Learning for Biomedical Information Extraction: Methodological Review of Recent Advances

TL;DR

This paper reviews recent advances in learning-based biomedical information extraction, emphasizing deep learning and open information extraction, and discusses future directions for the field.

Contribution

It provides a systematic summary of recent methodological developments in BioIE, focusing on deep learning and open information extraction techniques.

Findings

Deep learning has significantly advanced BioIE methods.

Open information extraction is emerging as an influential technique.

The review identifies future research directions in BioIE.

Abstract

Biomedical information extraction (BioIE) is important to many applications, including clinical decision support, integrative biology, and pharmacovigilance, and therefore it has been an active research. Unlike existing reviews covering a holistic view on BioIE, this review focuses on mainly recent advances in learning based approaches, by systematically summarizing them into different aspects of methodological development. In addition, we dive into open information extraction and deep learning, two emerging and influential techniques and envision next generation of BioIE.