Identifying reports of randomized controlled trials (RCTs) via a hybrid machine learning and crowdsourcing approach

Abstract

Abstract 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.