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Bioinformatics. 2014 Dec 1;30(23):3365-71. doi: 10.1093/bioinformatics/btu557. Epub 2014 Aug 20.

A novel feature-based approach to extract drug-drug interactions from biomedical text.

Author information

1
Department of Medical Informatics, Erasmus University Medical Center Rotterdam, Informatics Institute, University of Amsterdam, The Netherlands, Complexity Institute, Nanyang Technological University, Singapore and ITMO University, St. Petersburg, Russian Federation.
2
Department of Medical Informatics, Erasmus University Medical Center Rotterdam, Informatics Institute, University of Amsterdam, The Netherlands, Complexity Institute, Nanyang Technological University, Singapore and ITMO University, St. Petersburg, Russian Federation Department of Medical Informatics, Erasmus University Medical Center Rotterdam, Informatics Institute, University of Amsterdam, The Netherlands, Complexity Institute, Nanyang Technological University, Singapore and ITMO University, St. Petersburg, Russian Federation Department of Medical Informatics, Erasmus University Medical Center Rotterdam, Informatics Institute, University of Amsterdam, The Netherlands, Complexity Institute, Nanyang Technological University, Singapore and ITMO University, St. Petersburg, Russian Federation.

Abstract

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.

AVAILABILITY AND IMPLEMENTATION:

The source code is available for academic use at http://www.biosemantics.org/uploads/DDI.zip.

PMID:
25143286
DOI:
10.1093/bioinformatics/btu557
[Indexed for MEDLINE]

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