# Enhancing PIO Element Detection in Medical Text Using Contextualized Embedding

## TL;DR

This paper improves PIO element detection in medical texts by creating a refined dataset and leveraging domain-specific BERT embeddings, ensemble methods, and boosting to enhance classifier performance.

## Contribution

It introduces a new, less ambiguous dataset for PIO detection and demonstrates the effectiveness of domain-specific BERT embeddings combined with ensemble techniques.

## Key findings

- Domain-specific BERT embeddings improve detection accuracy.
- Ensemble and boosting methods further enhance classifier performance.
- Refined dataset reduces redundancy and ambiguity in PIO detection.

## Abstract

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.