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DOI: 10.14569/IJACSA.2017.080445
PDF

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

Author 1: ANASS RAIHANI
Author 2: NABIL LAACHFOUBI

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 4, 2017.

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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.

Keywords: Drug–drug interaction; Feature-based approach; Nonlinear kernel; Biomedical informatics; Natural Language Processing

ANASS RAIHANI and NABIL LAACHFOUBI, “A Rich Feature-based Kernel Approach for Drug- Drug Interaction Extraction” International Journal of Advanced Computer Science and Applications(IJACSA), 8(4), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080445

@article{RAIHANI2017,
title = {A Rich Feature-based Kernel Approach for Drug- Drug Interaction Extraction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080445},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080445},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {4},
author = {ANASS RAIHANI and NABIL LAACHFOUBI}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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