A Rich Feature-based Kernel Approach for Drug- Drug Interaction Extraction
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2017, Vol 8, Issue 4
Abstract
Discovering drug-drug interactions (DDIs) is a crucial issue for both patient safety and health care cost control. Developing text mining techniques for identifying DDIs has attracted a great deal of attention in the last few years. Unfortunately, state-of-the-art results didn't exceed the threshold of 0.7 F1 score, which calls for more efforts. In this work, we propose a new feature-based kernel method to extract and classify DDIs. Our approach consists of two steps: identifying DDIs and assigning one of four different DDI types to the predicted drug pairs. We demonstrate that by using new groups of features non-linear kernels can achieve the best performance. When evaluated on the DDIExtraction 2013 challenge corpus, our system achieved an F1-score of 71.79%, as compared to 69.75% and 68.4% reported by the top two state-of-the-art systems.
Authors and Affiliations
ANASS RAIHANI, NABIL LAACHFOUBI
Keywords
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- EP ID EP258359
- DOI 10.14569/IJACSA.2017.080445
- Views 97
- Downloads 0
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