alabour-intensive activity to read each published paper and extract theinformation for each field in an evidence table. There have been some NLPstudies investigating how some of the features from papers can be extracted, orat least the relevant sentences identified. However, there is a lack of an NLPsystem for the systematic extraction of each item of information required foran evidence table. We address this need by a combination of a maximum entropyclassifier, and integer linear programming. We use the later to handleconstraints on what is an acceptable classification of the features to beextracted. With experimental results, we demonstrate substantial advantages inusing global constraints (such as the features describing the patient group,and the interventions, must occur before the features describing the results ofthe comparison).

Extraction of evidence tables from abstracts of randomized clinical trials using a maximum entropy classifier and global constraints