2014
A novel feature-based approach to extract drug-drug interactions from biomedical text
Publication
Publication
Bioinformatics , Volume 30 - Issue 23 p. 3365- 3371
Motivation: Knowledge of drug-drug interactions (DDIs) is crucial for health-care professionals to avoid adverse effects when co-administering drugs to patients. As most newly discovered DDIs are made available through scientific publications, automatic DDI extraction is highly relevant. Results: We propose a novel feature-based approach to extract DDIs from text. Our approach consists of three steps. First, we apply text preprocessing to convert input sentences from a given dataset into structured representations. Second, we map each candidate DDI pair from that dataset into a suitable syntactic structure. Based on that, a novel set of features is used to generate feature vectors for these candidate DDI pairs. Third, the obtained feature vectors are used to train a support vector machine (SVM) classifier. When evaluated on two DDI extraction challenge test datasets from 2011 and 2013, our system achieves F-scores of 71.1% and 83.5%, respectively, outperforming any state-of-the-art DDI extraction system.
Additional Metadata | |
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doi.org/10.1093/bioinformatics/btu557, hdl.handle.net/1765/83802 | |
Bioinformatics | |
Organisation | Department of Medical Informatics |
Bui, Q. C., Sloot, P., Van Mulligen, E. M., & Kors, J. (2014). A novel feature-based approach to extract drug-drug interactions from biomedical text. Bioinformatics, 30(23), 3365–3371. doi:10.1093/bioinformatics/btu557 |