Learning for Biomedical Information Extraction: Methodological Review of Recent Advances
arXiv:1606.07993
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.
arXiv commentThe version under review of Briefings in Bioinformatics(submitted on 06/16/2016)
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Cited by in corpus (6)
- A Relation Extraction Approach for Clinical Decision Support
- Unsupervised patient representations from clinical notes with interpretable classification decisions
- A Biomedical Information Extraction Primer for NLP Researchers
- Semi-Automating Knowledge Base Construction for Cancer Genetics
- A Concept-Centered Hypertext Approach to Case-Based Retrieval
- Unsupervised Identification of Study Descriptors in Toxicology Research: An Experimental Study