  Systematic use of the published results of randomized clinical trials is
increasingly important in evidence-based medicine. In order to collate and
analyze the results from potentially numerous trials, evidence tables are used
to represent trials concerning a set of interventions of interest. An evidence
table has columns for the patient group, for each of the interventions being
compared, for the criterion for the comparison (e.g. proportion who survived
after 5 years from treatment), and for each of the results. Currently, it is a
labour-intensive activity to read each published paper and extract the
information for each field in an evidence table. There have been some NLP
studies investigating how some of the features from papers can be extracted, or
at least the relevant sentences identified. However, there is a lack of an NLP
system for the systematic extraction of each item of information required for
an evidence table. We address this need by a combination of a maximum entropy
classifier, and integer linear programming. We use the later to handle
constraints on what is an acceptable classification of the features to be
extracted. With experimental results, we demonstrate substantial advantages in
using global constraints (such as the features describing the patient group,
and the interventions, must occur before the features describing the results of
the comparison).
