The best evidence concerning comparative treatment effectiveness comes from
clinical trials, the results of which are reported in unstructured articles.
Medical experts must manually extract information from articles to inform
decision-making, which is time-consuming and expensive. Here we consider the
end-to-end task of both (a) extracting treatments and outcomes from full-text
articles describing clinical trials (entity identification) and, (b) inferring
the reported results for the former with respect to the latter (relation
extraction). We introduce new data for this task, and evaluate models that have
recently achieved state-of-the-art results on similar tasks in Natural Language
Processing. We then propose a new method motivated by how trial results are
typically presented that outperforms these purely data-driven baselines.
Finally, we run a fielded evaluation of the model with a non-profit seeking to
identify existing drugs that might be re-purposed for cancer, showing the
potential utility of end-to-end evidence extraction systems