In this paper, we investigate a new approach to Population, Intervention and
Outcome (PIO) element detection, a common task in Evidence Based Medicine
(EBM). The purpose of this study is two-fold: to build a training dataset for
PIO element detection with minimum redundancy and ambiguity and to investigate
possible options in utilizing state of the art embedding methods for the task
of PIO element detection. For the former purpose, we build a new and improved
dataset by investigating the shortcomings of previously released datasets. For
the latter purpose, we leverage the state of the art text embedding,
Bidirectional Encoder Representations from Transformers (BERT), and build a
multi-label classifier. We show that choosing a domain specific pre-trained
embedding further optimizes the performance of the classifier. Furthermore, we
show that the model could be enhanced by using ensemble methods and boosting
techniques provided that features are adequately chosen.

本研究探究了一种新的人口、干预和结果（PIO）元素检测方法，特别关注证据医学（EBM）中的这一任务。本研究目的是建立一个训练数据集，以便最大限度地减少冗余和歧义，并探讨利用最先进的嵌入方法进行 PIO 元素检测的可能选项。我们利用最先进的文本嵌入 BERT 建立了一个多标签分类器，这进一步优化了分类器的性能，同时表明模型可以通过使用集成方法和提升技术来提高。