  Evidence-based medicine, the practice in which healthcare professionals refer
to the best available evidence when making decisions, forms the foundation of
modern healthcare. However, it relies on labour-intensive systematic reviews,
where domain specialists must aggregate and extract information from thousands
of publications, primarily of randomised controlled trial (RCT) results, into
evidence tables. This paper investigates automating evidence table generation
by decomposing the problem across two language processing tasks: \textit{named
entity recognition}, which identifies key entities within text, such as drug
names, and \textit{relation extraction}, which maps their relationships for
separating them into ordered tuples. We focus on the automatic tabulation of
sentences from published RCT abstracts that report the results of the study
outcomes. Two deep neural net models were developed as part of a joint
extraction pipeline, using the principles of transfer learning and
transformer-based language representations. To train and test these models, a
new gold-standard corpus was developed, comprising almost 600 result sentences
from six disease areas. This approach demonstrated significant advantages, with
our system performing well across multiple natural language processing tasks
and disease areas, as well as in generalising to disease domains unseen during
training. Furthermore, we show these results were achievable through training
our models on as few as 200 example sentences. The final system is a proof of
concept that the generation of evidence tables can be semi-automated,
representing a step towards fully automating systematic reviews.
