A Rich Feature-based Kernel Approach for Drug- Drug Interaction Extraction

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 90
  • Downloads 0

How To Cite

ANASS RAIHANI, NABIL LAACHFOUBI (2017). A Rich Feature-based Kernel Approach for Drug- Drug Interaction Extraction. International Journal of Advanced Computer Science & Applications, 8(4), 324-330. https://europub.co.uk./articles/-A-258359