- A novel feature-based approach to extract drug-drug interactions from biomedical text
- Volume | Issue number
- 30 | 23
- Number of pages
- Document type
- Faculty of Science (FNWI)
- Informatics Institute (IVI)
Motivation: Knowledge of drug-drug interactions (DDIs) is crucial for healthcare professionals in order to avoid adverse effects when co-administering drugs to patients. Since 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.
Availability: The source code is available for academic use at http://www.biosemantics.org/uploads/DDI.zip
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