  The rapid growth in published clinical trials makes it difficult to maintain
up-to-date systematic reviews, which requires finding all relevant trials. This
leads to policy and practice decisions based on out-of-date, incomplete, and
biased subsets of available clinical evidence. Extracting and then normalising
Population, Intervention, Comparator, and Outcome (PICO) information from
clinical trial articles may be an effective way to automatically assign trials
to systematic reviews and avoid searching and screening - the two most
time-consuming systematic review processes. We propose and test a novel
approach to PICO span detection. The major difference between our proposed
method and previous approaches comes from detecting spans without needing
annotated span data and using only crowdsourced sentence-level annotations.
Experiments on two datasets show that PICO span detection results achieve much
higher results for recall when compared to fully supervised methods with PICO
sentence detection at least as good as human annotations. By removing the
reliance on expert annotations for span detection, this work could be used in
human-machine pipeline for turning low-quality crowdsourced, and sentence-level
PICO annotations into structured information that can be used to quickly assign
trials to relevant systematic reviews.
