Objectives: Identifying all published reports of randomized controlled trials (RCTs) is an important aim, but it requires extensive manual effort to separate RCTs from non-RCTs, even using current machine learning (ML) approaches. We aimed to make this process more efficient via a hybrid approach using both crowdsourcing and ML.
Methods: We trained a classifier to discriminate between citations that describe RCTs and those that do not. We then adopted a simple strategy of automatically excluding citations deemed very unlikely to be RCTs by the classifier and deferring to crowdworkers otherwise.
Results: Combining ML and crowdsourcing provides a highly sensitive RCT identification strategy (our estimates suggest 95%-99% recall) with substantially less effort (we observed a reduction of around 60%-80%) than relying on manual screening alone.
Conclusions: Hybrid crowd-ML strategies warrant further exploration for biomedical curation/annotation tasks.
Keywords: crowdsourcing; evidence-based medicine; human computation; machine learning; natural language processing.
© The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association.