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The rapid growth in published clinical trials makes it difficult to maintainup-to-date systematic reviews, which requires finding all relevant trials. Thisleads to policy and practice decisions based on out-of-date, incomplete, andbiased subsets of available clinical evidence. Extracting and then normalisingPopulation, Intervention, Comparator, and Outcome (PICO) information fromclinical trial articles may be an effective way to automatically assign trialsto systematic reviews and avoid searching and screening - the two mosttime-consuming systematic review processes. We propose and test a novelapproach to PICO span detection. The major difference between our proposedmethod and previous approaches comes from detecting spans without needingannotated span data and using only crowdsourced sentence-level annotations.Experiments on two datasets show that PICO span detection results achieve muchhigher results for recall when compared to fully supervised methods with PICOsentence detection at least as good as human annotations. By removing thereliance on expert annotations for span detection, this work could be used inhuman-machine pipeline for turning low-quality crowdsourced, and sentence-levelPICO annotations into structured information that can be used to quickly assigntrials to relevant systematic reviews.

Sent2Span: Span Detection for PICO Extraction in the Biomedical Text without Span Annotations