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 This leads to policy and practice decisions based on out-of-date, incomplete, and biased subsets of available clinical evidence.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.

本文提出并测试了一种新的 PICO 区间检测方法，该方法不需要标注的区间数据，仅使用众包句子级注释来实现。该方法有助于将低质量众包和句子级 PICO 注释转化为可用于快速分配临床试验的结构化信息，并提高临床试验的回收率，减少了系统性回顾的时间。

在医学文本中提取 PICO 时不借助 Span 注解的 Span 检测方法