  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.
